United States

Department of

Agriculture A Land Manager’s Guide to Point Counts

of Birds in the Southeast

Forest Service

Paul B. Hamel, Winston Paul Smith,

Daniel J. Twedt, James R. Woehr,

Eddie Morris, Robert B. Hamilton,

and Robert J. Cooper

Southern

Research Station

General Technical

Report SO-l 20
SUMMARY

Current widespread concern for the status of

neotropical migratory birds has sparked interest in

techniques for inventorying and monitoring

populations of these and other birds in southeastern

forest habitats. The present guide gives detailed

instructions for conducting point counts of birds. It

further presents a detailed methodology for the design

and conduct of inventorial and monitoring surveys

based on point counts, including discussion of sample

size determination, distribution of counts among

habitats, cooperation among neighboring land

managers, vegetation sampling, standard data format,

and other topics. Appendices provide additional

information, making this guide a stand-alone text for

managers interested in developing inventories of bird

populations on their lands.

ACKNOWLEDGMENTS

This guide draws on the work of many individuals

and committees in the current Partners in Flight ad hoc

network, particularly C.J. Ralph, G S. Butcher, W.C.

Hunter, the National Monitoring Working Group, the

Southeast Regional Management Working Group, and

the Southeast Regional Monitoring Committee. In

addition, specific studies conducted in the Mississippi

Alluvial Valley are the basis for the quantitative

estimates. This manuscript has been improved after

review by David Buehler, Jaime Collazo, Ray Franzreb,

Glen Gaines, Sid Gauthreaux, Mike Guilfoyle, Wade

Harrison, Bob Hatcher, Chuck Hunter, Cherie Irby,

Fred Kinard, Terrell Rich, Alan Schultz, and David

Wiedenfield. Despite their generous attention, this

manuscript may not necessarily reflect their wisdom;

the authors acknowledge responsibility for the

approaches and opinions expressed here.

September 1996

Southern Research Station

P.O. Box 2680

Asheville, NC 28802
Contents

INTRODUCTION

METHODS PRELIMINARY TO

POINT COUNTS

The Basics

More Detail

WHAT IS A POINT COUNT?

The Basics

More Detail

1 TRANSCRIBING DATA 18

Transcribing Field Data to a Bird List 18

Summarizing Field Data into Reports 19

2

2 HAPPY POINT COUNT CENSUSING! 19

2

WHO CONDUCTS A POINT

COUNT?

The Basics

More Detail

4

4

4

CONDUCTING A POINT COUNT 5

The Basics 5

WHAT IS A POINT COUNT SURVEY? 7

Survey Design 7

Determination of Sample Size 1 1

Forming a Group of Cooperators When

Sample Size Exceeds Property Size 15

MEASURING VEGETATION 16

The Basics 16

More Detail 17

LITERATURE CITED 20

3

3

Appendix A. Equipment list for conducting

4

point count surveys. 21

Appendix B. Codes for wind speed and sky

condition used in conducting point counts. 21

Appendix C. Species codes for birds as found

in the North American Bird Banding

Manual (USDI 1991). 2 2

Appendix D. Proposed structure of computer

files for point count data. 30

Appendix E. Determining sample size by the

power method. 31

Appendix F. Sample vegetation

measurements. 35

Appendix G. List and descriptions of

computer files and suggested coding

standards for Level Three vegetation

variables. List follows a general sequence

from more general to more specific data

items. 38

A Land Manager’s Guide to Point Counts

of Birds in the Southeast

Paul B. Hamel, Winston Paul Smith, Daniel J. Twedt,

James R. Woehr, Eddie Morris, Robert B. Hamilton, and Robert J. Cooper

INTRODUCTION

Concern over declining numbers of neotropical

migratory birds has sparked interest among

managers in inventorying and monitoring these and

other nongame birds (Hunter and others 1993a).

Although such assessments are time consuming and

costly, they are essential in evaluating the status of

these species so that informed management will be

ensured. This guide is provided to assist managers in

the Mississippi Alluvial Valley (MAV) and

throughout the Southeast in accumulating

inventories and monitoring changes in nongame

birds in southeastern habitats.

This guide is consistent with the guidelines of the

monitoring group of the Partners in Flight

Neotropical Migratory Bird Conservation Program

(Ralph and others in press). The standard handbook

for monitoring landbirds, Ralph and others (1993),

will be useful to readers of this guide. Intended to

assist managers in designing, conducting, and

maintaining monitoring, this guide is based on work

conducted in the Mississippi Alluvial Valley since

1985, and reflects what wilI work on the ground. It is

not a complete discussion of the various monitoring

and inventory techniques, and cannot claim to

provide all the answers; accordingly, additional

sources of more technical information are indicated.

Statistical advice beyond that presented in this guide

will be useful to managers as they cooperate and

pool their data.

The Authors

This guide has been written by a group of land

managers and research biologists whose individual

opinions are strong and frequently opposed to each

other. The group has a common focus, however. It

believes that an inventory of nontarget resources,

such as birds, is essential for wise land management.

It is equally convinced that monitoring changes in

the status of such nontarget resources is both an

urgent and an important part of land management

activities. Indeed, for many public land managers,

such monitoring is a part of legal requirements for

maintaining viable populations. The group believes it

is also imperative that managers cooperate with their

neighbors in accumulating inventory information.

Keeping these complex issues in focus, however, is

not easy Strong efforts have been made to maintain a

perspective that is neither of entirely practical land

management nor of purely research interest. The

group sincerely hopes that it has not lost its audience

somewhere between the work center and the ivory

tower. Thus, readers are urged to follow the guide

closely and not avoid the equations.

As a general approach, this guide strives to present a

clear statement of a technique or need in the text; it then

provides a more technical or extended discussion of the

topic in an appendix. For efficiency of use, each topic is

divided into “The Basics” and “More Detail.” “The

Basics” should be enough for the reader to initiate

inventory and monitoring activities. “More Detail” will

be available when needed. Still further detail is

provided in selected appendices. Some topics, such as

the nature of a point count, consist only of “The Basics.”

Paul B. Hamel is a research wildlife biologist at the Southern

Hardwoods Laboratory, U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station, P.0. Box 227,

Stoneville, MS 38776; Winston Paul Smith is a research wildlife

biologist at the U.S. Department of Agriculture, Forest Service,

Pacific Northwest Research Station, Tongass Land Management

Team, 8465 Old Dairy Road, Juneau, AK 99801; Daniel J. Twedt is

station leader at the National Biological Survey, Southern Science

Center, Mississippi Valley Research Field Station, 900 Clay Street,

Vicksburg, MS 39180; James R. Woehr is senior scientist at The

Wildlife Management Institute, 1101 14th St. NW, Suite 801,

Washington, DC 20005; Eddie Morris is wildlife biologist at the

Chattahoochee-Oconee National Forests, 508 Oak St., Gainesville,

GA 30501; Robert B. Hamilton is an associate professor in the

School of Forestry, Wildlife & Fisheries, Louisiana State

University, Baton Rouge, LA 70803; and Robert J. Cooper is an

assistant professor in the Department of Biology, University of

Memphis, Memphis, TN 38152.
METHODS PRELIMINARY

TO POINT COUNTS

The Basics

The most basic form of bird inventory is the list of the

bird species that have been observed on a particular

piece of land. This guide assumes that managers

have such lists of birds for their properties. Almost

spontaneously, those lists will stimulate the curiosity

of managers and of their clients. Questions about the

birds on the list will arise, leading managers to desire

information about variations in inventory among

different kinds of habitats, among different

management treatments of those habitats, among

adjacent properties with different land uses, and

about trends in numbers over time. Answering these

questions requires monitoring the birds on the

property. Managers can refer to a decision tree

developed for counting methods to select an

inventory and monitoring approach (table 1).

More Detail

Point counts constitute one of several methods of

inventorying and monitoring birds. Other techniques

include spot mapping, counts on line transects,

capture-recapture with mist nets, and nest

monitoring. Point counts are most often conducted in

the breeding season, but are suitable for work in

other seasons as well (Gutzwiller 1993). Although the

recommendation of Ralph and others (1993) that

monitoring should involve more than point counting

is undoubtedly correct, only point counts are treated

in this guide. Other methods are explained in Finch

and Stangel (1993), Ralph and others (1993), and

Wunderle (1994).

Table 1 .-Decision tree for matching

information needs with various

approaches to monitoring birds

I. What level of detail is necessary?

1 .

2 .

3 .

Do you want to know about presence/

absence?

NO-Go to 2.

YE!%Simple species list is enough; go

to 7.

Do you want to know about numbers and

trends?

NO-Go to 3.

YES-Point counts, and this guide, are for

you; go to 4.

Do you want to know about population

characteristics, such as survival and

productivity?

NO-Point counts, and this guide, are for

you; go to 4.

YES-Demographic methods, such as nest

searches or constant-effort mist netting,

are for you; see Ralph and others (1993).

II. Which species are of interest?

4 . Do you want to know about all species?

YES-A general method, like point counts,

is for you; go to 7.

NO-Go to 5.

5 . Do you want to know about a particular

group of species?

YES-Go to 6.

NO-A single species? In this case your

needs are beyond those of a general guide,

and you will likely be doing a specific

study with methods tailored directly to

that species alone.

6 . Which species group interests you?

Game species--These are beyond the scope

of this guide; consult a recognized text on the

species of interest.

Waterbirds-These are beyond the scope of

this guide.

LandbirdsRaptors, including hawks and owlsBecause of low population density, most

of these birds are beyond the scope of

this guide.

2
Night birds, including owls, whip-poorwills, and the like- Most of these

require specific nocturnal searches,

although nighttime point counts would

work for many of them.

Other landbirds, such as flycatchers,

woodpeckers, thrushes, warblers, wrens,

etc.-Most species belong to this group

for which point count monitoring works

well; go to 7.

III. What sort of information is needed?

7 . Do you have a list of the birds on your

property?

YE!&Go to 8.

NO-Compile one by getting volunteers

to share observations with you; go to 8.

8 . Is this enough information to support your

monitoring needs?

YES-Continue your existing monitoring

scheme.

NO-Go to 9.

9. Do you know the changes in species

occurrence from year to year?

YE!GGo to 10.

NO-Begin recording an annual birdlist

from checklists supplied by volunteers;

go to 10.

10. Is this enough information to support your

monitoring needs?

YE!%Continue your existing monitoring

scheme.

NO-Go to 11.

11. Do you know changes in relative abundance

in your region from year to year?

YE!&Go to 12.

NO-Participate in the Breeding Bird

Survey; go to 12.

12. Do you want to know trends in relative

abundance across your property in general?

NO-Continue your existing monitoring

scheme.

YES-This guide can help; do an annual

set of roadside counts; go to 13.

13. Do you want to know trends in relative

abundance by habitat for your property?

NO-Continue your existing monitoring

scheme.

YES-This guide can help; do annual set

of habitat-specific, off-road counts;

go to 14.

14. Do you want to know how relative

abundance responds to specific management

actions?

NO-Continue your existing monitoring

scheme.

YES-This guide can help; arrange offroad counts for before-after comparisons;

go to 15.

15. Do you want to know population

(demography) responses rather than how

relative abundance responds to management

treatments?

NO-Stop; continue your existing

monitoring scheme.

YES-Your needs exceed what this guide

can offer; institute demographic

monitoring technique such as nest

searching or constant-effort mist netting;

see Ralph and others (1993).

WHAT IS A POINT COUNT?

The Basics

A point count is a tally of all birds detected visually

or aurally by a single observer from a fixed station

during a specified period (e.g., 5 minutes). Counts

are made in the morning (e.g., before 10:00 a.m.

daylight time [09:00 a.m. standard time]) typically

during the breeding season (usually May and June)

under acceptable weather conditions (e.g., winds less

than 20 km/h [12 mi/h] and no rain). In the tally, the

birds are identified by species and, where desired,

their sex and age are recorded. Their distance from

the observer is listed in four categories: from 0 to 25

m (0 to 82 ft), 25 to 50 m (82 to 164 ft), beyond 50 m in

the habitat, or as flyovers of the habitat, regardless of

distance. The time of first detection is listed in two

categories: within the first 3 minutes of a count

period or during the last 2 minutes. Where time to

travel from point to point exceeds 15 minutes, longer

counts of 10 minutes are warranted (Ralph and

others, in press). When lo-minute counts are

conducted, a third time category is listed for the final

5 minutes. This protocol is almost identical to the

standard proposed by Ralph and others (1993, in

press).
More Detail WHO CONDUCTS A POINT COUNT?

Contrary to the proposed national standard, separate,

additional listing of birds within 25 m (82 ft) is

recommended. This recommendation is more specific

than that of Ralph and others (1993). In the Southeast,

three distance bands are desirable because of the

relatively dense populations of birds in the forests

and the complexity of the vegetation. Recording the

field data in a more detailed manner is useful

because the distance bands within 50 m (164 ft) can

be later collapsed into a single value, whereas birds

recorded in the field as only within 50 m (164 ft) can

never later be separated into those within 25 m (82 ft)

and those between 25 and 50 m (82 and 164 ft).

Because most detections are aural, usually of singing

males, singing behavior must be taken into account

in designing the surveys. Male song is strongly

influenced by breeding status; thus, the timing of

surveys is important. Singing frequencies are usually

greatest when males are establishing territories and

attempting to attract mates; usually, the singing

frequencies decline after the eggs are laid. This

decline is very noticeable in some species. Thus, the

singing frequencies are often greatest in unmated

males. Furthermore, nesting dates vary among

species; consequently, optimal dates for sampling

some species may be inadequate for others.

Generally, there exists a 4- to 5-week period that will

be optimal for simultaneously counting most species.

In the southernmost parts of the Southeast, point

count sampling can start around 1 May In the

northernmost parts, sampling should not start before

21 May because not all migrant species will have

arrived before that date. Consult local experts or

State bird guides for optimal survey times. A useful

rule of thumb, in any case, is to make all counts on

one piece of property during as few calendar days as

possible.

The Basics

Unavailability of suitable personnel is a serious

problem for a manager designing a point count

survey. Identifying birds found on point counts

requires great skill. Most of the birds encountered in

the field, usually more than 90 percent of them, are

heard and not seen. Personnel conducting point

counts must be able to identify the species present by

sight and sound and be able to estimate the distance

to the birds detected. Ideally they will have some

prior experience doing point counts. Finding people

with the proper skills to conduct such counts is not

easy. Local chapters of the Audubon Society and

State ornithological societies are a reliable source of

volunteer counters, many of whom are eager to assist

in other tasks involving birds and land management.

More Detail

Many people find potential workers, both volunteer

and paid, through “The Ornithological Newsletter,” a

bimonthly publication of the Ornithological Societies

of North America (OSNA). The address is “The

Ornithological Newsletter,” c/o American

Ornithologists’ Union, c/o OSNA, 810 E. 10th Street,

Lawrence, KS 66044-8897, U.S.A.

In the long run, it may be more useful for managers,

especially those in charge of larger properties, to

train their own staffs to conduct point counts.

Training sessions can be arranged through several

universities in the Southeast. Approaching schools

that currently offer courses in ornithology is one

method of determining the availability of such

training. It will likely be necessary to use volunteers

for some of the work or the training; each State in the

Southeast has active local birdwatching groups.

Because trained personnel are the most limited

resource in a point count survey, managers will do

well to use the counters to the fullest extent in

conducting as many counts as possible during the

brief morning counting period in the short breeding

season.
CONDUCTING A POINT COUNT

These procedures are suggested as standards for

counts in the Southeast; they are based upon the

standard protocol of Ralph and others (in press) as

modified by the experience of Smith and others

(1993) in the Mississippi Alluvial Valley The

instructions of the Breeding Bird Survey (U. S.

Department of the Interior, Fish & Wildlife Service

n.d.) are useful as well. A checklist of field equipment

is in appendix A.

The Basics

Locate Each Station with Explicit Geographic

Coordinates.-Locate each counting station with the

greatest practical precision and with an explicit

geographic reference, such as Universal Transverse

Mercator (UTM) coordinates or latitude-longitude

using a Geographic Positioning System (GE’S) device,

or determine the location in advance on a U. S.

Geological Survey 7.5-minute topographic map.

Some GPS devices are more difficult to use and less

accurate under a dense forest canopy It will be useful

to locate and permanently mark the counting stations

in advance, including reference distance markers at

25 m (82 ft) and 50 m (164 ft) from the counting point

in the cardinal directions.

