^»îiîiL*^ United States fiqUkl

Ifl Department of ^ ^

Agriculture

Combined Forest Pest

Research and

Development Program

Agriculture

Handbook No. 523

Gypsy Moth

Handbook

X

Gypsy Moth

Egg-Mass

Sampling With

Fixed- and

VariableRadius Plots
Contents

In 1974 the U.S. Department of

Agriculture initiated the Combined

Forest Pest Research and Development Program, an interagency effort

that concentrated on the Douglas-fir

tussock moth in the West, on the

southern pine beetle in the South, and

on the gypsy moth in the Northeast

The work reported in this publication

was funded in whole or in part by the

program. This manual is one in a

series on the gypsy moth.

Introduction 3

Survey Specifications 4

The Sampling Plan 7

Carrying Out the Survey 12

Compilation of the Data 20

Appendixes 23

A—Recording Instructions

and Codes 23

B—A Data Compilation

Example 28

C—Background Information 40
Gypsy Moth Egg-Mass

lëiampling With Fixed- and

Variable-Radius Plots

by

Robert W. Wilson, Jr/

and Gerald A. Fontaine^

Introduction

To obtain reliable information about

gypsy moth infestations in forested

areas, a survey must be conducted.

The resulting information can be used

either to guide management decisions

or to assess the end results of decisions.

The method introduced here—sampling with fixed- and variable-radius

plots, or FVP—is not intended for use

in forested areas smaller than 10 acres

or at egg-mass densities below 100 egg

masses per acre, both of which require

high sampling rates with attendant

high costs. For small areas, a method

based on small fixed-radius plots may

be more efficient. Atlow population

levels, a method based on pheromone

trapping of adult male gypsy moths

may be better if suitable techniques

can be developed.

The topics discussed in this handbook

include: specifications of requirements

the user must make prior to a survey,

how to arrive at them, and why they

are important; description ofthe

survey methods; organization and

execution ofthe survey; and compilation of the data. The appendixes

present details of various procedures.

' Research forester, Forest Insect & Disease

Laboratory, Forest Service, Hamden, Conn.

2 Forester, Forest Service, Northeastern Forest

Experiment Station, Upper Darby, Pa.
Survey Specifications

Objectives of the Survey

It is necessary to obtain information

on all facets of the situation relevant to

management decisionmaking and to

avoid the collection ofsuperfluous

information. It is clear that both egg

masses per acre above and below

snowline and stand basal area by gypsy

moth food classes are relevant

information; other information may

be obtained as needed. The following

objectives should be adopted for the

survey:

1. Estimate stand basal area per acre

of live overstory trees, by gypsy moth

food classes.

2. Estimate current egg masses per

acre on live overstory trees above and

below 1 foot from the ground.

3. Estimate current egg masses per

acre not on live overstory trees above

and below 1 foot from the ground.
Populations to be Sampled Data to be Collected

Designation of the target population to

be sampled demands careful attention

if a survey is to produce reliable

information. Valid inferences cannot

be made about the target population if

the sampled populations are not truly

representative.

Ideally, the target population in gypsy

moth surveys is all the egg masses

within some well-defined forested

area. There are two key problems in

sampling these populations.

The first problem is that the boundaries of infestations are indefinite at

any given time, tending to shift and

coalesce over time. The second problem is the inaccessibility of parts of the

populations, which takes two forms.

The obvious one is remoteness from

easy travel routes and trails. This can

readily be overcome, at a cost, simply

by insisting that the survey be designed

so that each part ofthe population has

a known chance of being sampled. The

other form is that of egg masses that

are hidden from the observer or are so

high in trees that the sampler cannot be

sure whether they are current or old.

The effects ofthis form of the problem

can be minimized, also at a cost, by

painstaking work in the collection of

the data, as will be discussed later.

There is a tendency in all surveys to

tack on data collection requirements

that are irrelevant to the purposes of

the survey and that may never be subsequently utilized. It is important to

verify that all the data to be collected

are relevant and that no essential data

are omitted. For the survey objectives

previously stated, the only relevant

data are the species (or food class) and

the diameter at breast height (to the

nearest inch) of sample trees, and the

counts of egg masses on the sample

trees and fixed-radius plots. The additional requirement that egg-mass

counts be kept separate by heightabove-ground categories is included so

that future survey techniques may be

made more efficient or at least of

greater diagnostic value. Marginal

notes about unusual conditions

encountered can also be helpful.
Degree of Precision Required

Deciding upon the desired degree of

precision is a troublesome aspect of

any sarapling procedure. It involves

balancing the value of the information

in the sample against the cost of

obtaining it. In forestry, this can rarely

be done on other than subjective

grounds. While there may sometimes

exist a quantitative measure of costs,

there is hardly ever such a measure of

derived values.

You may simplify the decisions by

noting that the coefficients of variation

on specdes composition are several

times smaller than those for egg masses

per acre. If a sensible precision for the

latter can be established, you can reasonably assume that the precision in

the other estimate will be adequate.

What about the precision in egg

masses per acre? It is believed that a

standaj-d error of estimate of about 20

percent of the mean is a reasonable

balanœ between the value of the

information and its cost. Of course,

meeting this standard becomes increasingly expensive as the mean density

decreaijes, but presumably the value of

the information increases correspondingly. The expense becomes prohibitive, however, at low egg-mass

densities, and perhaps the suggested

standard should be relaxed when eggmass densities are expected to be in the

low hundreds. In any case, the final

choice of degree of precision is the

responsibility of the user.
The Sampling Plan

The Frame

The sampling frame for the tree

population is an infinite set ofsampling points within the boundaries that

limit the target population. Each point

is a potential center for a sampling

unit. A sampling unit consists of a

variable-radius (prism) plot by which

trees are selected for the sample with

probability proportional to tree basal

area (prism an¿e= 147.27 minutes,

prism strength= 4.29 diopters, basal

area factor=20) and a fixed 1/200-

acre plot radius (radius = 8 Va feet) for

sampling other materials. As far as we

know, the original use of point sampling in egg-mass sampling was in 1973

in New York. ^

This sampling plan was selected from

among several investigated because:

1. Use of small sampling units requiring only a limited attention span

encourages careful and accurate

counts of egg masses by field crews.

This is of overwhelming importance to

the reliability of the survey.