Count the %&.-Steps for conducting the actual

point counts in the field are as follows:

1. Approach the location, noting any birds within 50

m (164 ft) of the counting station that are flushed,

fly away, or retreat. Mark these birds in the

appropriate distance band on the bulls-eye data

sheet. Circles on the data sheet (fig. 1) indicate

distances of 25 m (82 ft) and 50 m (164 ft).

2. Orient the bulls-eye data sheet to a fixed

direction, record the wind and sky conditions

(appendix B), date, time, and observer.

3. Position the GPS device and start it recording. Set

the thermometer in the shade.

4. As soon as possible, start the count. Use a pocket

timer or watch to keep track of the time.

5. Record each bird seen or heard with the

appropriate species code (appendix C). Count

family groups of juveniles with a single adult as a

single bird. It may be useful to note the sex and

any other indicator of behavior or breeding status,

and keep track of number of juveniles separately

from number of adults. The standard protocol

does not require this information, but other

workers have found that recording only 0 brownheaded cowbirds (Molothrus ater) provides more

accurate data related to the intensity of nest

parasitism than does the total number of

cowbirds.’

6. Mark birds on the data sheet in the appropriate

distance band and appropriate azimuth; i.e., use

the bulls-eye data sheet as a map of the count

station and a compass to determine the

approximate azimuth. A set of standard coding

symbols is included on the actual bulls-eye data

sheet (fig. 2) to aid in separating individuals.

7. Record data for the different time intervals of the

count in different ways. Some people use a pen

with multiple colors and record data for the first 3

minutes in one color only, using a second color for

minutes 4 and 5, etc. Others choose to underline

those birds observed in the second time interval

and circle those observed in the third. Be sure to

put a legend of the chosen coding scheme on the

bulls-eye data sheet.

8. Holding the sheet in a fixed position, spend part

of the time facing in each of the cardinal

directions in order to better detect birds in each.

9. Mark each bird once, using the mapped location

to judge whether subsequent songs are from new

or already mapped individuals. All flyovers are

recorded outside the second circle, underneath

the word “Flyover”.

10. Record birds observed in the different time

intervals separately Mark the birds encountered

in the final 2 minutes separately, such as by using

a second color, underlining those observations, or

otherwise distinguishing them. Be sure to note the

chosen method of distinguishing these different

time periods on the bulls-eye data sheet. Where

longer times are indicated because travel time

between points is long, count no longer than 10

minutes, and use a third color or other

designation for the 6th to 10th minutes.

11. Do not record any birds believed to have been

counted at previous stations.

* S. Robinson, Illinois Natural History Survey, and F. Thompson,

North Central Research Station, personal communication, 1993.

5
- - ---

ZGFz -_---

----- - - - - -_- ----

Region station Point t v&lit I Year i&zii Day mne

---

--- -- ---

Observer Temp g: W%d S??y H%itat Forest type S-W isite-

-me ---

--- N-S cootd%rtes E-W coord%;ltes

Geographic coding: Zone-0 for lat-lOW, else U’fM Zone

08 C

Flyovers: Remarks

W;NG SYMBOLS

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Figure L-Blank bull’.+eye data sheet.
12. At the end of 10 minutes, stop recording bird

observations. Do not record any new birds seen or

heard after the 10 minutes have passed.

13. Discontinue logging data on the GPS unit. From

the display on the GPS unit, transfer the latitude

and longitude to the bull’s-eye data sheet.

Although these are uncorrected data, they can

serve as the back-up in case the electronic data are

inadvertently lost from the GPS unit before they

can be dumped to the PC for correction.

14. Record the temperature.

15. Mark the location to facilitate a return, at a later

time, for appropriate vegetation measurements.

Field notations from the bull’s-eye data sheets will be

transcribed to bird count data forms and entered into

a computer format (appendix D) compatible with the

Southeastern standard. A sample completed bull’seye data sheet appears in figure 2, figure 3 is a blank

bird count data form, figure 4 is a sample completed

bird count data form for the count in figure 2, and

figure 5 is the sample completed bird count data

form entered into the Southeastern standard

computer format. A blank bulls-eye data sheet and a

blank bird count data form are inserted into the back

of this book.

WHAT IS A POINT COUNT SURVEY?

A point count survey is a group of point counts,

strategically distributed across a property such as a

management area, that are visited in a predetermined

order at a particular time. The tasks involved in

conducting a point count survey or inventory are:

1. Preparation of a survey design, determining where

the counts will be conducted;

2. Identifying personnel;

3. Counting the birds, and assessing vegetation

appropriate to your needs;

4. Transcribing the field data into a standard form,

usually a computer format; and

5. Summarizing the data into reports about the birds.

This section deals with the first stage in the process;

separate sections treat the others.

Survey Design

The Basics.-An inventory or monitoring project is

designed to find out what bird species occur on a

property, where they are, and how their numbers are

changing through time. The single most important

part of the process is to decide upon the objective of

the project. Carefully considering the kind of

information wanted, the level of detail required, and

the frequency of need to evaluate change will aid

greatly in making this decision; the decision tree in

table 1 can help.

Point counts are an efficient way to carry out these

activities. No inventory or monitoring survey can be

done without a map of the property of interest,

preferably marked with different forest stands,

compartments, ecological classification units, or other

areas that the manager wishes to consider as single

management types. Management units such as forest

types, successional stages, and stand condition

classes are the habitat units that monitoring is

designed to sample. Results of the activity will then

be expressed as numbers of birds in the different

management units.

Guidance for monitoring design comes from the

studies of Smith and others (1993), from the

recommendations of Ralph and others (1993), and

from standard statistical considerations of precision.

When a point count survey is set up, points are

located far enough apart to avoid encountering the

same birds; e.g., 250 m (820 ft) apart for off-road

counts and approximately 1 km (0.5 mi) apart for

roadside counts. As a general rule, point count

stations are distributed among habitats in proportion

to the number of stands in the different habitats on

the property. Counts within habitats are distributed

one per stand, wherever practicable. Placing more

than one count in a stand will probably constitute a

waste of resources. Counts intended to sample

particular habitats are located at least 100 m (328 ft)

from the edge of the habitat except when habitat

patches are too small to permit such positioning. In

that case, a counting station should be located at least

50 m (164 ft) from the edge.

Ideally, counts are conducted once a year, at as near

the same time as possible; and at the exact same

points each year. Where possible, the same personnel

conduct the counts each year.
- -

2-3 h ---

Observer Tap =C iii-and Sky tiiitat Forest type S&e

-- -em- - -- --m---w

Zone N-S coordinates E-W coordinates

Geographic coding: Gme-0 for lat-low, d.sa UIN Zoa*

Flyovers: Remarks

Figure 2.-Sample of point count made with bull%-eye data sheet.

8
State Region

----- __ -- - -

Eint I

__-- -- --

Statlon VTsit t Year Month Day TTmii- - -

--- - - --- -----

Observes Temp T

Wxd STy Hxitat Siege - - - -

Forest type Zone

-------

N-S coordmates E-W coordinates

Geographic coding: Zone-O for lat-long, else UTM Zone

Species

Alpha

Code

Count

< 25 m

O-3 min 4-5 min 6-10 min

Flyovers

O-3 min 4-5 min 6-10 min

25-50 m > 50 m

O-3 min 4-5 min 6-10 min O-3 min 4-5 min 6-10 min

I

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Submitter Remarks

Figure 3.-Blank bird count data form.

9
eBd-- 3 1 --

Obsekvsr Tamp 1: Wind S)ry Eabitst Foresttype Sta6e

-- / 6 3f_dh_S2o - 24re_53-0-

zone N-S coordinates E-W coordinates

Geographic codins: Zone=0 for lat-lon&, else UIM Zone

Species

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count

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0;3 min 4-5 min 6-10 min

- - 1

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Figure 4.-Data transcribed from bull’s-eye data sheet to bird count data form.

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Figure S.-Sample point count transcribed into standard ASCII fiat file format.

More DetuiL-Stands are selected at random, either

irrespective of habitat type or by habitat type, so that

each stand has an equal chance of being selected at

the beginning of the process. Counts are distributed

one per stand, wherever practicable. Summaries that

managers make of the counts and analyses that

managers will ask researchers to conduct on their

point count data will be much simpler if this general

guideline is followed. Statistical considerations of

independence of individual counts are also important

in understanding the meaning of results of a point

count survey. Generally speaking, counts at least 250

m (820 ft) apart are independent of each other

because they count different birds. If, however, a

manager wishes to compare habitats but all counts

from one habitat were made on one property or one

stand, whereas those from another habitat were made

from multiple stands, deciding whether differences

have to do with that one stand or with the habitat

will be difficult. Consulting a statistician for help in

designing the study will be useful, particularly if the

policy implications of the work are great or the

differences in bird numbers among various habitats

or years are small.

For practical purposes, only one visit per point count

station will be necessary to compare populations

from one year to the next. This approach will provide

a single estimate of the abundance of birds on the

property at that time. The only time that points

should be revisited within a year will be when

managers wish to examine how birds change during

a single season on their properties. The results of

such work can show how the populations change

during the breeding season.

Determination of Sample Size

The Basics.-No magic formula for determining

number of counts is available. The actual number of

point counts required to answer a particular question

will vary with the question, with the species, with the

variability in numbers of birds in each physiographic

area, with the habitat, as well as with the desired

precision. The two most common sorts of questions

relate to changes in bird populations over time and to

responses of bird populations to management

treatments. Ralph (1993) suggests a minimum total of

250 counts to enable monitoring annual changes in

bird populations on a property by roadside counts.

Smith and others (1993) showed that, for any given

number of counts, visiting a larger number of

counting stations once yielded more information

about the bird community than did visiting a smaller

number of stations more than once. They found that,

in any case, no more than five points per stand were

necessary for adequate sampling of the bird

community. They also found that point counts longer

than 10 minutes are not justified because they are less

efficient at sampling the bird community and

produce more variable results than do counts of 10 or

fewer minutes.

A practical method of sample size estimation for land

managers is presented in this section. A more

detailed and rigorous approach to sample size

determination appears in appendix E. The following

discussion assumes that individual forest stands are

the sampling units’and that each stand is sampled by

the same amount of effort, usually one point count. It

also assumes that a pilot sample of counts has been

conducted, such as the 50 to 100 counts that a

volunteer observer could gather by doing a roadside

11
survey of a property over a single weekend in late

May according to the standard methodology of the

U.S. Department of the Interior, National Biological

Service, Breeding Bird Survey (Butcher and others

1993).

Determining sample size starts with the question of

interest. Land managers may commonly wish to

answer one or both of two basic questions: “Are bird

populations on my property changing over the

years?” or “How did bird populations respond to my

management activity in particular habitats?”

A manager who wants to answer the first of these

questions should allocate the points for bird counts

among the different habitats so that the percentage of

points in each roughly corresponds to the percentage

of the property in that habitat. This strategy enables

the manager to detect trends in population numbers

over several years, as long as the points are visited

each year. A manager seeking to answer the second

question faces a slightly more complicated task in

deciding how many points to allocate and where to

put them.

More Detail: A “Quick-and-Dirty” Method of

Determining Sample S&z-The next issue is, how

many points are needed? The answer to that question

depends upon the specific question the manager

wants to answer. In general, the number of points

needed increases as questions get more specific, as

one attempts to detect smaller population changes, as

the desired confidence level increases, and as the

population of interest gets less abundant or more

variable. This trend accentuates the importance of

deciding in advance exactly what is the objective of

the monitoring.

How these factors affect needed sample size can be

illustrated by using a set of pilot data from

bottomlands in the Mississippi Alluvial Valley (Smith

and others 1993) to demonstrate how the required

sample size is affected by:

1. The nature of the question (from most general to

most specific)

a. Has the total number of birds or species

changed?

b. Has the total number of neotropical migratory

birds changed?

c. Has the total number of birds with high

Concern Scores (Hunter and others 1993a)

changed?

d. Has the total number changed of whatever

species is being counted; e.g., wood thrushes

(Hylocichla mustelina)?

2. The magnitude of population change one desires

to detect

a. 20-percent population change.

b. lo-percent population change.

3. a = The desired confidence level with which a

change is detected

a. a = 0.20, lower confidence.

b. a= 0.10.

c. a = 0.05, higher confidence.

The likelihood that answers of “No” to questions (a)

through (d) in 1 are really “No,” which is the power

of the test (l-b), will be discussed in appendix E.

From their pilot data for these determinations, Smith

and others (1993) present in detail the required

sample sizes for a selection of species in the

Mississippi Alluvial Valley; these are summarized in

appendix E, table E.l.

Managers who want to detect population changes

between years will compare populations in one year

with populations in another year on the same site.

Those who want to determine how birds responded

to a management activity will need to compare

changes in populations on the managed (treatment)

sites to changes in populations on nearby untreated

(control) sites. In either case, the hypothesis to be

tested will be that the average number of birds

recorded per count in one year or habitat type or

management treatment (pi) does not differ from the

average in another year, habitat type, management

treatment, or control area (@:

H, (the null hypothesis): ui = uLz,

and the test will be to compare that hypothesis with

the alternative that the average number of birds per

count in the 2 years, management treatments, or

habitat types differ:

H, (the alternative hypothesis): pi z uz.

The test of this hypothesis is the Student’s t-test. The

estimate of some average count of birds obtained

from sampling will be in the form of the mean * a

confidence interval, indicating that there is a specific

12
confidence level that the value of the mean falls conservative estimate for a = 0.10, and t = 1.33 is a

within the interval: conservative estimate for a = 0.20.

where

n = the number of counting points in a pilot sample

from a particular habitat or year, etc.,

s2 = the sample variance of the variable counted in

the pilot data,

t = Student’s t for a given confidence level,

X = the sample mean of the variable counted in pilot

data.

The basic problem of sampling design is to limit the

size of the confidence interval to the proportional

change (P) in a population which one desires to

detect (Snedecor and Co&ran 1967:516). The

objective is to ensure that

Solving this inequality for n yields

s2t2

?lLp2 x2

where

IZ = the required number of counting points in a

particular habitat or year, etc.

P = the proportional population change one desires

to detect and s2, t, and X are the same as above.

The manager will choose the value of P desired; e.g.,

P = 0.10 to detect a lo-percent population change, P =

0.20 to detect a 20-percent population change, etc.

Since t-values are taken from a standard t-table based

on the number of degrees of freedom (sample size

minus l), and the sample size is unknown, how does

one choose an appropriate t-value? Nearly every

point count sampling for birds will exceed 20 points,

the equivalent of two mornings in the field. Hence, a

conservative estimate of t (one that will ensure a

large enough n) can be obtained by selecting t-values

for 20 degrees of freedom and the desired confidence

level. From appendix E, table E-2, t = 2.1 is a

conservative estimate for a = 0.05, t = 1.73 is a

Now, all that remains is to obtain estimates of X and

s2, the sample mean and sample variance. These

estimates can be obtained from pilot study sampling;

a separate pilot sample will be required for each

habitat. The question now becomes, “How many

points are needed to get adequate estimates of Z and

s2 from pilot data?”

A graphical technique is useful to answer that

question. The counts of all birds combined from the

pilot data of Smith and others (1993) can be used to

illustrate how the sample mean (fig. 6a) and sample

variance (fig. 6b) can be estimated. These graphs are

obtained by calculating and plotting a new sample

mean and sample variance each time the data from

an additional counting station is added to the data

set. Such graphs of how sample means and sample

variances change as additional point counts are

added to the sample set typically show the depicted

pattern. They tend to have large oscillations initially,

and then either approach an asymptote or reach a

condition of steady-state, low-amplitude oscillations

around a fixed value (see figs. 6c, d). In either case,

estimates can be obtained by projecting graphically

to the ordinate axis. Because the formula for

calculating n has s2 in the numerator X and in the

denominator, use of a slightly low estimate for !i and

a slightly high estimate for s2 is recommended. Such

usage will ensure a conservative (probably large

enough) estimate for the sample size needed.

Now that estimates of X and s2 have been obtained

from pilot data, the question could be asked, “How

many points are needed to detect a lo-percent change

in the total number of birds with a = O.lO?” If P = 0.10

is used to represent the proportional population

change in question and if t = 1.73 while s2 is

estimated from the graphs at 25 and X at 16.4, then

s2t2 25(1.73)2

” p2x2 = (0.g2 (16.4)2 - 28

Now suppose a more specific question is asked; i.e.,

How many points are needed to detect a change in

the number of neotropical migratory birds? Figures

13
All Birds Combined Neotropical Migratory Birds

Smple Mean So

2 6

sample Mean

107

5

2 .-

0 !