2. Although the variation among

small sampling units is greater than

among large ones, the cost per unit is

also proportionately less. This allows

larger sample sizes drawn from sampling distributions to tend toward

normality, even with strongly aggregated underlying egg-mass distributions, and at the same time maintains a

given level of precision.

3. It tends to concentrate sampling

effort on the largest components of the

egg-mass populations. In tests of the

procedure in central Pennslyvania,

about 85 percent of total egg masses

per acre were on live overstory trees,

and the numbers in which they were

found on trees varied roughly with tree

size (for example, the cross-sectional

area of a tree).

4. It is simple and straightforward in

field operations and only slightly less

so in the compilation ofresults. Point

sampling is also compatible with

defoliation surveys.

^ New York State Department of Environmental

Conservation. 1973. N.Y. For. Pest Rpt. 73

(23-24).
Selection of the Sample

The sample selection process is crucial

to the success ofthe survey. Although

a wide v£iriety of plans exists by which

information may be collected, the one

used should be simple to apply in the

field, meet survey specifications at

reasonable cost, and, above all, yield

unbiased estimates of the parameters

or items to be estimated.

A simple probability sampling

scheme meets these criteria. It is a plan

ofsystematic sampling with a single

random start. A uniformly spaced grid

of points is laid down at random over

a map of the tract to be sampled. Each

point is the center for 1) a wedge prism

point for selecting the live overstory

sample trees and for 2) a fixed-radius

ground plot to sample the egg-mass

population on other materials.

To determine the spacing of the points

in the grid it is necessary to know the

area in acres ofthe tract and the sample size required (see following). The

number of acres per sample point can

then be computed. For example, if the

area in the survey unit is 100 acres, and

the number ofsample points required

is 50, then there are 100 ^ 50=2 acres

per sampling point. This result (2 x

43,560) is converted to 87,120 ft' per

sampling point. For a square grid, the

distance between lines and between

points on a line is the square root of

the per point square footage=

^877120 = 295 ft.

Sometimes, especially for large survey

units, it may be preferable to treat the

area per point as a rectangle rather

than a square, subject to the constraint

that the distance between lines should

not exceed three times the distance

between points on a line. For example,

suppose you found it more convenient

to space the points on a line at 200 ft

(this will also save almost one-third on

travel time between points). Then the

spacing between lines would be

87,120^ 200 = 436 ft, which is a solution that is within the constraint.

One final point The distance between

points should never be less than 100 ft.

If closer, the same sample trees may be

selected from two adjacent sampling

points, thus weakening the sample.
TABLE 1.—Population standard deviation and

standard error of estimate for samples ofsize 30, at

various numbers of egg masses per acre

Population

Standard error of estimate

Egg masses Egg masses Percent

per acre standard deviation per acre of means

25 39 7 29

50 69 13 25

75 96 18 23

100 122 22 22

150 170 31 21

200 216 39 20

300 301 55 18

400 381 70 17

500 457 83 17

600 531 97 16

800 672 123 15

1,000 808 147 15

1,200 938 171 14

1,400 1,064 194 14

1,800 1,308 239 13

2,000 1,426 260 13

2,500 1,713 313 13

3,000 1,989 363 12

3,500 2,258 412 12

4,000 2,519 460 11
Sample Size

There are two approaches to setting

sample size: One is to follow our

previous suggestion of a sampling

error of about 20 percent of mean egg

masses î)er acre; the other is to fine

tune the sample size for the specific

requirements of a given application.

For the first approach, simply set the

sample size at 30 sampling points. This

will give close to a 20-percent sampling

error at the more critical egg mass

densities, and will ease the requirement

a little at lower densities and tighten it

at higher densities. Columns 3 and 4 of

table 1 show the confidence limits in

egg masses per acre and percent of

mean density, respectively, within

which the actual mean will fall with

chances of two in three.

To fine tune the sample size to the

survey requirements, you must have

specified a sampling error (SE). This is

the limit within which the actual

population mean is desired to fall,

given the sample mean. In addition,

one may also set the confidence that

the desired outcome will occur. Confidence is expressed as the chances of a

desired outcome. Chances of two in

three (t == 1) were previously suggested, but for critical surveys you

might want to increase the chances to

95 in 100 (t= 2) or even 99 in 100

(t= 3). As will be seen, these specifications have a substantial effect on

sample size.

Also needed is an estimate of the

standard deviation among sampling

points in the population to be sampled.

This can be obtained from an estimate

of the mean egg masses per acre in the

population, which may be obtained

from experience or a quick preliminary

survey.

In order to estimate the relationship

between standard deviation (SD) and

egg masses per acre (EM), twenty-one

35-acre tracts in central Pennsylvania

were surveyed in 2 successive years

with the following results:

SD = 2.7875(EM)°«^^^

or

log SD = 0.44522 + 0.82066(log EM)

The latter equation accounted for 92

percent of the variation in log SD.

Some calculated SD's are shown in

table 1, column 2.

10
Now sample size (n, the number of

sampling points) can be computed

from:

»=-(i)'

when

t = 1 for a confidence of two

chances in three

t = 2 for chances of 95 in 100

t = 3 for chances of 99 in 100.

For example, suppose a preliminary

survey has come up with 900 egg

masses per acre and you have decided

that you want a standard error of ±300

egg masses with chances 95 in 100.

Then

SD = 2.7875(900)««^^^

- 740.70

and

"-(^)

= 25

The field costs ofthe survey will vary

with the size of the tract, size of the

trees, egg-mass density, and roughness

of the terrain and undergrov^h. Costs

thus far have not been under operational conditions, but the estimate is

roughly 1 man-hour per point under

usual conditions, a generous allowance

for careful work.

11
Carrying Out the Survey

Location of Sample Points

When the sampling plan is completed,

including mapped sample-point locations, the survey may be carried out.

This section deals with location of the

sample points, crew organization,

order of work, and timing of surveys.

Before launching into these matters,

you need to think about planning the

field work on a day-to-day basis. The

importance of prior planning, to assure that the survey is well founded to

accomplish the objectives successfully,

has been stressed. No less important

are the careful planning and execution

of the field work. If survey results are

to be meaningful, the field data must

be accurate. If the costs are to be

reasonable, the work must be done

efficiently but with care. Planning each

day's work in advance, maintaining

and replacing equipment, and editing

of field forms before leaving a field

point ai'e some of the ways by which

good results can be obtained.