0 6 10 16 20 26 60 26 40

‘0L

6 1 0 16 20 26 60 56 40

Number of Counts

Sample Varhnce

Number of Counts

(b)

8.

r

Sample variance (d)

1 5 10 15 2 0 2 5 3 0 3 5 4 0 Number of Counts

Number of Counts

Figure 6.-Selected variables as functions of the total number of point counts made in the pilot

data of Smith and others (1993): a, mean number of birds, drawn from all birds

combined; b, variance of number of birds, drawn from all birds combined; c, mean

number of neotropical migrate y birds; d, variance of number of neotropical migrate y

birds.

6c and 6d, which were derived from a subset of the

pilot data consisting of counts of neotropical

migratory birds only, yield estimates of X = 5.8

(fig. 6c) and s2 = 10 (fig. 6d).

If a lo-percent change in the number of neotropical

migratory birds needs to be detected at a = 0.10, the

number of points needed is

22 10(1.73)2

”+& = (0.1)2 (5~)2 = 89

If the same values for the sample means and

variances and the same t- values are used,

substituting P = 0.2 will give the sample size

necessary to detect a 20-percent population difference

in neotropical migratory birds-or n = 23 for cx = 0.10

and n = 33 for a = 0.05.

From the same pilot data, another subset of data

points consisting only of counts of birds of species

with high Concern Scores (2 24) in the MAV (Hunter

and others 1993a) was used to estimate s2 and X

graphically in the same way, producing estimates of

X = 2.6 and s2 = 6. These values yielded a required

sample size of n = 266 points to detect a lo-percent

population difference at a = 0.10 and n = 392 points to

detect a lo-percent population difference at a = 0.05.

Detecting a 20-percent difference in species with high

Concern Scores required sample sizes of n = 67 and n

= 98 points for = 0.10 and 0.05, respectively

14
The final and most specific question asked was “How

many points are needed to detect lo-percent and 20-

percent differences in wood thrush populations at a

levels of 0.10 and 0.05?” The pilot data and graphical

estimates for only wood thrushes yielded estimates

of X = 0.4 and s2 = 0.8. To detect a lo-percent

difference in wood thrush numbers at = 0.10 would

require a sample size of n = 1,497 points. To detect the

same difference at a = 0.05 would require n = 2,205

points. Detecting a 20-percent difference in wood

thrush populations would require n = 375 points for

a = 0.10 and n = 552 points for a = 0.05.

Thus, for the u = 0.10 confidence level, the number of

separate points needed in each group to detect a lopercent population difference between 2 groups of

counts increases from 28 for all birds combined to 89

for neotropical migratory birds alone, to 266 for birds

with high Concern Scores, to 1,497 for a single species

(wood thrush).

In general, increasing the confidence level from a =

0.10 to a = 0.05 will require about 50 percent more

counting points, and it will require four times the

number of counting points to detect a lo-percent

population difference as are needed to detect one of

20 percent. Hence, detecting a Xl-percent population

difference at a = 0.05 will require roughly six times

the number of counting points needed to detect a 20-

percent population difference at a = 0.10. Note

especially that increasing the confidence level from a

= 0.10 to a = 0.05 is far less costly than reducing the

size of the detectable population change from 20 to 10

percent.

Furthermore, because the usual annual or seasonal

variation of bird numbers on particular properties is

not well known, the amount of annual change that a

manager should worry about is not well known

either, As more data accumulate from actual point

count surveys, this issue will clarify itself. Managers

interested in monitoring individual bird species

should note that the more common and abundant

species (relatively high Y) and the more evenly

distributed species (relatively low s2, known as

habitat generalists) will require far fewer counting

points than relatively scarce (low X) species with

clumped distributions (high s2, known as habitat

specialists). Unfortunately, the relatively scarce

habitat specialists are precisely the species most often

in greatest need of monitoring and management.

Forming a Group of Cooperators

When Sample Size Exceeds Property Size

Monitoring the scarce, habitat-specialist species

requires changing the monitoring scale. First, scale

down by conducting point counts at the habitatspecific as well as the propertywide scale. For

example, if point counts are distributed

proportionally across all habitats on a property,

counts of species such as the prothonotary warbler

(Protonoturiu citreu), northern parula (Purulu

americana), and Acadian flycatcher (Empidonslx

virescens) will likely produce a low X and a high s2

(yielding a large n), because these species are

confined to corridors of riparian habitat where

relatively few counting points may have been placed.

If a large number of points are placed entirely within

riparian habitat, counts of these species will produce

a larger X and a smaller s2 (yielding a much lower n)

than counts at the propertywide scale. Thus, habitatspecialist species will probably require a separate

monitoring program focused upon their specific

habitats; this program may be quite different from

one in which the habitats are limited in area.

Next, scale up by forming a network of cooperating

bird monitors on other properties across the

landscape and then pooling the data. This method

can increase sample sizes enough to permit the

detection of population changes among habitatspecialist species on a landscape scale, even when

sample sizes on individual properties may be too

small to detect population changes. The Southeast

Management Working Group of Partners in Flight,

organized along physiographic-area lines, will help

with this task (Hunter and others 1993b) and will

help managers obtain the proper statistical advice as

they cooperate and pool their data. It will be

important to ensure that the habitat definitions used

by the various cooperators in a physiographic area

are clear enough to allow managers to know how to

pool their data.

Because of the large sample sizes needed to detect

population changes in individual species, especially

scarce species, most managers should not expect to

detect small population changes in individual species

on their own properties and may only be able to

detect changes of 30 percent or greater at the = 0.10

level. Moreover, most habitats on an individual

15
property will be too small to contain enough

counting points to detect differences in bird

populations among habitats. Nevertheless, a

propertywide monitoring program is vital to the

detection of such population changes on the larger

landscape scale.

If point counts are distributed proportionally among

habitats on each monitored property, then a

cumulative total of 2,000 point counts taken from at

least 10 properties spread across a physiographic area

should suffice to detect population changes of most

individual bird species within that area. The

contribution of each property to this total is

consistent with the number of point counts, 200 to

250, which can be comfortably completed by one

observer on a single property in a sampling season.

If 10 properties large enough to contain 200 point

counts each cannot be located or monitored within a

physiographic area, then at least 1,000 cumulative

point counts should be taken at the propertywide

scale from as many properties as possible. In this

case, each property manager should also conduct a

separate monitoring program at the habitat-specific

scale by placing an additional 40 point counts

exclusively within those habitats of highest priority

that contain the bird species of highest priority in the

physiographic area. If the habitats of highest priority

on a property are too small to contain 40 counting

points, then a manager should take as many point

counts as possible in those habitats.

Thus, land managers can best contribute to

regionwide bird monitoring by combining a

propertywide monitoring program for all bird

species with a separate habitat-specific program

designed to monitor particular bird species in

habitats of special interest and by sharing their data

with other bird monitors across the landscape.

The pilot data used in this illustration (figs. 6a, b, c,

d) showed that 30 to 40 points were sufficient in the

habitats sampled to yield adequate graphical

estimates for X and 9, regardless of whether the data

graphed were for all birds combined, just neotropical

migrants, just birds with high Concern Scores, or just

the wood thrush. Thus, pilot study data sufficient to

custom-design a monitoring program for an

individual property can be obtained by one person in

just two to three mornings of preliminary sampling.

Using only a hand-held calculator with statistical

functions, the necessary calculations to plot the

graphs and to determine required sample sizes can

be completed in one additional day. As a result, as

few as three observer-days of effort are required to

design a site-specific bird monitoring program

consistent with a land manager’s goals, budgets, and

staffing limitations.

MEASURING VEGETATION

The Basics

Vegetation measurements of some kind are an

essential part of the inventory and monitoring task.

How involved those measurements must be is a

source of concern for those who pay for and carry out

monitoring activities and a source of confusion for

those eager to use the data for research purposes. In

this section an attempt is made to reduce the concern

and the confusion. It is recognized that the effort

available for conducting vegetation sampling at point

count stations will frequently be limited. No attempt

will be made to discourage the conducting of point

count surveys by suggesting vegetation sampling

standards that are unattainable by most land

managers in the Southeast.

Managers should consider this approach to

vegetation measurements as a step-down process

with at least three steps, each of greater detail than

the others; they should select the level of detail

appropriate to their needs. The following hierarchical

scheme is presented as a method whereby a manager

can tailor the sampling intensity to match the

available effort without sacrificing quantitative detail

at the sampling point. In this section it is shown how

different levels of quantitative detail in vegetation

measurements can be incorporated into a single point

count inventory or monitoring program. The levels of

detail may be viewed as nested within each other,

with the detail from each level providing more

information than that available at the higher levels in

the hierarchy, and information at the higher levels

being less specific than that available at the lower

levels.

The highest level in the hierarchy, Level One,

involves associating the point with a particular forest

type and successional stage, or with whatever the

important habitat units are that the manager uses on

a daily basis. This is the most general information

and should be available for any point count. Ralph

16
and others (1993) suggest a qualitative evaluation of

the vegetation within 50 m (164 ft) of the counting

station. Usually this information is available from the

management map of the area; it should be gathered.

Beyond this minimal requirement, the detail of the

measurements will reflect the managers’ objectives

and their desire to compare their findings with those

of other managers.

The second level in the hierarchy, Level Two, is a

vegetation measurement scheme tailored to a

particular landowner’s own land and vegetation

classification. The scheme proposed for the National

Forests in the Southern Region (USDA FS 1995) is

such a vegetation measurement scheme; it is

described in appendix F. From the measurements

taken in that scheme, each point can be assigned to a

broad forest type/successional stage category. In

addition, measurements taken in that scheme provide

additional information pertaining to the variation

within each such category. These qualitative

measurements do not allow detailed analysis of

relationships between individual bird species and

one or more habitat variables. Associating individual

bird species with habitat variables requires

quantitative measurements of those variables.

At the third level in the hierarchy Level Three, are

quantitative measurements intended to characterize

the structure and composition of the vegetation. The

quantitative measurements identified in appendices

F and G are a suitable set to make at each point

counting station, whether on-road or off-road. These

measurements will enable managers to assign each of

their points to a currently accepted Society of

American Foresters’ Forest Type, to a Forest Service

Forest Type, to a Vegetation Type (Hamel 1992), and

probably to a specific cell in other vegetation and

ecological classification schemes as well. When other

considerations preclude conducting measurements in

this detail at each point, conduct them at a random or

stratified random subsample, such as at 10 percent of

the points. In this way, quantitative measurements

will serve several functions. First, they will serve as a

check on the qualitative measurements made at each

point in the monitoring program. Second, they will

be available for use in suggesting hypotheses for

more detailed evaluation. Third, they will be

available for use by others doing more detailed

investigations. Specific detailed study of the habitat

requirements of individual species will require more

detailed approaches than those of Level Three.

More Detail

A suggested standard set of Level Three

measurements is described in appendices F and G, At

this level of detail, the specific objectives of a

particular study, rather than a general measurement

standard, will determine which variables to measure

and how those measurements can be made. In some

circumstances, such as those involving controlled

burning, time at which measurements are taken will

be important; in other circumstances, this will not be

the case.

The parameters to measure are at least these: canopy

closure, canopy height, basal area by species, stem

density by species, shrub density, and groundcover.

In addition, other variables can also be useful:

presence or absence of snags, Spanish moss, vines in

the canopy water, and vegetation profile (or

midstory density). The suggested method is

reminiscent of that of James and Shugart (1970) and

covers an area of approximately 0.04 ha (0.1 acre).

Although James and Shugart (1970) utilize a circular

plot, a square (20- by 20-m [66- by 66-ft]) or

rectangular (16- by 25-m [52.5- by SZ-ft]) plot is much

easier to use under field conditions.

Two people will probably be required to conduct the

vegetation sampling design outlined in appendices F

and G; the following tools will be required: density

board and pole 5 m (16.4 ft) tall, 15-m (50~ft) tape,

ocular tube, clinometer, angle gauge or prism,

diameter tape or biltmore stick, flagging tape, hip

chain and biodegradable string, pencil or pen,

clipboard, and data sheet.

A team of 2 observers (a trained bird counter and a

vegetation sampler) might conduct 10 point counts

and 10 vegetation samples during a single field day

as follows:

Both proceed to the first station, where the bird

counter conducts the first bird count. Upon

completion of that count, the bird counter

proceeds alone to the next and subsequent

stations. When the bird counter has finished the

first count, the vegetation sampler measures

those vegetation parameters that are relatively

easy for a single person to measure; e.g., canopy

closure, groundcover, shrub cover, percentage

cover of vines and Spanish moss, presence or

absence of snags and significant water, species

1 7
composition, basal area, stem density, and

canopy height. The vegetation sampler can then

follow the string of the hip chain through the

vegetation to the second station, make the same

vegetation measurements there, and continue.

When the bird counter finishes the bird counts,

the bird counter then joins the vegetation

sampler to assist in completion of this subset of

measurements at all 10 stations. At this point, it

will probably be lunch time. After lunch the two

members of the field crew can retrace their steps

from the 10th to the 1st station, conducting the

measurements of vegetation profile at each

station as they return to the vehicle.

It should be noted that measurements such as these

are only the beginning of detailed vegetation

sampling that may be required by those conducting

studies of particular habitat requirements of

individual species.

TRANSCRIBING DATA

Transcribing Field Data to a Bird List

The Basics.-Transcribing field data is a tedious, but

necessary, process. Often, errors of transcription are

inadvertently made at this stage. The field observer

should transcribe the data as soon as possible after

leaving the field-and only once, if possible.

Fortunately, some computer software exists to

simplify the process.

If such software is not available, the following

process will suffice, but it does require extra care and

proofreading. Copy the header information from the

bulls-eye data sheet (fig. 2) onto the top of the bird

count data form (figure 3). Establish a routine

method of copying data from the bulls-eye data

sheet to the bird count data form, such as starting in

the center and moving clockwise in the 0- to 25-m (Oto 82-ft) band, then moving clockwise in the 25- to 50-

m (82- to 164-ft) band, and so forth, until each bird

marked on the bull’+eye data sheet has been listed

on the bird count data form. Mark the code name of

each species (from appendix C) in the appropriate

columns. Then mark down each bird in the column

as to the time and distance band where it was first

encountered; e.g., the northern parula, which was

encountered outside 50 m (164 ft) in the first 3

minutes and subsequently flew to within 25 m (82 ft)

sometime in the last 5 minutes, was recorded in the Oto 3-min, >50-m (>164-ft) column. A dot tally works

well for recording numerous birds. The simple

scheme noted at the bottom of figure 3 is an easy way

to tally.

When the information for each bird has been

transferred from the bulls-eye data sheet to the bird

count data form, proofread the transcription by

repeating the process, perhaps putting a small mark

on the bull’+eye data sheet beside each bird as its

correct transcription to the bird count data form is

verified. Figure 4 is a sample filled-out bird count

data form for the sample point count recorded on

figure 2.

Enter the data into a standard computer-readable

format such as the one in appendix D. A completed

sample for the point count of figure 2 is presented in

figure 5. When the data have been entered into a

standard format and verified, they are ready for

submission to a repository and for statistical analysis.

A repository for point count data will likely be

designated in the near future.

More Detail.-The Southern Region of the Forest

Service has developed software for use with data

gathered on national forests; the software is

described in the “Southern National Forest’s

Migratory and Resident Landbird Conservation

Strategy” (USDA FS 1995). The point count data can

be related through this system to other data bases in

the regional Resource Inventory Tracking and

Analysis System.

The U.S. Fish and Wildlife Service, in cooperation

with Louisiana State University,, has sponsored

development of a user-friendly software Bird Count

Analysis Database, GSBBase, tailored to the data

standards presented here (Guddanti 1994). The

system can run on any MS-DOS computer, provides

some error trapping to catch field coding errors, and

minimizes transcription errors. In addition, it

provides some standard reports that will be useful to

a land manager. Users in the National Wildlife

Refuge System and the National Park System in the

Southeast have found this software to be helpful.

Using a software product such as one of these will

simplify the job of transcribing the data. A good

software product to choose for this task will be one

18
that can accept field data directly, check for simple

errors in transcription, and produce output in flexible

format for sharing with other workers. In the future,

field procedures that permit direct entry of data into

hand-held computers, eliminating all transcription

errors, will become available.