One final matter of importance is the

effect of weather conditions on eggmass visibility. The weather class

should be recorded on the field forms

(appendix A), and if at all possible, the

survey should be conducted only when

the weather class is excellent or fair.

Prior to the start of the survey, the

supervisor must convert the set of

sampling points on the survey unit

map to instructions for locating the

points on the ground. To do this, select

a nearby point such as a road or trail

intersection that is easily identified

both on the ground and on the map.

From this point, plot a simple traverse

to the starting point of the survey, then

determine from the map the bearing or

azimuth of the lines of sampling points

and plot the offsets from one line to

another (fig. 1).

In the field, the supervisor must

emphasize careful compass work and

horizontal tape measurement. Ideally,

the lines will be run with a staff compass, a two-chain tape with topographic trailer, and a topographic Abney

level. Such techniques are particularly

appropriate in rough, hilly country

where the tract boundaries are not

marked on the ground. In relatively

level country, horizontal chaining by

breaking chain when necessary without

using the level and trailer is acceptable.

Horizontal chaining may also be done

where visibility is poor and short line

sights must be used.

12
Crew Organization

SYSTEMATIC SAMPLING

SCHEME

Finishing Point

9 16 JL3

o o 0

ö ci

Boundary

andö4 Direction

(D (3W

Cruise Lines

Figure 1.—Schematic of samplingpoint layout showing order of travel

to points. Also indicated is the pattern

of search for egg masses at the points.

A crew of three persons provides the

best continuity and speed in the work.

A two-person crew can be used if

necessary but is awkward because of

the multiplicity ofjobs.

The jobs, especially the egg-mass

counting, tend to be tedious and

should be rotated frequently among

crew members. Rotating job responsibilities at the completion of a working

unit, or every 4 hours, is sufficient to

reduce errors caused by monotony or

job fatigue. All crew members, therefore, must be well trained and proficient in all the jobs. At any given time,

the tally man should check the other

crew members for oversights and gross

mistakes.

If more than one crew is available for a

survey, the crews should be assigned

adjacent survey lines and should leapfrog each other in completing the

survey. This will distribute individual

crew biases evenly over the tract and

will facilitate the work of the supervisor. It will also permit the detection

and estimation of any serious crew

bias.

13
Timing of the Field Work

We have found that the survey can be

conducted equally well before or after

leaf fall. The survey season can be

extended from roughly the first of

August (after moth flight) throughout

the winter, depending upon snow and

weather conditions. The bottleneck

imposed by waiting until the leaves are

down to start field work can be

eliminated.

Estimates of egg masses per acre on

trees, based on counts made on sample

trees in August and September, were

compared with estimates based on

counts made at the same sampling

points in November. The study was

done in central Pennsylvania on eight

separate 35-acre survey units, each of

which contained 40 sampling points.

The within-unit correlations of fall and

summer adjusted counts were high.

ranging from0.82 to 0.95 (table2).

Evidently the fall and summer counts

were, in general, consistent with one

another. However, in half the units,

the fall/summer differences in

estimated egg masses per acre were

large and significantly different from

zero, while half were small and not

significantly different from zero. The

large differences ranged from 22 to 34

percent of the fall egg-mass estimates,

but the small differences were only 3 to

7 percent of the fall means.

No patterns were found in the data

that might explain the large differences. Certainly the greater visibility in

the fall and the loss of egg-mass color

in the fall are compensating factors.

Most likely, however, the differences

are due to counting errors on both

occasions and simply point up the need

for careful field work. Careless countTABLE 2.—Comparison of fall and summer

estimates oftotal egg masses per acre on overstory

trees ^

Fan Summer Difference

Survey egg masses egg masses Egg masses

unit per acre coefficient per acre per acre

1 1,325 0.95 1,639 -314

2 300 .82 322 -22

3 1,322 .94 1,394 -72

4 155 .89 102 53

5 806 .89 620 186

6 701 .93 667 34

7 458 .92 473 -15

13 605 .88 472 133

Number of points per survey is 40.

Significantly different from zero with P<.05.

14
ing can easily overpower counting

errors due to season.

In half the survey units, the fall

estimates of egg masses per acre were

the larger ones; in the other half, the

sunmier estimates were larger. The

average difference over all eight tracts

was just two egg masses per acre—in

favor of the sunmier estimates; hence

the conclusion that surveys can begin

as soon as egg-mass deposition is

complete.

A survey with foliage present is more

time consuming than one in the

absence of foliage. The extra time is

required for use of binoculars because

of a reduction in bole and crown visibility, and to insure careful viewing for

a maximum quality egg-mass count.

Standard deviation

of the difference

Percent

in egg masses

per acre t

-24 939 ^.115

-7 264 .528

-5 618 .737

34 149 ^.253

23 467 ^.517

5 330 .650

3 303 .313

22 275 ^3.053

15
Fixed-Radius Plots to Sample

Nonoverstory Egg Masses

The fixed-radius plot is used to survey

the egg masses oœurring in strata

other than the live overstory trees. The

best proœdure is first to establish the

fixed-radius plot and then to make the

egg-mass counts before the plot is

disturbed.

For uniformity, always start the eggmass count in line with the direction of

travel. The tally person stands over the

center point and the person doing the

count goes out to the plot perimeter, a

distance of 8V^3 ft. Use a tape measure

or measuring stick (fig. 2) for accuracy

and be sure the center point is not

allowed to move. The 8 Y3 -foot measure is the radius of a 1/200-acre plot,

which is the largest plot size that is

both convenient and conducive to

accurate, adequately precise counts.

Traveling in a clockwise direction, the

person making the egg-mass counts

uses the tape or measuring stick to

mark the territory covered. The area is

examined closely during the frequent

trips back and forth from the

perimeter to the center point.

All objects and materials within the

plot are examined for egg masses

exceptthe live overstory trees exposed

to direct skylight (dominant, codominant, and intermediate trees). Count

all egg masses on dead and understory

trees with stump centers in the plot.

For all other objects, only egg masses

within the plot boundary are included

in the count.

In counting nontree egg masses, experience is helpful in learning where the

egg masses are likely to be found.