Summarizing Field Data into Reports

The Basics.-Reports that express the number of

birds of each species found on a property, are the

desired outcome of the point count survey The

simplest list is one in which the species are listed

alphabetically together with the total number of

counts on which each was found, and the total

number of birds of each species. Such lists can be

tailored to express the number of birds and their

frequency per count on separate habitats or in

separate years (tables 2,3). Note that an important

part of the process of producing bird lists will be to

put the species into the standard checklist order

(American Ornithologists’ Union 1983, table 3).

Ornithologists and birdwatchers routinely list birds

in this sequence.

More Detail.-Data gathered in point count surveys

can be presented in many ways. Convenient

examples include Peterjohn and Sauer (1993), Smith

and others (1993), and various articles in recent issues

of Journal of Wildlife Management, Southern Journal of

Applied Forestry, and other publications.

Table 2.-Portion of sample bird lists resulting

from point count surveys on

hypothetical wildlife management

area (simple alphabetical list)

Species Point Counts* Individuals

___________ Number___-___-__

Acadian flycatcher 14 19

American redstart 3 3

Blue jay 2 2 2 7

Carolina chickadee 2 7 4 4

Great crested flycatcher 7 1 1

Red-eyed vireo 33 61

Red-shouldered hawk 1 1

White-breasted nuthatch 4 6

*Number of counts on which each species was recorded in sample

of 52 counts in 1993.

Table 3.-Portion of sample bird lists resulting

from point count surveys on

hypothetical wildlife management

area (comparison of two years’

surueys, species in checklist sequence)

1993 1994

Species Counts* Birds Counts+ Birds

Number Total Number Total

Red-shouldered hawk 1 1 0 0

Acadian flycatcher 14 19 22 41

Great crested flycatcher 7 11 5 11

Blue jay 22 27 26 29

Carolina chickadee 27 44 38 51

White-breasted nuthatch 4 6 3 3

Red-eyed vireo 33 61 47 55

American redstart 3 3 3 5

‘Number of counts on which each species was recorded in sample

of 52 point counts in 1993. Total of 525 birds of 65 species recorded

in 1993.

‘Number of counts on which each species was recorded in sample

of 65 point counts in 1994. Total of 663 birds of 63 species recorded

in 1994.

HAPPY POINT COUNT CENSUSING!

Monitoring and inventorying with point counts are

the beginning, not the end, of information gathering

by land managers seeking to incorporate nongame

birds into their management of southeastern habitats.

Conducting point count surveys will lead logically to

identification of situations where more powerful

tools, such as nest searching or constant-effort mist

netting, will become necessary in gathering sufficient

information for making proactive management

decisions.

The task of managing birds on one property cannot

be accomplished by the manager of that property

working alone. For example, factors affecting

migratory bird populations extend beyond the limits

of any one property. Joining together with other land

managers to coordinate monitoring and inventorying

on the basis of common.physiographic areas will lead

individual managers to more effective stewardship of

the birds on their own properties. Such stewardship

wilI undoubtedly result in more effective

management of birds throughout the Southeast.

1 9
LITERATURE CITED

American Ornithologists’ Union. 1983. Check-list of North

American birds. 6th ed. Lawrence, KS: American

Ornithologists’ Union. 877 p.

Butcher, Gregory S.; Petejohn, Bruce; Ralph, C. John. 1993.

Overview of national bird population monitoring

programs and databases. In: Finch, Deborah M.; Stangel,

Peter W., eds. Status and management of neotropical

migratory birds: proceedings of workshop; 1992

September 21-25; Estes Park, CO. Gen. Tech. Rep. RM229. Fort Collins, CO: U.S. Department of Agriculture,

Forest Service, Rocky Mountain Forest and Range

Experiment Station: 192204.

Cohen, J. 1977. Statistical power analysis for the behavioral

sciences. New York: Academic Press. 474 p.

Finch, Deborah M.; Stangel, Peter W., eds. 1993. Status and

management of neotropical migratory birds: proceedings

of workshop; 1992 September 21-25; Estes Park, CO.

Gen. Tech. Rep. RM-229. Fort Collins, CO: U.S.

Department of Agriculture, Forest Service, Rocky

Mountain Forest and Range Experiment Station. 422 p.

Freese, Frank. 1967. Elementary statistical methods for

foresters. Agric. Handb. 317. Minneapolis, MN: Burgess

Publishing Co. 87 p.

Guddanti, S. 1994. Bird count analysis database, GSBBase,

version 1.00, users manual. Baton Rouge, LA: GSSoft.

24 p.

Gutzwiller, Kevin J. 1993. Refining the use of point counts

for winter studies of individual species. Wilson Bulletin.

105: 612-627.

Hamel, Paul B. 1992. The land manager’s guide to the

birds of the South. Chapel Hill, NC: The Nature

Conservancy. 437 p.

Hunter, William C.; Carter, Michael F.; Pashley, David N.;

Barker, Keith. 1993a. The Partners in Flight species

prioritization scheme. In: Finch, Deborah M.; Stangel,

Peter W., eds. Status and management of neotropical

migratory birds: proceedings of workshop; 1992

September 21-25; Estes Park, CO. Gen. Tech. Rep. RM229. Fort Collins, CO: U.S. Department of Agriculture,

Forest Service, Rocky Mountain Forest and Range

Experiment Station: 109-119.

Hunter, William C.; Pashley, David N.; Escano, Ronald E.F.

1993b. Neotropical migratory landbird species and their

habitats of special concern within the Southeast Region.

In: Finch, Deborah M.; Stangel, Peter W., eds. Status and

management of neotropical migratory birds: proceedings

of workshop; 1992 September 21-25; Estes Park, CO.

Gen. Tech. Rep. RM-229. Fort Collins, CO: U.S.

Department of Agriculture, Forest Service, Rocky

Mountain Forest and Range Experiment Station:

159-169.

James, Frances C.; Shugart, Herman H. 1970. A

quantitative method of habitat description. Audubon

Field Notes. 24(6): 727-736.

Neter, John; Wasserman, William. 1974. Applied linear

statistical models. Homewood, IL: Richard D. Irwin, Inc.

842 p.

Peterjohn, Bruce G.; Sauer, John R. 1993. North American

breeding bird survey annual summary 1990-1991. Bird

Populations. 1: l-15.

Ralph, C. John. 1993. Designing and implementing a

monitoring program and standards for conducting point

counts. In: Finch, Deborah M.; Stangel, Peter W., eds.

Status and management of neotropical migratory birds:

proceedings of workshop; 1992 September 21-25; Estes

Park, CO. Gen. Tech. Rep. RM-229. Fort Collins, CO: U.S.

Department of Agriculture, Forest Service, Rocky

Mountain Forest and Range Experiment Station:

204-207.

Ralph, C. John; Geupel, Geoffrey R.; Pyle, Peter [and

others]. 1993. Handbook of field methods for monitoring

landbirds. Gen. Tech. Rep. PSW-GTR-144. Albany, CA:

U.S. Department of Agriculture, Forest Service, Pacific

Southwest Research Station. 41 p.

Ralph, C. John; Droege, Sam; Sauer, John R. [and the

members of the Point Count Workshop]. [In press].

Managing and monitoring birds using point counts:

Standards and applications. In: Ralph, C. John; Sauer,

John R.; Droege, Sam, tech. coord. Proceedings of the

symposium on monitoring bird population trends by

point counts; 1991 November 6-7; Beltsville, MD.

Albany, CA: U.S. Department of Agriculture, Forest

Service, Pacific Southwest Research Station.

Smith, Winston I’.; Twedt, Daniel J.; Wiedenfeld, David A.

[and others]. 1993. Point counts of birds in bottomland

hardwood forests of the Mississippi Alluvial Valley:

duration, minimum sample size, and points versus

visits. Res. Pap. SO-274. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern

Forest Experiment Station. 21 p.

Snedecor, George W.; Cochran, William G. 1967. Statistical

methods. 6th. ed. Ames, IA: The Iowa State University

Press. 593 p.

U.S. Department of Agriculture, Forest Service. 1995.

Southern national forest’s migratory and resident

landbird conservation strategy Atlanta, GA: U.S

Department of Agriculture, Forest Service, Southern

Region. 127 p.

U.S. Department of Interior, Fish &Wildlife Service. [nd.].

Instructions for cooperators in the Breeding Bird Survey

Laurel, MD: U.S. Department of Interior, Fish & Wildlife

Service. 4 p.

Wunderle, J. M., Jr. 1994. Metodos para contar aves

terrestres de1 Caribe. Gen. Tech. Rep. SO-100. New

Orleans, LA: U.S. Department of Agriculture, Forest

Service, Southern Forest Experiment Station. 28 p.

20
Appendix A. Equipment list for conducting point count surveys.

EQUIPMENT MATERIALS

Binoculars

Clipboard

Field guides

Gate keys

GPS (Geographic

Positioning System) unit

Magnetic compass

Pocket thermometer

Single-bit hand ax or mallet

Stopwatch or timer

Tape player and bird tapes

Three-color pens

(black, red, blue)

Working vehicle

Administrative map

Aerosol paint

Aluminum caps

AOU bird code list

Bulls-eye data forms

Compartment maps

Computer input forms

(summary sheets)

Insect repellent

2-cm (1 /a-inch) rebar

Topographic maps (quads)

ADDITIONAL MATERIALS

AND EQUIPMENT

FOR VEGETATION SAMPLING

Angle gauge or prism

Clinometer

Density board

Diameter tape or biltmore

stick

Flagging tape

Hip chain and biodegradable

string

Ocular tube

Sturdy pole 5 m (16 ft) tall

25-m (82~ft) tape

Vegetation field data sheet

Appendix B. Codes for wind speed and sky condition used in conducting point counts.

These codes are those used in the Breeding Bird Survey Acceptable conditions for counting birds

include a sky condition of 0, 1, or 2 and wind speeds of less than 20 km/h (12 mi/ h), preferrably less

than 13 km/h (8 mi/h).

SKY CONDITION CODES: WIND SPEED CODES:

Code

Number Sky Condition

Clear or a few

clouds

Partly cloudy

(scattered)

Cloudy (broken)

or overcast

Fog or Smoke

Drizzle

Snow

Showers

Beaufort Indicators of wind speed

scale Wind speed

Number km/h mi/h

0 Less than 2 Less than 1 Smoke rises vertically

1 2 to 5 1 to 3 Wind direction shown

by smoke drift

2 6 to 11 4 to 7 Wind felt on face; leaves rustle

3 12 to 20 8 to 12 Leaves, small twigs in constant

motion; light flag extended

4 21 to 32 13 to 18 Raises dust and loose paper;

small branches are .moved

5 33 to 39 19 to 24 Small trees in leaf crested sway;

wavelets on inland waters

21
Appendix C. Species codes for birds as found in the North American Bird Banding Manual (USDI 1991).

Alpha Alpha Alpha

Code Common Name Code Common Name Code Common Name

ABTO Abert’s Towhee

ACFL Acadian Flycatcher

ACWO Acorn Woodpecker

ADWA Adelaide’s Warbler

ADPE Adelie Penguin

AFPE African Penguin

AKEP Akepa

AKIP Akiapolaau

ALFL Alder Flycatcher

ACGO Aleutian Canada Goose

ALTE Aleutian Tern

ALHU Allen’s Hummingbird

ALOR Altamira Oriole

AMAV American Avocet

AMBI American Bittern

ABDU American Black Duck

AMCO American Coot

AMCR American Crow

AMDI American Dipper

AMGO American Goldfinch

AGWT American Green-winged Teal

AMKE American Kestrel

AMOY American Oystercatcher

AMRE American Redstart

AMRO American Robin

ASTK American Swallow-tailed Kite

ATSP American Tree Sparrow

AWPE American White Pelican

AMWI American Wigeon

AMWO American Woodcock

ANMU Ancient Mm-relet

ANHI Anhinga

ANIA Anianiau

ANHU Anna’s Hummingbird

ANPE Antarctic Petrel

ANTE Antarctic Tern

ACHU Antillean Crested Hummingbird

ANEU Antillean Euphonia

ANMA Antillean Mango

ANN1 Antillean Nighthawk

APAP Apapane

Al’FA Aplomado Falcon

ARLO Arctic Loon

ARTE

ARWA

AHWA

ATFL

ASSP

ATBR

ATPU

AUOR

AUSH

AUWA

AZTH

BACS

BAWA

BAMO

BAHS

BAWO

BAYE

BATE

BASA

BAIS

BAEA

BBOI

BAOR

BANA

BANP

BTGU

BTPI

BANS

BARG

BARS

BRNG

BAOW

BAG0

BBWA

BEG0

BSSP

BEVI

BEKI

BETH

BEHU

BESW

BEWR

BIOO

Arctic Tern

Arctic Warbler

Arrow-headed Warbler

Ash-throated Flycatcher

Ashy Storm-Petrel

Atlantic Brant

Atlantic Puffin

Audubon’s Oriole

Audubon’s Shearwater

Audubon’s Warbler

Aztec Thrush

Bachman’s Sparrow

Bachman’s Warbler

Bahama Mockingbird

Bahama Swallow

Bahama Woodstar

Bahama Yellowthroat

Baikal Teal

Baird’s Sandpiper

Baird’s Sparrow

Bald Eagle

Balt X Bull Oriole Intergrade

Baltimore Oriole

Bananaquit

Band-rumped Storm-Petrel

Band-tailed Gull

Band-tailed Pigeon

Bank Swallow

Bar-tailed Godwit

Barn Swallow

Barnacle Goose

Barred Owl

Barrow’s Goldeneye

Bay-breasted Warbler

Bean Goose

Belding’s Savannah Sparrow

Bell’s Vireo

Belted Kingfisher

Bendire’s Thrasher

Berylline Hummingbird

Bewick’s Swan

Bewick’s Wren

Bishop’s 00

BLBR

BLGU

BLNO

BLOY

BLPH

BLRA

BLRF

BLSC

BLSK

BLSP

BLSW

BLTE

BLTU

BLVU

BAWW

BWAG

BBWO

BBI’L

BBWD

BBCU

BBMA

BBAL

BCCH

BCGN

BCPE

BCVI

BCHU

BCSP

BCOR

BCTI

BCNH

BEBU

BFGR

BFAL

BHGR

BHPA

BLKI

BNTE

BNST

BRWX

BSKI

BTGN

BLAG

Black Brant

Black Guillemot

Black Noddy

Black Oystercatcher

Black Phoebe

Black Rail

Black Rosy-Finch

Black Scoter

Black Skimmer

Black Storm-Petrel

Black Swift

Black Tern

Black Tumstone

Black Vulture

Black-and-white Warbler

Black-backed Wagtail

Black-backed Woodpecker

Black-bellied Plover

Black-bellied Whistling-Duck

Black-billed Cuckoo

Black-billed Magpie

Black-browed Albatross

Black-capped Chickadee

Black-capped Gnatcatcher

Black-capped Petrel

Black-capped Vireo

Black-chinned Hummingbird

Black-chinned Sparrow

Black-cowled Oriole

Black-crested Titmouse

Black-crowned Night-Heron

Black-eared Bushtit

Black-faced Grassquit

Black-footed Albatross

Black-headed Grosbeak

Black-headed Parakeet

Black-legged Kittiwake

Black-naped Tern

Black-necked Stilt

Black-rumped Waxbill

Black-shouldered Kite

Black-tailed Gnatcatcher

Black-tailed Godwit

2 2
Alpha Alpha Alpha

Code Common Name Code Common Name Code Common Name

BTBW Black-throated Blue Warbler

BTYW Black-throated Gray Warbler

BTNW Black-throated Green Warbler

BTSP Black-throated Sparrow

BVOR Black-vented Oriole

BVSH Black-vented Shearwater

BWVI Black-whiskered Vireo

BLBW Blackburnian Warbler

BLPW Blackpoll Warbler

BABI Blck X Atlant Brant Intergrade

BLBU Blue Bunting

BLGO Blue Goose

BLGR Blue Grosbeak

BLJA Blue Jay

BLPE Blue Petrel

BBGR Blue-black Grassquit

BECO Blue-eyed Cormorant

BFBO Blue-footed Booby

BGGN Blue-gray Gnatcatcher

BGNO Blue-gray Noddy

BLUH Blue-throated Hummingbird

BWTE Blue-winged Teal

BWWA Blue-winged Warbler

BLUE Bluethroat

BTGR Boat-tailed Grackle

BOB0 Bobolink

BOWA Bohemian Waxwing

BOGU Bonaparte’s Gull

BOPE Bonin Petrel

BOCH Boreal Chickadee

BOOW Boreal Owl

BOSP Botteri’s Sparrow

BRAM Brambling

BRAC Brandt’s Cormorant

BRBL Brewer’s Blackbird

BRSP Brewer’s Sparrow

BREG Brewster’s Egret

BRWA Brewster’s Warbler

BRQD Bridled Quail-Dove

BRTE Bridled Tern

BRTI Bridled Titmouse

BTCU Bristle-thighed Curlew

BBLH Broad-billed Hummingbird

BBPR Broad-billed Prion

BTLH Broad-tailed Hummingbird

BWHA Broad-winged Hawk

BROC

BRBO

BRCR

BRJA

BRNO

BRPE

BRSH

BRSK

BRTH

BRTO

BCRF

BCFL

BHCO

BHNU

BUFH

BBFL

BBSA

BCNI

BUFF

BUAL

BUSH

BUOR

BUPE

BUHU

BUOW

CACG

CACW

CALC

CAGU

CATH

CAHU

CAGO

CAWA

CANV

CANW

CMWA

CAPE

csss

CARC

CAEL

CAPA

CACH

CARW

CATE

CAAU

CAFI

Bronzed Cowbird

Brown Booby

Brown Creeper

Brown Jay

Brown Noddy

Brown Pelican

Brown Shrike

Brown Skua

Brown Thrasher

Brown Towhee

Brown-capped Rosy-Finch

Brown-crested Flycatcher

Brown-headed Cowbird

Brown-headed Nuthatch

Buff-bellied Hummingbird

Buff-breasted Flycatcher

Buff-breasted Sandpiper

Buff-collared Nightjar

Bufflehead

Buller’s Albatross

Buller’s Shearwater

Bullock’s Oriole

Bulwer’s Petrel

Bumblebee Hummingbird

Burrowing Owl

Cackling Goose

Cactus Wren

California Condor

California Gull

California Thrasher

Calliope Hummingbird

Canada Goose

Canada Warbler

Canvasback

Canyon Wren

Cape May Warbler

Cape Petrel

Cape Sable Seaside Sparrow

Caribbean Coot

Caribbean Elaenia

Caribbean Parakeet

Carolina Chickadee

Carolina Wren

Caspian Tern

Cassin’s Auklet

Cassin’s Finch

CAKI

CASP

CAEG

CASW

CAYT

CEDW

CERW

CHMA

CBCH

CCL0

CSWA

CHRA

CHSW

CHPE

CHSP

CHSH

CWWI

CITE

CLRA

CLGR

CLNU

CCRO

CCSP

CLSW

COLW

COAM

COB0

CBHA

CBHG

COBU

COCA

COCH

COEI

COG0

COGR

COGD

COLO

COME

COMO

COMU

cola

CON1

COPA

CPOC

COP0

CORA

Cassin’s Kingbird

Cassin’s Sparrow

Cattle Egret

Cave Swallow

Cayenne Tern

Cedar Waxwing

Cerulean Warbler

Chestnut Manakin

Chestnut-backed Chickadee

Chestnut-collared Longspur

Chestnut-sided Warbler

Chihuahuan Raven

Chimney Swift

Chinstrap Penguin

Chipping Sparrow

Christmas Shearwater

Chuck-wills-widow

Cinnamon Teal

Clapper Rail

Clark’s Grebe

Clark’s Nutcracker

Clay-colored Robin

Clay-colored Sparrow

Cliff Swallow

Colima Warbler

Common Amakihi

Common Barn-Owl

Common Black Hawk

Common Black-headed Gull

Common Bushtit

Common Canary

Common Chaffinch

Common Eider

Common Goldeneye

Common Grackle

Common Ground-Dove

Common Loon

Common Merganser

Common Moorhen

Common Murre

Common Myna

Common Nighthawk

Common Pauraque

Common Pochard

Common Poor-will

Common Raven

2 3
Alpha Alpha Alpha

Code Common Name Code Common Name Code Common Name

CORE

CORB

CRPL

CORO

COSA

COSN

COTE

COMW

COYE

CONW

COPE

COHA

COSH

COHU

COKI

CRMU

CRAU

CRCA

CRHO

CRMY

CRTE

CRTH

CUSA

CBTH

DRPE

DICK

DCCO

DOPR

DOVE

DOW0

DUNL

DUFL

DSSP

DUTH

DUWA

DCFL

EAGR

EATR

EABL

EAKI

EAME

EAPH

EASO

ETTI

EAWP

ELTE

24

Common Redpoll

Common Reed-bunting

Common Ringed Plover

Common Rosefinch

Common Sandpiper

Common Snipe

Common Tern

Common Waxbill

Common Yellowthroat

Connecticut Warbler

Cook’s Petrel

Cooper’s Hawk

Cory’s Shearwater

Costa’s Hummingbird

Couch’s Kingbird

Craveri’s Mm-relet

Crested Auklet

Crested Caracara

Crested Honeycreeper

Crested Mynah

Crested Tern

Crissal Thrasher

Curlew Sandpiper

Curve-billed Thrasher

Dark-rumped Petrel

Dickcissel

Double-crested Cormorant

Dove Priori

Dovekie

Downy Woodpecker

Dunlin

Dusky Flycatcher

Dusky Seaside Sparrow

Dusky Thrush

Dusky Warbler

Dusky-capped Flycatcher

Eared Grebe

Eared Trogon

Eastern Bluebird

Eastern Kingbird

Eastern Meadowlark

Eastern Phoebe

Eastern Screech-Owl

Eastern Tufted Titmouse

Eastern Wood-Pewee

Elegant Tern

ELTR Elegant Trogon

ELEP Elepaio

ELOW Elf Owl

EWWA Elfin Wood Warbler

EMGO Emperor Goose

EMPE Emperor Penguin

EUBU Eurasian Bullfinch

EUDO Eurasian Dotterel

EUKE Eurasian Kestrel

EUSK Eurasian Skylark

EUWI Eurasian Wigeon

EUGO European Goldfinch

EGWT European Green-winged Teal

EUST European Starling

ETSP European Tree Sparrow

EUWO European Woodcock

EVGR Evening Grosbeak

EBTH Eye-browed Thrush

FAPE Fairy Penguin

FAPR Fairy Prion

FATE Falcated Teal

FTWA Fan-tailed Warbler

FEHA Ferruginous Hawk

FEPO Ferruginous Pygmy-Owl

FISP Field Sparrow

FIEL Fieldfare

FICR Fish Crow

FSSP Five-striped Sparrow

FLOW Flammulated Owl

FFSH Flesh-footed Shearwater

FLIN Flicker Intergrade

FTFL Fork-tailed Flycatcher

FTSP Fork-tailed Storm-Petrel

FOTE Forster’s Tern

FOSP Fox Sparrow

FRGU Franklin’s Gull

FUWD Fulvous Whistling-Duck

GADW Gadwall

GALP Galapagos Penguin

GWCS Gambel’s White-crowned Sparrow

GARG Garganey

GEPE Gentoo Penguin

GIWO Gila Woodpecker

GIFL Gilded Flicker

GLGU Glaucous Gull

GWGU Glaucous-winged Gull

GLIB

GOB1

GOEA

GCWA

GCKI

GCSP

GOLW

GFWO

GWWA

GRWA

GRSP

GRBU

GRCA

GRFL

GRHA

GRAJ

GRAK

GRVI

GRAW

GBSP

GRAT

GBJA

GBMA

GCTH

GCRF

GCYE

GHAL

GHJU

GSFL

GBBG

GTBH

GRCO

GREG

GRFR

GGOW

CHOW

GKIS

GRSK

GWHE

GCFL

GTGR

GABU

GAEL

GAGR

GREP

GREF

Glossy Ibis

Golden Bishop

Golden Eagle

Golden-cheeked Warbler

Golden-crowned Kinglet

Golden-crowned Sparrow

Golden-crowned Warbler

Golden-fronted Woodpecker

Golden-winged Warbler

Grace’s Warbler

Grasshopper Sparrow

Gray Bunting

Gray Catbird

Gray Flycatcher

Gray Hawk

Gray Jay

Gray Kingbird

Gray Vireo

Gray Wagtail

Gray-backed Storm-Petrel

Gray-backed Tern

Gray-breasted Jay

Gray-breasted Martin

Gray-cheeked Thrush

Gray-crowned Rosy-Finch

Gray-crowned Yellowthroat

Gray-headed Albatross

Gray-headed Junco

Gray-spotted Flycatcher

Great Black-backed Gull

Great Blue Heron

Great Cormorant

Great Egret

Great Frigatebird

Great Gray Owl

Great Homed Owl

Great Kiskadee

Great Skua

Great White Heron

Great-crested Flycatcher

Great-tailed Grackle

Greater Antillean Bullfinch

Greater Antillean Elaenia

Greater Antillean Grackle

Greater Antillean Pewee

Greater Flamingo
Alpha Alpha Alpha

Code Common Name Code Common Name Code Common Name

GRGP

GRPE

GRRO

GRSC

GRSH

GSGO

GWFG

GRYE

GNLT

GREJ

GKIN

GRMA

GRVE

GNBH

GTTO

GTCA

GREL

GBAN

GUCO

GBTE

GYRF

HAWO

HAFL

HRLH

YARD

HRSH

HASP

HCRE

HACO

HCRO

HAWD

HAG0

HWHA

HAM0

HAST

HAWF

HEEG

HESP

HETA

HETH

HEWA

HERG

HPKT

HPRT

HORE

HOME

Greater Golden-Plover

Greater Pewee

Greater Roadrunner

Greater Scaup

Greater Shearwater

Greater Snow Goose

Greater White-fronted Goose

Greater Yellowlegs

Greatr Neckl’d Laughing-thrush

Green Jay

Green Kingfisher

Green Mango

Green Violet-ear

Green-backed Heron

Green-tailed Towhee

Green-throated Carib

Greenish Elaenia

Groove-billed Ani

Guanay Cormorant

Gull-billed Tern

Gyrfalcon

Hairy Woodpecker

Hammond’s Flycatcher

Harlan’s Hawk

Harlequin Duck

Harris’ Hawk

Harris’ Sparrow

Hawaii Creeper

Hawaiian Coot

Hawaiian Crow

Hawaiian Duck

Hawaiian Goose

Hawaiian Hawk

Hawaiian Moorhen

Hawaiian Stilt

Hawfinch

Heermann’s Gull

Henslow’s Sparrow

Hepatic Tanager

Hermit Thrush

Hermit Warbler

Herring Gull

Hispaniolan Parakeet

Hispaniolan Parrot

Hoary Redpoll

Hooded Merganser

HOOR Hooded Oriole

HOWA Hooded Warbler

HOWF Hooded Weaver-finch

HBKI Hook-billed Kite

HOGR Homed Grebe

HOLA Homed Lark

HOPU Homed Puffin

HOFI House Finch

HOSP House Sparrow

HOWR House Wren

HUGO Hudsonian Godwit

HUPE Humboldt Penguin

HUVI Hutton’s Vireo

ICGU Iceland Gull

IIWI Iiwi

INDO Inca Dove

IHMY Indian Hill Myna

INBU Indigo Bunting

IPSP Ipswich Sparrow

IVGU Ivory Gull

IBWO Ivory-billed Woodpecker

JACR Jamaican Crow

JAEL Jamaican Elaenia

JAEU Jamaican Euphonia

JAMA Jamaican Mango

JAOR Jamaican Oriole

JATO Jamaican Tody

JWEV Jamaican White-eyed Vireo

JAW0 Jamaican Woodpecker

JAWE Japanese White-eye

JASP Java Sparrow

KAMA Kamao

KAAK Kauai Akialoa

KACR Kauai Creeper

KAOO Kauai 00

KEWA Kentucky Warbler

KEPE Kerguelen Petrel

KETE Kerguelen Tern

MQD Key West Quail-Dove

KILL Killdeer

KIEI King Eider

KIPE King Penguin

KIRA King Rail

KIWA Kirtland’s Warbler

KIMU Kittlitz’s Murrelet

LBWO Ladder-backed Woodpecker

LALO Lapland Longspur

LARB Lark Bunting

LASP Lark Sparrow

LASF Lasagra Flycatcher

LAGU Laughing Gull

LAFF Lavender Fire-finch

LAG0 Lawrence’s Goldfinch

LAWA Lawrence’s Warbler

LAAL Laysan Albatross

LADU Laysan Duck

LAFI Laysan Finch

LAZB Lazuli Bunting

LCSP Le Conte’s Sparrow

LCTH Le Conte’s Thrasher

LHSP Leach’s Storm-Petrel

LEAU Least Auklet

LEBI Least Bittern

LEFL Least Flycatcher

LEGR Least Grebe

LESA Least Sandpiper

LTSP Least Storm-Petrel

LETE Least Tern

LESB Lesser Antillean Bullfinch

LESF Lesser Antillean Flycatcher

LAPE Lesser Antillean Pewee

LBBG Lesser Black-backed Gull

LEFR Lesser Frigatebird

LEGP Lesser Golden-Plover

LEG0 Lesser Goldfinch

LEN1 Lesser Nighthawk

LESC Lesser Scaup

LESH Lesser Sheathbill

LSGO Lesser Snow Goose

LWFG Lesser White-fronted Goose

LEYE Lesser Yellowlegs

LEWO Lewis’ Woodpecker

LMSA Light-mantled Sooty Albatross

LIMP Limpkin

LISP Lincoln’s Sparrow

LBHE Little Blue Heron

LIBU Little Bunting

LIGU Little Gull

LISH Little Shearwater

LOKI Loggerhead Kingbird

LOSH Loggerhead Shrike

LBCU Long-billed Curlew

25
Alpha Alpha Alpha

Code Common Name Code Common Name Code Common Name

LBDO

LBTH

LEOW

LTJA

LOWA

LUHU

LUWA

MGWA

MACP

MAGU

MAGI’

MAFR

MAHU

MAWA

MALL

MBDH

MACU

MASH

MAGO

MAMU

MAID

MAWR

MABO

MADU

MACR

MAI’A

MCLO

MKBU

MEGR

MELT

MERL

MEGU

MECH

MECR

MEDU

MIGW

MILL

MIKI

MOCR

MONP

MODU

MOPE

MOBL

MOCH

MOUP

Mwcs

2 6

-..