Upper surfaces of brush, sticks, logs,

and rotten moist materials are the least

likely areas; more favorable locations

include the crotches of understory trees

and brush, and the undersides of logs,

branches, and rocks.

At very low densities, the egg masses

tend to be deposited on the ground

cover. The ground cover, therefore,

becomes proportionally more important with a decrease in density. With

moderate to high densities, the stems

of brush are more favorable depositories. The undersides of rocks and

branches are used in all population

densities.

16
2B i

Figure2, A and B.—Using a measuring sück to lay out 8 '/s-foot radius of

fixed ground plot.

17
Prism Points to Sample Overstory

Tree Population

After completing the data collection

for the fixed-radius plot, the collection

of data for the prism point can begin.

Theprism pointis the centerpoint of

thefixed-radiusplot Utilizing the

procedures in appendix C, the sample

trees are now selected. As with the

fixed-radius plot, start the selection in

line with the direction oftravel and

rotate in a clockwise direction to locate

the sample trees, being sure always to

keep the wedgeprism over the center

pointing. 3).

After a candidate tree is selected for

the sample, check the tree condition. If

the tree is not alive, or is not receiving

direct skylight on its crown (that is,

in the open-grown, dominant, codominant, or intermediate crown class),

reject the tree as a sample and go on to

the next one.

If the trœ is acceptable as a sample,

carefully check the prism sighting and

be sure to measure the distance to the

tree from the center point, if it is a

borderline tree. Then, if the tree is

**in," record the tree species (or food

class) and d.b.h. to the nearest inch

(see appendix A for codes). Finally,

proceed with the egg-mass census of

the tree.

/^"^ Borderline >7n^

IíT^--<V y^''""^

ct^V.

Out\\©

SAME OBSERVATIONS

PROPERLY MADE

3A

Observation

Points Pa, Pb, Pc,

Off True Center (Ctr)

Borderline^

FAILURE TO MAKE

OBSERVATIONS FROM

DIRECTLY OVER POINT

3B

Figure 3.—Prism sighting technique:

A, Proper technique; B, improper

technique.

For summer counts (foliage present),

binoculars are a necessity. With fall

counts (after leaves have fallen), the

naked eye can be used for portions of

the bole. At moderate to high eggmass densities, it is best to tally

the bole, then tally the limbs and

branches. Atlow densities, it is possible to tally the tree totally, keeping the

crown and bole counts separate, then

noting the final counts (by height class)

on the talJly sheet.

1Í
With small trees (4 inches d.b.h. and

smaller), counting on two faces is

usually sufficient. Larger trees can be

surveyed by using three faces. Be

careful not to overlap the faces and

cause an overcoverage (overcount). If

only two faces are tallied, an undercoverage (undercount) error is made.

For very large trees, it may be

necessary to count more than three

faces for the total coverage (fig. 4).

For a census of egg masses deposited

on the crown, it is best to divide the

crown by branch clumps, if such

clumping occurs on the tree. If clumping does not occur, then divide the tree

into faces. As with the bole, caution

must be exercised in order to avoid

either undercoverage or overcoverage.

Because of the presence of foliage

during a summer census, dividing the

tree into more faces may be desirable

to keep any one working area a more

manageable size. The purpose is to

count the live overstory tree egg masses

as accurately as possible. Too many

faces cause overcoverage, whereas too

few faces cause undercoverage.

BOLE OBSERVATION

<~"^ Inadequate

Coverage

KS^'Total

^Coverage

\/ \

Overlap^^^^

The number of

observation points

affects amount of 1

bole covered. /,W\

Figure 4.—Schematic illustrating in

vertical projection how the tree bole

should be laid off in faces for counting egg masses.

Once the data for the prism point have

been collected, remove the center pin

and continue to the next sample point.

The proceduresjust described in this

section are repeated for all sample

points in the survey.

19
CompUation of the Data

Basal Area Estimates

Now you must calculate the estimated

means and standard errors for the

variables of interest—that is, basal

area per acre and total egg masses per

aae, from the sample data. An example of the »calculations using a small

illustrative sample is given in appendix

B.

In the fonnulas that follow, the sample

data are symbolized in the following

mannen

Xi = egg mass count in the ith 1/200-acre

fixed plot

yij = egg mass count on theyth sample tree

at the fth prism point

dij = d.b.h. of the/th sample tree at the rth

prism point in inches

ki = number of sample trees at the äh prism

point

n= number of 1/200-acre fixed plots and

prism pointts

n and

S = sum of the n terms which follow this

i=i symbol

Total basal area per acre in the

overstory is estimated simply as:

Basal area per acre=20 | ._ •

n

The standard error of estimated basal

area per acre is:

^ ' sy^-li^

n

n(n-l)

To estimate the basal area by food

classes, just substitute the number of

trees in a food class at each point for

Iq; repeat the calculation for each food

class.

20
Fixed 1/200-Acre Plot Estimates of Prism Point Estimates of Egg Masses

Egg Masses per Acre per Acre

Mean number of egg masses per acre

(X):

Weighted egg-mass count at a point

(Yi):

i=200(i!i'''

Variance of the mean number of egg

masses per acre (W^):

v.=2ooY.^,^'"-"^n

n(n-l)

n(n-l)

.=S m

Mean number of egg masses per acre

(y):

Syi

y=3667l i=,

Variance of the mean number of egg

masses per acre (Vy):

Vv = 3667Mi=i

S (yi.-y)'

n(n-l) ^

= 3667

21
Estimates of Total Egg Masses per

Acre

Mean totisd egg masses per acre (EM):

EM=x+y

Variance of the mean total number of

egg masses per acre (V^^ y):

Vj^y = (Vx + Vy + 2COV-x-y)

where the covariance term is

computed as:

S

COV-xy = (200) (3667) I i-i

s x,n-(!")(]/') ^

n(n-l)

Standard error ofthe estimate (SE^i+y):

22
Appendixes

A—Recording Instructions and Codes

Sample Field Form for the Data at a

Sampling Point

Region Study Unit Line Point Mo./Day/Yr.

Card

No.

Trees Crew

EM Visibility New Egg Masses |

Excel Fair Poor Prec Wet 0-1 1-3 3-6 6-»-

Tree Spec DBH

New Egg Masses |

0-1 1-3 3-6 6 + Crwn

01

02

03

04

05

06

07

08

09

10

NOTES:

23
Weather Classes

Weather variables that affect the

visibility of pupae and egg masses

should b<î noted.

ExceDenlr-Sky clear with little or no

haze or overcasting clouds. Sunlight

diffuse (** noonday" conditions) with

little or no shadows or direct blinding

sunlight. Contrast high(treetops dark

with blue sky background).

Fair—Sky mostly clear with clouds

present (cloud cover between one-third

and two-thirds). Sunlight more directional and may cause shadows and/or

direct raj^s. (Generally, the sun is approximately at a 45° angle.) Contrast

medium (treetops dark with cloudy sky

background).

Poor—Sky is overcast (cloud cover

more than two-thirds) or a heavy haze

present. Sun is below a 45° angle, causing heav]»^ shadows and/or blinding

directional sunlight. Contrast low

(treetops dark with gray overcast sky

backgroimd).

Precipitaitíon—Rain, snow, sleet, etc.

Wet bark—With or without active

precipitation.

* To be included in the sample, a tree of given

d.b.h. must have its center at breast height within

the indicated horizontal limiting distance from

the sampling point.

Limiting Distance for Basal Area

Factor 20 by Diameter at Breast

Heiglif

Diameter at Breast

Height, in Inches •" .1 .2

2 3.89 4.08 4.28

3 5.83 6.03 6.22

4 7.78 7.97 8.17

5 9.72 9.92 10.11

6 11.67 11.86 12.06

7 13.61 13.81 14.00

8 15.56 15.75 15.95

9 17.50 17.70 17.89

10 19.45 19.64 19.83

11 21.39 21.58 21.78

12 23.33 23.53 23.72

13 25.28 25.47 25.67

14 27.22 27.42 27.61

15 29.17 29.36 29.56

16 31.U 31.31 31.50

17 33.06 33.25 33.45

18 35.00 35.20 35.39

19 36.95 37.14 37.33

20 38.89 39.08 39.28

21 40.83 41.03 41.22

22 42.78 42.97 43.17

23 44.72 44.92 45.11

24 46.67 46.86 47.06

25 48.61 48.81 49.00

26 50.56 50.75 50.95

27 52.50 52.70 52.89

28 54.45 54.64 54.84

29 56.39 56.59 56.78

30 58.34 58.53 58.72

31 60.28 60.47 60.67

32 62.22 62.42 62.61

33 64.17 64.36 64.56

34 66.11 66.31 66.50

35 68.06 68.25 68.45

36 70.00 70.20 70.39

37 71.95 72.14 72.34

38 73.89 74.09 74.28

39 75.84 76.03 76.22

40 77.78 77.98 78.17

24
.3 .4 .5 .6 .7 .8 .9

Limiting Distance in Feet== l.S»445x db-ltin inches

4.47 4.67 4.86 5.06 5.25 5.44 5.64

6.42 6.61 6.81 7.00 7.19 7.39 7.58

8.36 8.56 8.75 8.94 9.14 9.33 9.53

10.31 10.50 10.69 10.89 11.08 11.28 11.47

12.25 12.44 12.64 12.83 13.03 13.22 13.42

14.19 14.39 14.58 14.78 14.97 15.17 15.36

16.14 16.33 16.53 16.72 16.92 17.11 17.31

18.08 18.28 18.47 18.67 18.86 19.06 19.25

20.03 20.22 20.42 20.61 20.81 21.00 21.20

21.97 22.17 22.36 22.56 22.75 22.95 23.14

23.92 24.11 24.31 24.50 24.70 24.89 25.08

25.86 26.06 26.25 26.45 26.64 26.83 27.03

27.81 28.00 28.20 28.39 28.58 28.78 28.97

29.75 29.95 30.14 30.33 30.53 30.72 30.92

31.70 31.89 32.08 32.28 32.47 32.67 32.86

33.64 33.83 34.03 34.22 34.42 34.61 34.81

35.58 35.78 35.97 36.17 36.36 36.56 36.75

37.53 37.72 37.92 38.11 38.31 38.50 38.70

39.47 39.67 39.86 40.06 40.25 40.45 40.64

41.42 41.61 41.81 42.00 42.20 42.39 42.58

43.36 43.56 43.75 43.95 44.14 44.33 44.53

45.31 45.50 45.70 45.89 46.08 46.28 46.47

47.25 47.45 47.64 47.83 48.03 48.22 48.42

49.20 49.39 49.59 49.78 49.97 50.17 50.36

51.14 51.34 51.53 51.72 51:92 52.11 52.31

53.09 53.28 53.47 53.67 53.86 54.06 54.25

55.03 55.22 55.42 55.61 55.81 56.00 56.20

56.97 57.17 57.36 57.56 57.75 57.95 58.14

58.92 59.11 59.31 59.50 59.70 59.89 60.09

60.86 61.06 61.25 61.45 61.64 61.84 62.03

1 62.81 63.00 63.20 63.39 63.59 63.78 63.97

64.75 64.95 65.14 65.34 65.53 65.72 65.92

66.70 66.89 67.09 67.28 67.47 67.67 67.86

68.64 68.84 69.03 69.22 69.42 69.61 69.81

70.59 70.78 70.97 71.17 71.36 71.56 71.75

72.53 72.72 72.92 73.11 73.31 73.50 73.70

74.47 74.67 74.86 75.06 75.25 75.45 75.64

76.42 76.61 76.81 77.00 77.20 77.39 77.59

78.36 78.56 78.75 78.95 79.14 79.34 79.53

25
Breakdown of Encountered Tree Species

into Five Food-Preference Classes^

I. MOST PREFERRED

A. Class 1

802 Quercus alba (white oak)

832 Quercusprinus(chestnut oak)

B. Class II

806 Quercus coccínea (scarlet oak)

816 Quercus ilicifolia (bear oak)

833 Q. rubra{noTÚitm red oak)

837 Q. ve/w^/ra (black oak)

C. Class III

350 Alnusspp. (alder)

375 Betulapapyrifera var, papyrifera (paper birch)

379 Betulapopulifolia (gray birch)

660 Malusspp. (apple)

743 Populus grandidentata (bigtooth aspen)

746 Populus tremuloides(quaking aspen)

951 Tilia americana (American basswood)

II. INTERMEDIATE

Class IV

316 Acerrubrum var. rubrum(red maple)

318 Acersaccharum (sugar maple)

331 Aesculus glabra (Ohio buckeye)

999 A melanchier canadensis(amelanchier)

391 Carpinus caroliniana (American bombean)

402 Carya cordiformis(bitternut hickory)

403 Carya glabra (pignut hickory)

407 C. ovo/úr(shagbark hickory)

409 C. /omeA2i05ûf(mockernut hickory)

421 Castanea dentata (American chestnut)

462 Celtis occidentalis(hackberry)

* Food class designation from: Houston, D. R.,

and H. T. Valentine. 1977. Comparing and

predicting forest stand susceptibility to gypsy

moth. Can. J. For. Res. Species codes and names

from Northeastern Forest Survey Field Manual.