Long-billed Dowitcher

Long-billed Thrasher

Long-eared Owl

Long-tailed Jaeger

Louisiana Waterthrush

Lucifer Hummingbird

Lucy’s Warbler

MacGillivray’s Warbler

Macaroni Penguin

Magellan Gull

Magellanic Penguin

Magnificent Frigatebird

Magnificent Hummingbird

Magnolia Warbler

Mallard

Mallard X Black Duck Hybrid

Mangrove Cuckoo

Manx Shearwater

Marbled Godwit

Marbled Murrelet

Marianas Fruit-Dove

Marsh Wren

Masked Booby

Masked Duck

Maui Creeper

Maui Parrotbill

McCown’s Longspur

McKay’s Bunting

Melodious Grassquit

Melodious Laughing-thrush

Merlin

Mew Gull

Mexican Chickadee

Mexican Crow

Mexican Duck

Middendorff’s Grasshop-Warbler

Millerbird

Mississippi Kite

Molokai Creeper

Mongolian Plover

Mottled Duck

Mottled Petrel

Mountain Bluebird

Mountain Chickadee

Mountain Plover

Mountain White-crowned Sparrow

MOD0 Mourning Dove OWI’A

MOWA Mourning Warbler ORAN

MUSW Mute Swan OROR

MYWA Myrtle Warbler ORJU

NAWA Nashville Warbler ORGR

NESH Newell’s Shearwater OSPR

NIFI Nihoa Finch o u

NBTY Northern Beardless Tyrannulet OVEN

NOCA Northern Cardinal PALO

NOFU Northern Fulmar PABU

NOGA Northern Gannet PARE

NGI’E Northern Giant Petrel PAL1

NOGO Northern Goshawk PALB

NOHA Northern Harrier I’AAU

NOHO Northern Hawk-Owl I’AJA

NOJA Northern Jacana PETH

NOLA Northern Lapwing PEP1

NOM0 Northern Mockingbird PESA

NOPA Northern Parula PECO

NOI’I Northern Pintail PEFA

NOPO Northern Pygmy-Owl PHAI

NRWS Northern Rough-winged Swallow PHVI

NSWO Northern Saw-whet Owl PHTD

NSHO Northern Shoveler PHPE

NSHR Northern Shrike PBGR

NOWA Northern Waterthrush PIGU

NOWH Northern Wheatear PIWO

NOCR Northwestern Crow PTWI-I

NUKU Nukupuu PIGR

NUMA Nutmeg Manakin PISI

Nwcs Nuttall’s White-crown Sparrow PIWA

NUWO Nuttall’s Woodpecker PFGO

NUFL Nutting’s Flycatcher PFSH

OACR Oahu Creeper PIJA

OLDS Oldsquaw PIPL

OLCO Olivaceous Cormorant PLPI

OLSP Olive Sparrow PLTI

OLTP Olive Tree-pipit PCST

OLWA Olive Warbler POJA

OLIW Olive-capped Warbler POUL

OSFL Olive-sided Flycatcher PRFA

OLOM Olomao PRAW

OMAO Omao PROW

ORAW Orange-cheeked Waxbill PUAI

OCWA Orange-crowned Warbler PUEB

OFI’A Orange-fronted Parakeet PREM

Orange-winged Parrot

Orangequit

Orchard Oriole

Oregon Junco

Oriental Greenfinch

Osprey

ou

Ovenbird

Pacific Loon

Painted Bunting

Painted Redstart

Palila

Pallas’ Reed-bunting

Parakeet Auklet

Parasitic Jaeger

Pearly-eyed Thrasher

Pechora Pipit

Pectoral Sandpiper

Pelagic Cormorant

Peregrine Falcon

Phainopepla

Philadelphia Vireo

Philippine Turtle-Dove

Phoenix Petrel

Pied-billed Grebe

Pigeon Guillemot

Pileated Woodpecker

Pin-tailed Whydah

Pine Grosbeak

Pine Siskin

Pine Warbler

Pink-footed Goose

Pink-footed Shearwater

Pinyon Jay

Piping Plover

Plain Pigeon

Plain Titmouse

Plain-capped Star-throat

Pomarine Jaeger

Poo-uli

Prairie Falcon

Prairie Warbler

Prothonotary Warbler

Puaiohi

Puerto Rican Bullfinch

Puerto Rican Emerald
Alpha Alpha Alpha

Code Common Name Code Common Name Code Common Name

PRFL Puerto Rican Flycatcher

PRLC Puerto Rican Lizard-Cuckoo

PRNI Puerto Rican Nightjar

PRPA Puerto Rican Parrot

PRSO Puerto Rican Screech-Owl

PRTA Puerto Rican Tanager

PRTO Puerto Rican Tody

PRVI Puerto Rican Vie0

PRWO Puerto Rican Woodpecker

PSWS Puget Snd White-crown Sparrow

PUFI Purple Finch

PUGA Purple Gallinule

PUMA Purple Martin

PUSA Purple Sandpiper

PYNU Pygmy Nuthatch

PYRR Pyrrhuloxia

RAZO Razorbill

REAV Red Avadavat

REBI Red Bishop

RECR Red Crossbill

REKN Red Knot

REPH Red Phalarope

RESI Red Siskin

RBWO Red-bellied Woodpecker

RBLE Red-billed Leiothrix

RBPI Red-billed Pigeon

RBTR Red-billed Tropicbird

RBFL Red-breasted Flycatcher

RBME Red-breasted Merganser

RBNU Red-breasted Nuthatch

RBSA Red-breasted Sapsucker

RCCO Red-cheeked Cordonbleu

RCWO Red-cockaded Woodpecker

RCCA Red-crested Cardinal

RCPA Red-crowned Parrot

REV1 Red-eyed Vireo

RFCO Red-faced Cormorant

RFWA Red-faced Warbler

RFBO Red-footed Booby

RI-IWO Red-headed Woodpecker

RLKI Red-legged Kittiwake

RLTH Red-legged Thrush

RNSA Red-naped Sapsucker

RNGR Red-necked Grebe

RNPH Red-necked Phalarope

RSFL Red-shafted Flicker

RSHA

RTHA

RTTR

RTLO

RTPI

RVBU

RWBU

RWBL

REEG

REDH

REDW

REHE

RHAU

RBGU

RNDU

RIKI

RITD

ROHA

ROSA

ROWR

ROCP

RBGR

RTBE

ROSP

ROST

ROGO

ROGU

RLHA

ROAL

ROYP

ROYT

RCKI

RTHU

RUDU

RUGD

RUQD

RUTU

RUFF

RUHU

RBRO

RCWA

RUFS

RCSP

RSTO

RUFH

RTSO

Red-shouldered Hawk

Red-tailed Hawk

Red-tailed Tropicbird

Red-throated Loon

Red-throated Pipit

Red-vented Bulbul

Red-whiskered Bulbul

Red-winged Blackbird

Reddish Egret

Redhead

Redwing

Reef Heron

Rhinoceros Auklet

Ring-billed Gull

Ring-necked Duck

Ringed Kingfisher

Ringed Turtle-Dove

Roadside Hawk

Rock Sandpiper

Rock Wren

Rockhopper Penguin

Rose-breasted Grosbeak

Rose-throated Becard

Roseate Spoonbill

Roseate Tern

Ross’ Goose

Ross’ Gull

Rough-legged Hawk

Royal Albatross

Royal Penguin

Royal Tern

Ruby-crowned Ringlet

Ruby-throated Hummingbird

Ruddy Duck

Ruddy Ground-Dove

Ruddy Quail-Dove

Ruddy Tumstone

Ruff

Rufous Hummingbird

Rufous-backed Robin

Rufous-capped Warbler

Rufous-collared Sparrow

Rufous-crowned Sparrow

Rufous-sided Towhee

Rufous-tailed Hummingbird

Rufous-throated Solitaire

RWSP

RUBU

RUBL

SAGU

SAFL

SAFI

SAGS

SATH

SAND

SACR

SATE

SAVS

SAPH

SNPI

SCIB

SCTA

STFL

SCOR

SCJA

SESP

SEWR

SEPL

SESA

SSHA

SHAS

STSP

SHCO

SBDO

SEOW

STAL

STHA

SHOS

STSW

SHAL

SIAC

SIFL

SIRU

SIT1

SCJU

SLAR

SBGU

SCGO

SMLO

SBAN

SNKI

SNBU

Rufous-winged Sparrow

Rustic Bunting

Rusty Blackbird

Sabine’s Gull

Sad Flycatcher

Saffron Finch

Sage Sparrow

Sage Thrasher

Sanderling

Sandhill Crane

Sandwich Tern

Savannah Sparrow

Say’s Phoebe

Scaly-naped Pigeon

Scarlet Ibis

Scarlet Tanager

Scissor-tailed Flycatcher

Scott’s Oriole

Scrub Jay

Seaside Sparrow

Sedge Wren

Semipalmated Plover

Semipalmated Sandpiper

Sharp-shinned Hawk

Sharp-tailed Sandpiper

Sharp-tailed Sparrow

Shiny Cowbird

Short-billed Dowitcher

Short-eared Owl

Short-tailed Albatross

Short-tailed Hawk

Short-tailed Shearwater

Short-tailed Swift

Shy Albatross

Siberian Accentor

Siberian Flycatcher

Siberian Rubythroat

Siberian Tit

Slate-colored Junco

Slate-throated Redstart

Slaty-backed Gull

Small Canada Goose

Smiths Longspur

Smooth-billed Ani

Snail Rite

Snow Bunting

27
Alpha Alpha Alpha

Code Common Name Code Common Name Code Common Name

SNPE

SBGI

SNEG

SNOW

SNPL

SNSH

SOSA

SOVI

SOSP

SOAL

SOSH

SOOP

SOTE

SORA

SPSK

SBBG

SOFU

SGPE

SHWR

SPEI

SPOS

SPDO

SPOW

SPSA

SPTO

SPOO

SPPI

STEI

STJA

STSA

STOF

STRO

STRS

STTL

STWO

SHTA

SBFL

SUTA

susc

SURF

SUWA

SWHA

SWTH

SWWA

SWSP

TSBL

28

r-‘

Snow Petrel

Snow X Blue Goose Intergrade

Snowy Egret

Snowy Owl

Snowy Plover

Snowy Sheathbill

Solitary Sandpiper

Solitary Vireo

Song Sparrow

Sooty Albatross

Sooty Shearwater

Sooty Storm-Petrel

Sooty Tern

Sora

South Polar Skua

Southern Black-backed Gull

Southern Fulmar

Southern Giant Petrel

Southern House Wren

Spectacled Eider

Spoonbill Sandpiper

Spotted Dove

Spotted Owl

Spotted Sandpiper

Spotted Towhee

Spotted-breasted Oriole

Sprague’s Pipit

Steller’s Eider

Steller’s Jay

Stilt Sandpiper

Stolid Flycatcher

Streak-backed Oriole

Streaked Shearwater

Streamertail

Strickland’s Woodpecker

Stripe-headed Tanager

Sulphur-bellied Flycatcher

Summer Tanager

Surf Scoter

Surfbird

Sutton’s Warbler

Swainson’s Hawk

Swainson’s Thrush

Swainson’s Warbler

Swamp Sparrow

Tawny-shouldered Blackbird

TEWA

THGU

TBKI

TBMU

TBPA

TBVI

TBPR

TTWO

TOSH

TOSO

TOWA

TRFL

TRES

TREM

TRBL

TRHE

TRKI

TRMO

TRl’A

TROU

TRUS

TUDU

TUPU

TUVU

UL’EJ

UNGU

UNHU

UNTE

UPSA

VABU

VATH

VAT1

VASW

VEER

VERD

VEFL

VESP

VIPE

VCHU

VGSW

VIRA

VIWA

WAAL

WATA

WAS1

WAVI

Tennessee Warbler

Thayer’s Gull

Thick-billed Kingbird

Thick-billed Murre

Thick-billed Parrot

Thick-billed Vireo

Thin-billed Priori

Three-toed Woodpecker

Townsend’s Shearwater

Townsend’s Solitaire

Townsend’s Warbler

Traill’s Flycatcher

Tree Swallow

Trembler

Tricolored Blackbird

Tricolored Heron

Tropical Kingbird

Tropical Mockingbird

Tropical Panda

Troupial

Trumpeter Swan

Tufted Duck

Tufted Puffin

Turkey Vulture

Unidentified Dark-eyed Junco

Unidentified Gull

Unidentified Hummingbird

Unidentified Teal

Upland Sandpiper

Varied Bunting

Varied Thrush

Varied Tit

Vaux’s Swift

Veery

Verdin

Vermilion Flycatcher

Vesper Sparrow

Victoria Penguin

Violet-crowned Hummingbird

Violet-green Swallow

Virginia Rail

Virginia’s Warbler

Wandering Albatross

Wandering Tattler

Warbling Silverbill

Warbling Vireo

WAPI Water Pipit

WRSP Wedge-rumped Storm-Petrel

WTSH Wedge-tailed Shearwater

WIWD West Indian Whistling-duck

WEBL Western Bluebird

WEFL Western Flycatcher

WEGR Western Grebe

WEGU Western Gull

WEKI Western Kingbird

WEME Western Meadowlark

WI’WA Western Palm Warbler

WESA Western Sandpiper

WESO Western Screech-Owl

WETA Western Tanager

WEWP Western Wood-Pewee

WHIM Whimbrel

WPWI Whip-poor-will

WHAU Whiskered Auklet

WI-Is0 Whiskered Screech-Owl

WHSW Whistling Swan

WI-m White Ibis

WHTE White Tern

WHWA White Wagtail

WBDO White-bellied Dove

WBNU White-breasted Nuthatch

WHIP White-cheeked Pintail

WCPE White-chinned Petrel

WCTH White-chinned Thrush

WCSE White-collared Seedeater

WCPI White-crowned Pigeon

WCSP White-crowned Sparrow

WEHU White-eared Hummingbird

WETH White-eyed Thrush

WEVI White-eyed Vireo

WFIB White-faced Ibis

WFSP White-faced Storm-Petrel

WI-IWO White-headed Woodpecker

WNCR White-necked Crow

WRSA White-rumped Sandpiper

WRSH White-rumped Shama

WTHA White-tailed Hawk

WTTR White-tailed Tropicbird

WTGD White-throated Ground-Dove

WTSP White-throated Sparrow

WHSP White-throated Storm-Petrel

WTSW White-throated Swift
Alpha Alpha Alpha

Code Common Name Code Common Name Code Common Name

WTDO

WWCR

WWDO

WWJU

wwsc

WWTE

WHOS

WHCR

WILL

WISA

WIFL

WIPL

WISP

WIWA

WIPH

WIWR

WODU

WOST

White-tipped Dove

White-vented Myna

White-winged Crossbill

White-winged Dove

White-winged Junco

White-winged Scoter

White-winged Tern

Whooper Swan

Whooping Crane

Willet

Williamson’s Sapsucker

Willow Flycatcher

Wilson’s Plover

Wilson’s Storm-Petrel

Wilson’s Warbler

Wilsons Phalarope

Winter Wren

Wood Duck

Wood Stork

WOTH

WOWA

WEWA

WOSP

WREN

XAMU

YEGR

YPWA

YERA

YWAG

YWAR

YBEL

YBFL

YBSA

YBSE

YBCA

YBCU

YBLO

YBMA

Wood Thrush

Wood Warbler

Worm-eating Warbler

Worthen’s Sparrow

Wrentit

Xantus’ Murrelet

Yellow Grosbeak

Yellow Palm Warbler

Yellow Rail

Yellow Wagtail

Yellow Warbler

Yellow-bellied Elaenia

Yellow-bellied Flycatcher

Yellow-bellied Sapsucker

Yellow-bellied Seedeater

Yellow-billed Cardinal

Yellow-billed Cuckoo

Yellow-billed Loon

Yellow-billed Magpie

YBCH

YBCR

YCNH

mJU

YFGR

YFGU

YFCA

YGVI

YHBL

YNAL

YSFL

YSBL

YSGR

YTVI

YTWA

ZEBD

ZEND

ZTHA

Yellow-breasted Chat

Yellow-breasted Crake

Yellow-crowned Night-Heron

Yellow-eyed Junco

Yellow-faced Grassquit

Yellow-footed Gull

Yellow-fronted Canary

Yellow-green Vireo

Yellow-headed Blackbird

Yellow-nosed Albatross

Yellow-shafted Flicker

Yellow-shouldered Blackbird

Yellow-shouldered Grassquit

Yellow-throated Vireo

Yellow-throated Warbler

Zebra Dove

Zenaida Dove

Zone-tailed Hawk

29
APPENDIX D. Proposed structure of computer files for point count data.

VARIABLE COLUMNS DESCRIPTION OF VARIABLE

STATE

REGION

STATION

POINT NUMBER 17-19

VISIT NUMBER 20

YEAR 21-24

MONTH 25-26

DAY 27-28

TIME 29-32

OBSERVER 33-35

TEMPERATURE 36-37

WIND 38

SKY CONDITION 39

HABITAT 40

FOREST TYPE

TIMBER TYPE

GEO-CODING SYSTEM 45-46

N-S COORDINATES 47-53

E-W COORDINATES 54-60

SPECIES 61-64

0-3min/ c25m 65-66

4-5min/ c25m 67-68

6-l Omin / c25m 69-70

0-3min / 25-50m 71-72

4-5min / 25-50m

6-lOmin/2~50m

0-3min/ >50m

4-5min/ >50m

6-lOmin/>50m

0-3min / fly-overs

P5min/ fly-overs

6-lOmin/fly-overs

O-3 min/c50m

4-5min/ c50m

6-lOmin/c50m

O-3 min/ total

4-5min/ total

6-lOmin/total

SUBMITTER

REMARKS

l-2

3-12

13-16

4143

44

73-74 Number of birds counted between 25-50 m (82-164 ft) during minutes P5

75-76 Number of birds counted between 25-50 m (82-164 ft) during minutes 6-10

77-78 Number of birds counted beyond 50 m (164 ft) during the first 3 minutes

79-80 Number of birds counted beyond 50 m (164 ft) during minutes 4-5

81-82 Number of birds counted beyond 50 m (164 A) during minutes 6-10

83-84 Number of fly-overs counted during the first 3 minutes

85-86 Number of fly-overs counted during minutes 4-5

87-88 Number of fly-overs counted during minutes 6-10

89-90 Number of birds counted within 50 m (164 ft) during the first 3 minutes

91-92 Number of birds counted within 50 m (164 A) during minutes P5

93-94 Number of birds counted within 50 m (164 ft) during minutes 6-10

95-96 Total number of birds counted during the first 3 minutes

97-98 Total number of birds counted during minutes P5

99-100 Total number of birds counted during minutes 6-10

101-105 Initials of the person or entity submitting the data

106-150 Notes and observations

State

Regional description of area (e.g., STFRANNF = St. Francis National

Forest or WRNWR = White River National Wildlife Refuge)

Station, compartment, or study area within region (e.g.,

CMPl =compartment 1)

Number of point within the station l-999

Visits to same point within the same year (1) l-9

Year (19xX)

Month (01-12)

Day (0131)

Time of day; 2400 hour clock (e.g., 0732), in standard, not daylight time

Observer identification (e.g., initials)

Temperature “C

Beaufort scale (Appendix B)

Sky condition, combining cloud cover and precipitation (Appendix B)

General habitat characterization (A=agriculture, F=forest, G=grasslands,

U=urban/suburban)

Society of American Foresters Forest Cover Type (001-999)

B=brush/scrub-shrub, C=cut/clear, P=poletimber, S=sawtimber,

M=mature/old growth, D=disease/dead/damaged

0 if in lat-long; UTM zone if in UTM (e.g., 15 or 16 within the Mississippi

ValIe y)

UTM (Northing - 7 digits) or latitude (DDMMSS) = (3OO42’33”)

UTM (Easting - 6 digits) or longitude (DDDMMSS) = (089°14’59”)

Alpha code - from Appendix C (e.g., NOCA, WOTH)

Number of birds counted within 25 m (82 ft) during the first 3 minutes

Number of birds counted within 25 m (82 A) during minutes 4-5

Number of birds counted within 25 m (82 ft) during minutes 6-10

Number of birds counted between 25-50 m (82-164 ft) during the first 3

minutes

30
Appendix E. Determining sample size by the

power method.

The power method (Neter and Wasserman 1974) can

be used to determine the desired sample size for a

particular application. Unfortunately it requires

some math, some previous data, and some reference

tables. The following is an example of the use of this

method. The math is outlined below, the previous

data come from Smith and others (1993), and the

reference tables appear at the end of this appendix.

Neter and Wasserman (1974) propose to determine

the sample size required to decide whether mean

numbers of birds recorded for a particular species

from counts in different habitats are equal. Suppose

there are four habitats of interest for a particular

situation. Suppose the species is red-eyed vireo (Vireo

olivuceus). What is needed is to find a sample size

with which to test the working hypothesis that

numbers of red-eyed vireos are similar among the

four habitats being examined:

Case 1: All four means are equal, p1 = uZ = us = pd.

Case 2: All four means are not equal.

For this test, it is necessary to determine a particular

amount of difference that is meaningful to detect and

then to select appropriate a and S values. For the

example, a = 0.05 and S = 0.10 have been selected.

The power of the test is given by l-S, in this case 0.90.

For this calculation, it is necessary to compute 4,

called the noncentrality parameter, which reflects

how evenly dispersed the means for individual

habitats are relative to the overall mean. For this

example, it is assumed that the mean numbers of

birds in the various habitats are uniformly dispersed

and symmetrical about the overall mean. This

approach is used because it minimizes the value of $.

and thus provides the most conservative (i.e.,

maximizes) estimates of minimum sample size (Neter

and Wasserman 1974).

The formula for #, a suitable approximation of 4, is

where:

4’ = estimate of the noncentrality parameter 4,

MSE = mean square error from analysis of variance,

pi = mean number of birds in habitat i,

p= overall mean, and

Y = number of habitats, also called factor levels (four,

in this case).

Once 4’ has been calculated, the minimum sample

size for each factor level can be obtained for a

specifed a and S from tables E-l through E-5. The

values for red-eyed vireo for the example come from

Smith and others (1993), as follows: p = 0.524 birds

per count, MSE = 0.44 birds per count. Habitats to be

compared are r = 4. The only remaining need is for a

manager’s decision as to how different the means

need to be; i.e., ui - u.

Suppose the manager chooses 25 percent of the mean,

0.131 birds per count. Then the calculation of

minimum sample size is as follows:

I$’ = 0.33

Looking up the value of 4’ = 0.33, with a = 0.05, S =

0.1, and Y = 4 habitats in table E-4 yields an estimate

of 33 counts per habitat. Thus, in order to distinguish

differences in relative abundance of 25 percent of the

mean of red-eyed vireos in 4 different habitats in

bottomland forests of the Mississippi Alluvial Valley,

according to the pilot data of Smith and others (1993),

at least 33 separate point counts from each habitat

will be necessary.

3 1
Table E-l.-Minimum sample sizes calculated by Smith and others (1993) according to the

power method for several bird species and community measures; these sample

sizes are the number of point counts fm each factor level necessary to detect the

listed differences in mean values with a = 0.05 and&I = 0.10, as based on the

obsetved variation among the three regions in the Mississip’ Alluvial Valley

Sample size required to dectect a difference equivalent to

Variable

Actual kO.25

difference* birds+

*25%

of mean

Yellow-billed cuckoo

(coccyzus americunus)

Red-bellied woodpecker

(Melanerpes carolinus)

Acadian flycatcher

(Empidonax virescens)

Carolina chickadee

(Parus carolinensis)

Tufted titmouse

(Pawbicolor)

Carolina wren

(Th yothorus ludovicianus)

Blue-gray gnatcatcher

(Polioptila caeruleu)

Wood thrush

(Hylocichla mustelina)

Yellow-throated vireo

(Vireo flavifrons)

Red-eyed vireo

(Vireo olivuceus)

Northern parula

(Purulu americana)

American redstart

(Setophaga ruticillu)

Prothonotary warbler

(Protonoturia citreu)

Kentucky warbler

(Oporornis formosus)

Hooded warbler

(Wilsonia citrina)

Summer tanager

(Pirungu rubra)

Northern cardinal

(Cardinalis cardinalis)

Indigo bunting

(Pusserina cyuneu)

Rufous-sided towhee

(Pipilo e ythrophthalmus)

Brown-headed cowbird

(Molothrus ater)

80

>200

53

85

58

>200

9

>200

100

15

2 7

>200

9

8 0

65

53

>200

5 0

>200

2 3

44

9 0

65

>200

53

85

58

2 7

9

3 7

2 3

9

5 8

9

9

2 7

>200

19

9

44

100

53

80

>200

8 0

4 4

100

>200

>200

>200

>200

>200

70

>200

>200

>200

53

>200

>200

>200

Total species

Total individuals

41 >500 5

2 0 >500 5

*Actual difference is the difference observed by Smith and

others (1993) among mean values in the various regions

of the Mississippi Alluvial Valley in which their counts

were conducted.

‘Absolute difference between mean values for the various

regions in the Mississippi Alluvial Valley.

32
Table E-2.-Cdtical values of Student’s t*

Table E-2a.-Critical value for power of test (I-/3)

Desired power of test (1-p)

df 0.60 0.70 0.80 0.90 0.95 0.99

1 1.376 1.963 3.078 6.314 12.706 63.657

Table E-2b.-Critical value for statistical

significance a

Desired statistical significance a

df 0.40 0.30 0.20 0.10 0.0 0.01

10 0.879 1.093 1.372 1.812 2.228 3.169

15 0.866 1.074 1.341 1.753 2.131 2.947

20 0.860 1.064 1.325 1.725 2.086 2.845

25 0.856 1.058 1.316 1.708 2.060 2.787

30 0.854 1.055 1.310 1.697 2.042 2.750

40 0.851 1.050 1.303 1.684 2.021 2.704

60 0.848 1.046 1.296 1.671 2.000 2.660

120 0.845 1.041 1.289 1.658 1.980 2.617

00 0.842 1.036 1.282 1.645 1.960 2.576

‘Adapted from table 2 of Freese (1967).

Table E-3.-Determination of sample size for point counts with a = 0.10”

Number of

factor levels

4’

B 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.50 0.60 0.70 0.80

r=2 0.05 2165 542 241 136 8 7 61 4 5 3 5 2 2 1 6 1 2 9

0.102 1713237 429 310 19138 108 7 6 9 5 0 4 8 3 5 3 6 2 27 0 1 3 8 139 1 07 :

0.30 942 236 105 6 0 3 8 27 2 0 1 5 1 0 7 6 5

r=3 0.05 1738 435 194 109 7 0 4 9 3 6 2 8 1 8 1 3 1 0 8

0.10 1395 349 156 8 8 5 7 4 0 2 9 2 3 1 5 1 1 8 6

0.20 1029 258 115 6 5 41 2 9 2 2 1 7 1 1 8 6 5

0.30 797 200 8 9 5 0 3 2 2 3 1 7 1 3 9 6 5 4

r=4 0.05 1458 365 163 9 2 5 9 41 3 0 24 1 5 11 8 7

0.10 1180 296 132 7 4 4 8 3 4 2 5 1 9 1 3 9 7 5

0.20 883 221 9 9 5 6 3 6 2 5 i: 1 5 1 0 7 5 4

0.30 690 173 7 7 4 3 2 8 20 11 8 6 4 4

r=5 0.05 1267 317 141 8 0 51 3 6 27 2 1 13 1 0 7 6

0.10 1031 258 115 6 5 4 2 2 9 2 2 1 7 11 8 6 5

0.20 773 193 8 7 4 9 3 2 2 2 1 7 1 3 9 6 5 4

0.30 612 154 6 8 3 8 2 5 1 8 1 3 1 0 7 5 4 3

r=9 0.05 765 192 8 6 4 9 31 2 2 1 6 1 3 8 6 5 4

0.10 633 159 71 4 0 2 6 1 8 1 4 11 7 5 4 3

0.20 482 121 5 4 31 2 0 1 4 1 1 8 6 4 3 3

0.30 390 9 8 4 4 2 5 1 6 1 1 9 7 5 4 3 2

r = 1 3 0.05 688 173 77 44 28 20 15 11 8 5 4 4

0.10 571 143 64 36 24 17 12 10 6 5 4 3

0.20 437 110 49 28 18 13 10 8 5 4 3 3

0.30 355 8 9 40 23 15 11 8 6 4 3 3 2

T = 2 5 0.05 456 114 52 30 19 13 10 8 5 4 3 3

0.10 382 9 6 4 3 25 16 11 8 7 5 3 3 2

0.20 298 7 5 34 19 12 9 5 4 3

2 5 3 2

2 2

0.30 246 6 2 27 16 10 7 2 2

‘Adapted from table 8.4 of Cohen (1977).

33
Table E-4.-Determination of sample size fmpoint counts with a = 0.05*

Number of

factor levels

4’

B 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.50 0.60 0.70 0.80

r=2 0.05 2600 651 290 163 105 7 3 5 4 4 2 27 1 9 1 4 11

0.10 2102 526 234 132 8 5 5 9 4 4 3 4 2 2 1 6 1 2 9

0.20 1571 393 175 9 9 6 4 4 5 3 3 26 1 7 1 2 9 7

0.30 1235 310 138 7 8 5 0 3 5 2 6 20 1 3 1 0 7 6

r=3 0.05 2060 515 230 130 8 3 5 8 4 3 3 3 2 2 1 5 1 2 9

0.10 1682 421 188 106 6 8 4 8 3 5 27 1 8 13 1 0 8

0.20 1286 322 144 81 5 2 3 6 27 2 1 1 4 1 0 8 6

0.30 1028 258 115 6 5 4 2 2 9 2 2 1 7 1 1 8 6 5

r=4 0.05 1718 430 192 108 7 0 4 9 3 6 2 8 1 8 1 3 1 0 8

0.10 1415 354 158 8 9 5 8 4 0 3 0 2 3 1 5 1 1 8 7

0.20 1096 274 123 6 9 4 5 31 2 3 1 8 1 2 9 7 5

0.30 881 221 9 9 5 6 3 6 2 5 1 9 1 5 1 0 7 6 5

r=5 0.0510 1486231 37209 16638 9 47 8 6 05 g 3 126 2 40 1 6 3 1 0 1 79 ii

0.20 956 240 107 61 3 9 27 20 1 6 1 0 6 5

0.30 776 195 8 7 4 9 3 2 2 2 1 7 1 3 9

z

5 4

r=9 0.05 1012 254 113 6 4 4 1 2 9 2 2 1 7 11 8 6 5

0.10 848 213 9 5 5 4 3 5 2 4 1 8 1 4 9 7 5 4

0.20 669 168 7 5 4 2 27 1 9 1 4 1 1 8 6 4 4

0.30 548 138 61 3 5 2 3 1 6 1 2 9 6 5 4 3

r= 1 3 0.05 796 200 8 9 51 3 3 2 3 1 7 1 3 9 6 5 4

0.10 673 169 7 5 4 3 2 8 20 1 5 1 1 8 6 4

0.20 534 134 6 0 3 4 2 2 1 6 1 2 9 6 5 4 i

0.30 443 111 5 0 2 8 1 8 1 3 1 0 8 5 4 3 3

r = 25 0.0510 457 525 132 115 5 9 1 3 4 2 9 2 21 9 1 5 3 1 01 1 i 56 44 34 z

0.20 0.30 363 303 917 6 4 13 4 2 32 0 1 51 3 1 19 87 65 44 33 23 i

‘Adapted from table 8.4 of Cohen (1977).

3 4

I---.
Table E&-Determination of sample size for point counts with a = 0.01’

Number of

factor levels

4’

B 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.50 0.60 0.70 0.80

r=2 0.05 3564 892 398 224 144 101 7 4 5 7 3 7 26 2 0 1 6

0.10 2978 746 332 188 120 8 4 6 2 4 8 3 1 2 2 1 7 1 3

0.20 2338 586 259 148 9 5 6 7 4 9 3 8 2 5 1 8 1 4 1 1

0.30 1924 482 215 122 7 9 5 5 4 1 3 2 2 1 1 5 1 2 9

r=3 0.05 2756 690 308 174 112 7 8 5 8 4 5 2 9 2 1 1 6 1 2

0.10 2325 582 260 147 9 5 6 6 49 3 8 25 1 8 1 4 11

0.20 1851 464 207 117 7 6 5 3 3 9 3 0 2 0 1 4 1 1 9

0.30 1543 387 173 9 8 6 3 4 4 3 3 2 6 1 7 1 2 1 0 8

r=4 0.05 2270 568 253 143 9 2 6 4 4 8 3 7 24 1 7 13 1 0

0.10 1927 483 215 122 7 8 5 5 4 1 3 1 2 1 1 5 1 1 9

0.20 1548 388 175 9 8 6 3 4 4 3 3 2 5 1 7 :: 9 8

0.30 1299 326 146 8 3 5 3 3 7 2 8 2 2 1 4 8 7

r=5 0.05 1948 488 218 123 7 9 5 5 4 1 3 2 2 1 1 5 1 1 9

0.10 1661 416 186 105 6 8 47 3 5 27 1 8 1 3 1 0 8

0.20 1341 336 150 8 5 5 5 3 8 2 9 22 1 5 1 1 8 7

0.30 1128 283 127 7 2 4 6 3 3 2 4 1 9 1 3 9 7 6

r=9 0.05 1303 324 146 8 3 5 3 3 7 2 8 2 2 1 4 10 8 6

0.10 1122 251 126 71 4 6 3 2 1 9 1 2 9 6

0.20 918 230 103 5 8 38 27

?i

1 5 1 0 8

ii

5

0.30 785 197 8 8 5 0 3 2 2 3 1 7 1 3 9 7 5 4

r= 1 3 0.05 1017 255 114 6 5 4 2 2 9 22 1 7 1 1 8 6 5

0.10 881 221 9 9 5 6 3 6 2 5 1 9 1 5 10 7 6 5

0.20 726 182 8 2 4 6 3 0 2 1 1 6 1 2 8 6 5 4

0.30 623 157 7 0 4 0 2 6 1 8 1 4 1 1 7 5 4 3

r = 2 5 0.05 662 166 : 42 27 1 9 1 4 11 8 6 4 4

0.10 578 145 3 7 24 1 7 1 3 1 0 7 5 4 3

0.20 485 121 5 5 31 20 1 5 1 1 8 6 4 3 3

0.30 417 105 47 27 1 7 1 2 9 7 5 4 3 3

‘Adapted from table 8.4 of Cohen (1977).