U.S. Dept. Agrie, For. Serv., Northeastern For.

Exp. Stn.

26
Class IV (cont'd)

491 Cornusflorida (flowering dogwood)

521 Diospyros virginiana (common persimmon)

531 Fagas grandifolia (American beech)

999 Hamamelis virginiana (witch hazel)

601 Juglans cinérea (butternut)

602 Juglans nigra (black walnut)

651 Magnolia acuminata (cucumber tree)

693 Nyssa sylvatica var. sylvatica (black túpelo)

701 Ostrya virginiana (hophombeam)

123 Pinuspungens(table mountain pine)

125 Pinus resinosa (red pine)

126 P. n^/(iûr (pitch pine)

129 P. Strobus( eastern white pine)

132 P. v/>^/Az/úrA2úf (Virginia pine)

762 Prunusserótina (black cherry)

824 Quercus marilandica (blackjack oak)

835 Quercusstellata var. stellata (post oak)

931 Sassafras albidum (sassafras)

261 Tsuga canadensis(eastern hemlock)

972 Ulmis americana (American elm)

975 Ulmis rubra (slippery elm)

III. LEAST PREFERRED

Class V

541 Fraxinus americana (white ash)

068 Juniperus virginiana (eastern redcedar)

621 Liriodendron tulipifera (yellow-poplar)

097 Picea rubens(red spruce)

130 Pinussylvestris(scotch pine)

901 Robiniapseudoacacia (black locust)

999 Rhusspp. (sumac)

27
B—A Data Compilation Example

This example is intended to help the

serious r^îader through the basic calculations nœessary for the compilation

of the egg-mass survey data.

A sample survey of six sampling points

is the basis for the exercise and is

presented on field forms that are also

used as preliminary worksheets for the

summary' of the sampling point data.

With the field forms as a guide, individual estimates are made: basal area

per acre of overstory trees and its

standard error; the egg masses per acre

for overstory trees and its variance; the

egg masses per acre on other materials

and its variance; and finally, the total

egg masses per acre and its standard error. Similar calculations using only egg

masses in the 0-1 foot height class will

lead to estimates of egg masses per acre

for that portion of the population. The

first step is to summarize the egg-mass

data on each tally sheet to obtain the

point values, yj (weighted egg masses

on overstory trees) and Xj (egg masses

on other materials).

The Xj is simply the sum of the egg

masses tallied on the l/2(X)-acre plot

by height above ground. For example,

on the tally sheet for point one,

x, = 0-hO-hO-hO = 0

The yj is a little more complicated:

yi= s

j=i

Where k^ is the number of trees at a

point, yj is the sum of egg masses on a

tree (as for x¡ above) and dij is the

d.b.h. of the tree. For the first point in

the example we have:

yi = — + —= 0.0200

10^ 12^

28
Region Study Unit Line Point Mo./Day/Yr.

¿6 / /

/Ö/7/76

Card

No.

Trees Crew

EMVisibiüty New Egg Masses |

Excel Fair Poor Prec Wet 0-1 1-3 3-6 6 +

Z / / 0 0 0 0 r^rO

Tree Spec DBH

New Egg Masses

0-1 1-3 3-6 6-1- Crwn

01 SOi> 10 0 / 0 0 /

02 m tí. 0 0 0 0 0

03

04

05

06

07

08

09

10

Pi 4

3.

0

OMOO

0

NOTES: y,,^o.ozoo

29
Region Study Unit Line Poin» Mo./Day/Yr.

2G / JL /o/7/7e

Card

No.

Trees Crew

EM Visibility New Egg Masses |

Excel Fair Poor Prec Wet 0-1 1-3 3-6 6-H

S / / 0 0 0 0 x»-0

Tree Spec DBH

New Egg Masses

0-1 1-3 3-6 6-1- Cn/vn

01 3/6 B 0 0 0 0 0

02 doi 9 1 0 Z 1 0

03 S32 II 0 0 0 0 0

04 Soi a 0 0 /

/ 9

05 m 9 0 1 0 / 0

06

07

08

09

10

pi

0 0

0

il

0

o,on^

NOTES: h.^O.i^OS

30
Region Study Unit Line Point Mo./Day/Yr.

âh / v5 iu¡7/7(,

Card

No.

Trees Crew

EM Visibility New Egg Masses |

Excel Fair Poor Prec Wet 0-1 1-3 3-6 6-h

H / / 0 0 0 0 Xt^O

Tree Spec DBH

New Egg Masses |

0-1 1-3 3-6 6 + Crwn

01 ÎOJL 7 0 0 0 0 0

02 eob 7 0 0 0 0 0

03 8oz S 1 0 1 0 <z

04 S03. 13 0 0 0 1 6

05

06

07

08

09

10

y>j

/'J ^Ä

0 0

0 0

^ O.lbOO

7 o.om

NOTES: is^O.ZOl^

31
Region Study Unit Line Point Mo./Day/Yr.

^e / ^ /0/7/76

Card

No.

Trees Crew

EM Visibility New Egg Masses ]

Excel Fair Poor Prec Wet 0-1 1-3 3-6 6 +

s- / t/ 0 0 0 0 x«»0 '4f

Tree Spec

CIHJ

DBH

New Egg Masses

0-1 1-3 3-6 6 + Crwn

01 SOb /5 0 0 0 0 7

02 m 7 0 0 0 0 0

03 806 8 1 0 0 0 0

04 802 8 0 0 0 0 1

05 903L 9 0 0 1 0 3

06

07

08

09

10

NOTES:

/¥J

iMi

<

1 o.om

0 0

/ O.Ofsib

/ 0.0/5é?