Appendix F. Sample vegetation measurements.

I. Level ‘Ike scheme

Habitat variables, incorporated into the Migratory

and Resident Landbird Conservation Strategy of the

Forest Service, Southern Region, National Forests

(USDA FS 1995), wiIl be measured at each of the

point counting stations employed by the National

Forest System in monitoring landbirds in the South.

Nonvegetative variables include:

Elevation (in feet [l ft = 0.3049 m])

Aspect (0 to 360 degrees)

Slope (0 to 90 percent)

Water (present, absent, standing, flowing, fresh,

brackish, salt, within point, nearer than 328 ft [lo0

m] to point)

Variables of the dominant and codominant canopy

include:

1. Forest cover (using the Forest Service’s

Continuous Inventory of Stand Conditions [CISC;

Compartment Prescription Fieldbook (USDA FS

no date)] codes that will describe the makeup of

the major tree species in the overstory).

35
Dominant age (using a dominant tree

characteristic of the point or CISC data for the

stand if these are thought to be a reliable

representation of age at the point.)

Dominant tree height (using a clinometer estimate

of the average height (in feet [l ft = 0.3049 m]) of

the dominant tree canopy for the point).

Total basal area (using a prism to determine total

square feet per acre ([l m2/ha = 4.36 ftz/acre] of

dominant tree species measured from point center;

the basal area will be tallied and recorded

separately according to the categories of conifer

and hardwood).

Percentage of canopy cover (using an ocular

estimate from point center to determine

percentage of cover by the dominant and

codominant canopy according to the following

categories: 0 percent, 1 to 25 percent, 26 to 50

percent, 51 to 75 percent, 76 to 100 percent).

Variables of the midstory layer include:

1. Dominant vegetation (general vegetation

categories based on ocular observations from point

center that include deciduous [i.e., dogwood/

maple, oak/ hickory, gum/poplar, other],

coniferous [i.e., pine, hemlock, cypress, other],

evergreen [i.e., rhododendron, mountain laurel,

titi, bays, other]).

2. Percentage of canopy cover (an ocular estimate

from point center of percentage of cover by the

midstory canopy according to the following

categories: 0 percent, 1 to 25 percent, 26 to 50

percent, 51 to 75 percent, 76 to 100 percent).

Variables of the shrub layer include:

1. Dominant vegetation (general vegetation

categories based on ocular observations from point

center that include deciduous trees, deciduous

shrubs, vines, coniferous [i.e., pine, hemlock,

cypress, other], and evergreen [i.e., rhododendron,

mountain laurel, bays, switchcane, other]).

2. Percentage of canopy cover (an ocular estimate

from point center of percentage of cover by the

shrub layer according to the following categories:

0 percent, 1 to 25 percent, 26 to 50 percent, 51 to 75

percent, 76 to 100 percent).

Variables of the herbaceous layer include:

Dominant vegetation (general vegetation

categories based on ocular observations from point

center that include woody plants, forbs/sedges,

and grasses).

Percentage of canopy cover (an ocular estimate

from point center of percentage of cover by the

herbaceous layer according to the following

categories: 0 percent, 1 to 25 percent, 26 to 50

percent, 51 to 75 percent, 76 to 100 percent).

Groundcover description (an ocular estimate from

point center of the following groundcover

categories: litter, open glade, bare ground, moist,

standing water).

Other key habitat components include:

Standing snags (an ocular estimate from point

center of the following categories: number of snags

greater than 25 cm [12 inches] in d.b.h., number of

snags greater than 50 cm [20 inches] in d.b.h.).

Live cavity trees (an ocular estimate from point

center of the following categories: number of

cavity trees greater than 25 cm [12 inches] in d.b.h.,

number of cavity trees greater than 50 cm [20

inches] in d.b.h.).

II. Level Three scheme

The standard set of measurements follows:

Elevation to the nearest meter (1 m = 3.28 ft),

measured with an altimeter or from a topographic

map

Slope in percent, measured with a clinometer

Aspect of the slope (the compass direction from plot

center downslope) to the nearest degree, measured

with a compass

Presence or absence of significant water within 50 m

(164 ft) of plot center

Canopy closure, in percent, is measured with an

ocular tube, a 3- or 6-cm (1.5- or 2-&h) diameter

cylinder of polyvinylchloride or other material, with

cross-hairs attached to one end. The tube is held

vertically, and the presence or absence of living
vegetation at the cross-hairs recorded; 20 readings are

taken at each point count station, at 2-m (6.5-ft)

intervals along 10-m (33~ft) transects in the cardinal

directions. The number of “vegetation present”

readings multiplied by 5 equals the percentage of

canopy closure.

Groundcover is measured at the same points as

canopy closure. The ocular tube is pointed down in a

randomly selected direction, and the observer’s eyes

are closed. The eyes are then opened and if green

vegetation is present at the cross-hairs, the point is

counted as “groundcover present.” The number of

“groundcover present” readings multiplied by 5 is

the estimate of the percentage of groundcover. The

three most common plant species in the groundcover

layer are recorded.

Shrub cover: If the station at which canopy closure is

measured is in a shrub, a positive reading of “shrub

present” occurs. The number of “shrub present”

readings multiplied by 5 is the estimate of the

percentage of shrub cover. Shrub density per hectare

can be estimated as the number of shrubs within a

circular plot centered on the point with a radius of

5.64 m (18.5 ft) multiplied by 100. Density of the three

most numerous shrub species is also recorded.

Species composition, basal area, stem density, and

canopy height of the overstory are measured in the

following way: From the center point counting

station, use an angle gauge or prism to determine the

sample of trees 10 cm (4 inches) in d.b.h. or larger.

Use a basal-area factor prism or gauge that includes

approximately 10 trees in the sample (e.g., 5x or 7x

m2/ha [20x or 30x P/acre] in dense forests, 2.5x or

1.3x m2/ha [10x or 5x #/acre] in sparse forests).

Measure and record the species and diameter of each

of the trees included in the prism sample. Basal area

for each species is the number of stems of that species

counted, multiplied by the basal-area factor. Stem

density is a function calculated from the diameter

and basal-area factor for each tree (stem density =

basal area factor/basal area of the tree, in the

appropriate units; thus, if basal area is in m2, the

basal area of the tree is: (d.b.h. in cm/200)2

multiplied by sd (for basal area in ft2, the basal area of

the tree is: [d.b.h. in inches/2412 multiplied by n).

Stem density for the species is the sum of the stem

densities for each of the trees of that species. Dead

trees are recorded in the same way as live ones, but

assigned to the species “SNAG.” Canopy height is

measured with a clinometer and is the average of the

heights of the four tallest trees in the prism sample.

Additional measurements that have been found

useful:

Vegetation profile (or midstory density)-

Measure with a 0.5- by 0.5-m (20- by 20-inch)

density board g-ridded into a checkerboard of 25

equal squares, read from the center of the point

count station, and set up at a distance of 10 m (33

ft) in each of the cardinal directions. The board is

read at 0,2.5, and 5 m (0,8.2, and 16.4 ft). The

number of squares more than 50 percent obscured

by vegetation is the measure of vegetation density

When all four measures in each direction are

summed, a percentage of cover in that layer is

obtained. As a practical matter in the field, the

number of open squares is usually tallied, and that

number is subtracted from 25 to determine the

number of squares that are obscured by vegetation.

Percentage of cover of vines in the canopy and of

Spanish mossMeasure in the same way as canopy closure, except

that presence of these features anywhere in the

view of the tube, rather than only at the cross-hairs,

counts as a “present.” Because the entire field of

the ocular tube is examined, standardization of the

diameter of the tube at 3 to 5 cm (1.5 to 2 inches) is

necessary to ensure comparability of data among

observers.

Presence or absence of snags and waterMeasure in two bands, representing the 25-m and

25- to 50-m (82~ft and 82- to 164-ft) bands around

the counting station. If water (e.g., creek, pond,

temporary pool) is present in either of these bands,

it is recorded as “present.” If standing dead trees

are present in either of these bands, then that fact is

recorded as well. Note that the presence of snags

may also be recorded by the canopy tree sample.

37
APPENDIX G. List and descriptions of computer files and suggested coding standards for Level

Three vegetation variables. List follows a general sequence from more general to

more specific data items.

Table G-L-File One: Description of suggested coding standards for level three vegetation variables

associated with point counts of birds.

Variable Description of variable

STATE

REGION

STATION

POINT NUMBER

YEAR

MONTH

DAY

OBSERVER

GEO-CODING SYSTEM

N-S COORDINATES

E-W COORDINATES

ELEVATION

ASPECT

SLOPE

WATER within 25 m

WATER within 25 to 50 m

TREE LAYER*

COVER

BASAL AREA

TOTAL SNAGS WITHIN

25m

TOTAL SNAGS WITHIN

25 to 50 m

srsx

SPSx-HT

SPSX-#

SPSx-BA

SHRUB LAYER

COVER

38

Two-letter State code (U.S. Postal Service code).

A description of the region or geographic locality where points are conducted.

Regions could be such entities as national forests, national parks, river basins,

counties, etc.

Smaller area within the region. Stations could be forest compartments, study

areas, townships, or any subdivision of the regional description. Stations should

be small enough to allow for no more than 999 point count locations.

Number assigned to a point count location within the station (between 1 and 999).

Year (4 digits).

Month (01 to 12).

Day (01 to 31).

Any observer identification unique within the station, generally observer’s

initials.

Enter 0 if geo-coordinates are recorded in latitude and longitude; enter UTM

zone if UTM coordinates are being used.

If data are in UTM coordinates, enter the Northing (7 digits); if latitude is used,

enter the degrees, minutes, seconds as (DDMMSS) for DD”MM’SS”.

If data are in UTM coordinates, enter the Easting (6 digits); if longitude is used,

enter the degrees, minutes, seconds as (DDDMMSS) for DDD”MM’SS”.

Elevation in meters (1 m = 3.28 ft).

Aspect (O” to 36OO).

Percent slope (0 to 99).

Presence or absence (+ /-) of significant water within 25 m (82 ft).

Presence or absence (t /-) of significant water within 25 to 50 m (82 to 164 ft).

Canopy layer of vegetation; generally trees >lO cm (4 inches) in d.b.h.

Percentage of plot occupied by tree layer (i.e., canopy cover) (0 to 99).

Total basal area (m2) per hectare (1 m2/ha = 4.36 ft2/acre) of all trees (trees > 10

cm [4 inches] in d.b.h.).

Total number of dead trees (> 10 cm [4 inches] in d.b.h.) within 25 m (82 ft) of

counting station,

Total number of dead trees (> 10 cm [4 inches] in d.b.h.) within 25 to 50 m (82 to

164 ft) of counting station.

Four-letter code for tree species “x”, where x = 1,2, . . . in order of importance value.

Average height (m [l m = 3.28 ft]) of tree species “x” (canopy height).

Number of trees per hectare (1 tree/ha = 2.47 trees/acre) of tree species “x”

(trees > 10 cm [4 inches] in d.b.h.).

Basal area (m2) per hectare (1 m2/ha = 4.36 ft2/acre) of tree species “x” (trees >

10 cm [4 inches] in d.b.h.).

Shrubs and small trees; generally between 0.5 and 2.5 m (1.5 and 8 ft).

Percentage of plot occupied by all shrubs (shrubs and trees c 10 cm [4 inches] in

d.b.h.).
Variable Description of variable

DENSITY Total number of shrubs measured on a O.Ol-ha (0.04-acre) circular plot centered

on the counting station.

SPSX

SPSX-I-IT

SPSX-#

Four-letter code for shrub species “x”, where x = 1,2, . . ., in decreasing order of

percentage of cover.

Average height (dm) of shrub species “x” (e.g., 12 = 1.2 m; 1 dm = 4 inches)

Number of indviduals of shrub species “x”.

HERB LAYER

COVER

SPSX

Ground-story vegetation; generally c 0.5 m (20 inches).

Percentage of plot occupied by herbaceous cover.

Four-letter code for herb species “x,” where x = 1,2,3 for first, second, third

most abundant herb species.

VEGETATION PROFILE Vegetation coverage 0 to 5 m (0 to 16 A)

COVER at 0 m Percentage of cover by vegetation at 0 m (0 ft), measured with density board.

COVER at 2.5 m Percentage of cover by vegetation at 2.5 m (8 ft), measured with density board.

COVER at 5 m Percentage of cover by vegetation at 5 m (16 ft), measured with density board.

COVER OF VINES IN

CANOPY Percentage of cover by vines in the canopy measured with ocular tube.

COVER OF SPANISH

MOSS IN CANOPY Percentage of cover by Spanish moss in the canopy, measured with ocular tube.

SUBMITTER Five-letter code to identify person or group submitting data (required to relate

species codes to region if multiple groups are monitoring within same region).

NOTES Comments and notes.

‘Basal area and number of stems are all standardized on a per-hectare basis. This method allows for the use of sampling plots of varying

size and also for plotless sampling methods.

Table G-IL.-File l’kuo: Description of suggested mechanism for standardizing computer coding of

species nomenclature.

Variable* Description of variable

Species Code

Species Name

Four-Letter Codes Used for Trees, Shrubs, and Herbs.

Scientific name of species

*The species codes and species names are required because no universally accepted standard species codes are available for plants. All data

should entered using the four-letter codes suggested by Ralph and others (1993); i.e., the first two letters of the genus name and the first

two letters of the species epithet. For example, American beech (FugusgrandijoZia)would be coded as FAGR. Standardization using this

system will minimize (although not eliminate) species identification conflicts. Nevertheless, the use of alternate nomenclature is

accommodated by the use of this identification file. Ideally, this file need only be submitted with the initial submission of data from a

region and updated (verified by year) periodically as additional species are added.

39



---_-me------ -

State Region EaTTon F&E f

----

Esit t Year SXii Sy- T&T --

--- -- ---

Observer Temp Oc Wxd SFy Hxitat Forest type Sxge

-- --__---_------

Zone N-S coordinates E-W coordinates

Geographic coding: Zone-O for lat-long, else Ul'% Zone

0

08 I

Flyovers: Remarks

WFNG SYMBOLS

s cmw 0

Po%itiOll of

,

singing male

S

----.-.

Assumed change

‘X in position

Male

observed

Pair together

assumed mated

0 fxnw Observed. sex

unknown
------

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_---

Xsit I Year

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State Region station Month Day xlne

----- -_- -------

Observer Temp Oc ad Syy Hxitat Forest type S%ge Zone -- N-S ---7--- coordinates E-W coordinates

Geographic coding: Zone-O for lat-long, else VI?4 Zone

Species

Alpha

Code

Count

< 25 m 25-50 m

O-3 min 4-5 min 6-10 min O-3 min 4-5 min 6-10 min

> 50 m Flyovers

O-3 min 4-5 min 6-10 min O-3 min 4-5 min 6-10 min

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Submitter Remarks
Hamel, Paul B.; Smith, Winston Paul; ‘Iwedt, Daniel J. [and others].

1996. A land manager’s guide to point counts of birds in the

Southeast. Gen. Tech. Rep. SO-120. New Orleans, LA: U.S.

Department of Agriculture, Forest Service, Southern Research

Station. 39 p.

A strategy for monitoring birds using point counts is outlined and

developed. Techniques for conducting point counts, recording

data, and computer coding are presented. Means for determining

sample sizes of point counts to answer specific questions are

explained. Blank data forms are included. A standard protocol for

point counts of birds in the Southeast is suggested.

Keywords: Bird sampling bird sampling data forms, inventory

land management, monitoring, Partners in Flight, sample size

determination, Southeastern United States, vegetation sampling.
The Forest Service, U.S. Department of

Agriculture, is dedicated to the principle of

multiple use management of the Nation’s forest resources for

sustained yields of wood, water, forage, wildlife, and

recreation. Through forestry research, cooperation with the

States and private forest owners, and management of the

National Forests and National Grasslands, it strives-as

directed by Congress-to provide increasingly greater service

to a growing Nation.

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beliefs, and marital or familial status. (Not all prohibited

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require alternative means for communication of program

information (braille, large print, audiotape, etc.) should

contact the USDA Office of Communications at (202) 720-

588 1. To file a complaint, write the Secretary of Agriculture,

U.S. Department of Agriculture, Washington, DC 20250, or

call (202) 720-7327. USDA is an equal employment

opportunity employer.