H- O.0H94

)^-=-0.1^X0

32
Region Study Unit Line Point Mo./Day/Yr.

o267 / S f0/7l7i,

Card

No.

Trees Crew

EM Visibility New Egg Masses ]

Excel Fair Poor Prec Wet 0-1 1-3 3-6 6 +

3 / /

/ 0 0 0

Tree Spec

dsi

DBH

New Egg Masses

0-1 1-3 3-6 6 + Crwn

01 3/6 7 0 0 0 0 0

02 801 6 0 0 1 0 0

03 803L /3 0 0 0 0 3i

04

05

06

07

08

09

10

,__j

1*i

0

I

3L

0

aOJL78

0.01/8

Xs I

NOTES: y* -= 0.03^6

33
Region Study Unit Line Point Mo./Day/Yr.

a.e / 6 I0¡7¡7(,

Card

No.

Trees Crew

EM Visibility New Egg Masses]

Excel Fair Poor Prec Wet 0-1 1-3 3-6 6 +

6 / / 0 0 0 0 x¿^0

Tree Spec DBH

New Egg Masses |

0-1 1-3 3-6 6 + Cn«^n

01 80X // 0 0 0 0 7

02 833 /3 0 1 0 0 0

03 Sû2 13. 0 0 0 1 5

04 601 11 0 0 0 0 0

05 SoZ 10 0 0 0 0 0

06 SO^ /Ä 0 0 0 0 /

07

08

09

10

y»j /o <^h

7 aoS79

/ O.0OS9

b o.omi

0 0

0 0

1 O.OOùf

NOTES: h.^0.H24

34
Egg Masses per Acre on Overstory Trees

(Points)

The next step is to produce the estimate of the egg masses per acre on overstory

trees (points). The estimate is:

y=3667 llA

when, using the yi summarized on each tally sheet,

S yi. = 0.0200 + 0.1505 + 0.2014 + 0.1220 + 0.0396 -1-0.1124

i=l

= 0.6459

SO that

- ^^1 / 0.6459 \ ^^

y=3661 ( ) = 395 egg masses per acre.

The variance of the estimate (to be used in calculating the standard error of the

total) is: / ,^

/ " Í Sy,)

Vy=3667M Syi.^-\i^i_/

n(n-l)

when

S yi' = 0.0200' -h 0.1505' -h 0.2014' + 0.1220' + 0.0396' -^ 0.1124'

i=l

= 0.0927

and

n

S yi. = 0.6459 as above

i=i

so that

/o.0927- MË§2'\

6(6-1)

= 10,385.

35
Egg Masses per Acre on CMher Materials (Plots)

Now we can carry out the analogous operations to estimate egg masses per acre

on other materials (plots). The estimate is:

x=20o(i?i^'I

where, using the Xi summarized on the tally sheet

n

Sxi=0+0 + 0 + 0-}-l + 0=l

i=l

so that

X=(200)-7 = 33 egg masses per acre

The variiance ofthe estimate (which is also used in calculating the standard error

of the total) is

n(n-l)

where

n

Sxi = (P + (P + (P + (F+P + (P=l

and

n

S Xi = 1 as above

¡=)

so that

\6(6-iy

= 1111.

36
Total Egg Masses per Acre (Points and

Plots)

The estimate of total egg masses per acre is simply the sum of two previous

estimates:

TOT=x+y

= 33 + 395 = 428 egg masses per acre

The standard error of the.estimate is

SEx-, y = ±\/V-x + Vy+ 2COV3^

where V^^ and Vy have been computed above and

SxiViCOVxy=(200X3667) I i=i

n(n-l)

where

Sxiyi =0x0.200 + 0x0.1505 + 0x0.2014

'=1 +0x0.1220+1x0.0396 + 0x0.1124 = 0.0396

so that

(1X0.6459)\

0.0396 COV-xy = (200X3667) I ^'^^^ "" 6

\ 6(6-1)

= -1,664

then

SE5+y = \/1,111 +10,385 + 2 x(-l,664)

= ±90 egg masses per acre.

37
Egg Masses per Acre for Subtotals

If estimates are required for subtotals (say egg masses per acre less than 1 ft from

the ground), it is necessary only to go back to the tally sheet, define x¡ and y^ in

those terais, and then redo all the above calculations. The results:

y=45 x=33

Vy=503 COVxy = —301 V5j = 1,111

TOT=78 SE3^^y = ±32

38
Basal Area per Acre in Overstory Trees

The basal area per acre in overstory trees depends only on the numbers ofsuch

trees, kj, tallied at each sampling point

BA=20 (b)

where

n

i=l

so that

BA=20^—^83.

The standard error of the basal area estimate is

SE„, = 20 //sV -(sk.

n

n(n-l)

where

S k¡2 = 2^ +5^ +4^+5^ + 3^ + 6^= 115

i=i

and

n

S kj = 25 as above

1=1

so that

115 ^^'

SEBA=±20 "

6(6-1)

= ±12.

For estimates of basal area by species or gypsy moth food class, simply go back

to the tally sheets, redefine the k¡ accordingly, and repeat the calculations for

each class. For food classes, the results from the example are:

Food dass

Species

included Basal area

Standard error

of basal area

1 802,832 57 14

2 806,833 20 7

3 - 0 0

4 316 7 4

5 - 0 0

39
C—Background Information

Training Exercise in Use of Wedge Prism

A short training exercise in use of the

wedge prism helps to eliminate human

error during an actual survey.

(1) Carefully measure the d.b.h. of a

tree to the nearest tenth of an inch,

using a diameter tape. Take the reading at a right angle to the line of sight.

Refer to figure 5 for the correct

procedure.

(2) From Appendix A, use the table

** Horizontal Limiting Distance in Feet

For Trees of a Given Diameter' ' to

determine the "critical distance" (the

maximum distance for a given diameter tree for which the tree is considered

"in" or is tallied). Determine the critical distance from the table. Critical

distance is measured from the center of

the tree at d.b.h. Measure this distance

and mark the point.

Figure 5, A and B.—

Procedures for establishing diameter at breast height

(d.b.h.). From Northeastern

Forest Survey Field Manual.

U.S. Dept. Agrie, For. Serv.,

Northeastern For. Exp. Stn.

DIAMETER BREAST HIGH (DBH)

MEASUREMENT IN DIFFERENT

SITUATIONS:

1. Tree on

slope

2. Tree on

level ground

4.5'

4. Tree forking at or

above 4.5 feet

4.5'

6. Tree deformed

at 4.5 feet

3. leaning tree

5. Tree forking

below 4.5 feet

Dia. Pt.

7. Bottleneck

tree

5A

40
(3) In using a wedge prism, it is critical

to hold the prism over the center point

or, in this exercise, over the critical

distance point. The distance between

the observer's eye and the prism is not

critical; therefore, the observer can

hold the prism wherever it is most

comfortable (e.g., arms length or onehalf arm's length). From the critical

distance point, the tree will appear to

be borderline; thatIs, the offset portion ofthe tree will just touch the tree

(fig. 6). As the observer moves closer

to the tree (from the critical distance point) it becomes more * * in. "

As the observer moves further from

the tree (away from the critical distance point) it becomes more * * out. ' '

Because of the differences in observer

eyesight, it is always best to measure

the critical distance for all borderline

trees.

5B USING THE DIAMETER TAPE

The tape must be

perpendicular to the

lean of the tree.

Don't place tape at an

abnormal place on

the bole.

Right Wrong Right Wrong

41
If the tree leans, the prism can be

rotated so it is parallel to the tree (fig.

7). Ifthe lean is either away or toward

the observer, it is best to measure the

distancefrom thepoint to the center of

the tree at db.h,, then refer to the critical distance table to see ifthe tree is

either "in" or "out. "

The importance of keeping the prism

directly over the point is graphically

illustrated in figure 8.

Figure 6.—Schematic of prism

use in the selection of sample

trees.

Figure 7.—Schematic showing

rotation of prism for selection

of leaning trees.

DONT TALLY

IN

TALLY

BORDERLINE

CHECK

USE OF WEDGE PRISM FOR

PERPENDICULAR LEANING TREES

42
Observation

Points Pa, Pb, Pc,

Off True Center (Ctr)

Borderline ^

Figure 8.—Effect of prism position on selection of sample

trees. Note that when the prism

is circled around the sampling

point, tree B becomes "in" instead of "borderline" and tree

C becomes "borderline" instead of "out."

FAILURE TO MAKE OBSERVATIONS

FROM DIRECTLY OVER POINT

SAME OBSERVATIONS PROPERLY

MADE

43
(4) Repeat step 3 using trees of various

diameter and tree species with rough

and smooth bark. Repeat the procedure where the brush or reproduction

partially obscures the sample tree. If a

tree is completely obscured from sight,

the center point may be moved by

keeping the distance from the tree to

the old center point equal to the

distance between the tree and the new

point (fig. 9).

Figure 9.—Schematic of prism

use when target tree is

obscured, emphasizing that

movement to a clear sight must

be along an arc around the

target tree that passes through

the sampling point.

HIDDEN TREE MEASUREMENT

New Point

Treejs

Tallied

from This

Point

Tree Not

Tallied

from This

Point

due to

Increased

Distance

Old -« Incorrect Line of Travel » Wrong

Point Point

44
Gypsy Moth Egg-Mass Counting

Before making a field count of egg

masses, it is best to acquaint oneself

with the local population, in order to

distinguish old egg masses from new or

current season's egg masses. No one

characteristic can be relied upon for

this distinction.

For egg masses within reach, a basic

test is the "touch test." New egg

masses are generally firm to touch,

whereas old ones are soft. Caution

must be exercised where new egg

masses have few eggs per mass or large

quantities of hair per mass. These two

factors can often cause new egg masses

to feel soft. In cold weather, hands can

become numb, thereby reducing the

effectiveness of the touch test.

When the eggs are squeezed between

the fingernails, if a popping sound is

heard, the eggs are new. However, if

the new eggs are dehydrated or have

been parasitized, this test is less

reliable. If the egg mass is within

reach, scraping the egg mass apart and

watching the eggs fall can give an

indication of age. Old eggs are less

dense and tend to float to the ground,

whereas new eggs tend to descend

more rapidly and make a slight sound

when hitting leaves or other ground

cover.

General appearance can also be an

indicator of age. Old egg masses have

exit (hatch) holes and have an overall

ragged appearance. The older the egg

mass, the greater the weathering and

raggedness. Care is needed where

parasitism of new egg masses has

occurred. Both exit holes of old egg

masses and points of entry of parasites

to the new eggs appear as pinholes in

the egg masses. New egg masses may

also appear ragged because of

predators such as birds or mice.

Color and relative size of the egg

masses are less reliable indicators of

age. Old egg masses are dull in color

and appear bleached out. It is best to

see if the color test holds true for those

egg masses that can be aged by the

previously described methods. If the

color test holds true for low, reachable

egg masses, it can then be safely

utilized to determine age of higher,

unreachable egg masses. As the time

interval between the deposit of egg

masses by the gypsy moth and the eggmass count increases, the reliability of

the color test decreases. Relative size of

the egg masses can be used where the

previous season's population was

stressed. In this case, the new egg

masses will be generally smaller than

the old egg masses.

By reviewing the above characteristics

in the field, particular distinction for

age of a given population can be noted

and utilized in surveying that population. If the survey is extensive or is to

include populations of various ages, be

aware of transition zones in these populations. Another indicator which can

be used is the forest type. Under stress,

the gypsy moth will move into a less

favorable or even an undesirable food

class. A typical movement of this

nature is from oaks to maples and

evergreens.

45
Acknowledgments

This manual has been compiled

through the efforts of many people

and the authors wish to express their

sincere appreciation for the assistance

given by the following individuals. For

field study establishment and data

collection: Joel Bigelow, Robert

Bouthillier, Albert Garlow, David

Hubbaid, Stephen Ivanowsky,

Charles Leonard, Richard Rollinson,

and William Rollinson. For photography and diagraming: Peter Carpentier, Richard Rollinson, and Roger

Zerillo. For editing: Frank Lewis,

Ronald Sloan, and Gerald Walton.

46