United States

Department 01

Agriculture

t ‘j’

Point Counts Of’ Birds in BottomIan:

Forest Service

Hal.dwood Fotests of the Mississippi

Southern Forest

Experiment Stetion

Alluvial Valley: Duration, Minimum

Sample Se, and Points VerFus Visits

New Orleans,

Louisiana

Winston Paul Smith, Daniel J. Twedt, David A. Wiedenfeld,

Paul 8. Hamel, Robert P. Ford, and Robert J. Cooper

,

l .

1

c

Research Paper

SO-274

May 1993

. .
SUMMARY

To compare efficacy of point count sampling in bottomland hardwood forests, duration of

point count, number of point counts, number of visits to each point during a breeding season,

and minimum sample size were examined. Minimum sample sizes were computed from the

variation recorded during 82 point counts from 3 selected localities containing 3 habitat

types (wet, mesic, and dry) across 3 regions of the Mississippi Alluvial Valley (northern,

central, and southern). For each of these point counts, all birds seen or heard during the

initial 3 minutes and during each minute thereafter up to 10 minutes within three concentric distance categories (~26 m, 25 to 50 m, and >50 m> were recorded. In a second study,

the effect of increasing the number of points or visits was determined by comparing the

results of 150 4-minute point counts obtained from each of four stands on Delta Experimental Forest. Within each stand, bootstrap estimates of the mean cumulative number of species

each year were obtained from all possible combinations of six points and six visits. Similar

analyses of 384 counts obtained from 132 points distributed among 56 sites in west Tennessee bottomland forests were undertaken. Mean number of species recorded during 5- and

IO-minute counts were 10.3 and 12.9 and 11.3 and 14.7 for the lower MAV and west Tennessee, respectively. There was significant variation in numbers of birds and species between

regions and localities nested within regions; neither habitat nor the interaction between

region and habitat was significant. Sample size sufficient to detect actual differences of some’

species (e.g., wood thrush LHylocichla mustelind was >500; for other species (e.g.,

prothonotary warbler Protonotaria citrean, this same level of precision could be achieved

with <lo counts. Significant differences in mean cumulative species were detected among

the number of points visited and among the number of visits to a point. Although no interaction was detected between number of points and number of visits, when paired reciprocals

were compared, more points invariably yielded significantly greater cumulative number of

species than more visits to a point in the lower MAV. In west Tennessee, more points yielded

either similar or significantly more cumulative species than more visits.

ACJ3NOWLEDGMENTS

Joseph H. McGuiness and Tracy D. McCarthey provided valuable assistance

with point counts of songbirds on Delta Experimental Forest. Richard Cody

contributed valuable field assistance during point count censuses throughout

the lower Mississippi Alluvial Valley. Assistance was also provided by Mark

Swan of the Nature Conservancy of Louisiana. Funding for this.work was

provided by the U.S. Department of Agriculture, Forest Service, Southern

Forest Experiment Station, Southern Hardwoods Laboratory, Stoneville, MS;

and U.S. Department of the Interior, Fish and Wildlife Service, National Wetlands Research Center, Vicksburg, MS.
Point Counts of Birds in Bottomland Hardwood Forests of

the Mississippi Alluvial Valley: Duration, Minimum Sample

Size, and Points Versus Visits

Winston Paul Smith, Daniel J. Twedt, David A. Wjedenfeld, Paul B. Hamel,

Robert P. Ford, and Robert J. Cooper

- I

1 INTRODUCTION

Recently there has been much interest regarding

apparent declines of bird species that were historically resident or breeding in bottomland hardwood

forests of the lower Mississippi River Valley. Previous

investigators, notably Burdick and others (1989) and

Wiedenfeld and others,’ demonstrated appreciable

and consistent declines of many species within the

Mississippi Alluvial Valley (MAV). In particular,

numerous neotropical migrant bird (NTMB) species

have experienced significant declines over the last two

decades, many of which appeared to increase in other

portions of their historical ranges.1 As a result of this

concern, university scientists, personnel of Federal

and State natural resource agencies and nongovernmental organizations, and public and private

land managers are initiating research or other monitoring programs with the specific goal of quantitatively describing NTMB species distributions and

relative abundance among the remaining forest

fragments.

\

Despite the extensive literature on estimating numbers of terrestrial birds (e.g., Scott and Ralph 1981),

general agreement over a standardized protocol for

monitoring neotropical migrant birds has not been

achieved. The Monitoring Working Group of the

Neotropical Migrant Bird Conservation Program has

recently released a recommended protocol for sampling bird populations (Ralph and others, in press).

‘Wiedenfeld, David A.; Messick, Lyla A.; James, Frances C. 1992.

Population trends in 65 species of North American birds 1965-

1992. 200 p. Unpublished report to National Fish and Wildlife

Foundation, U.S. Department of the Interior Fish and Wildlife

Scrvrce, and U.S. Department of Agriculture, Forest Scrvicc. On

file with: National Wildlife Foundation, 1400 Sixteenth Street

NW, Washington, DC 20036-2266.

The intent was to provide guidelines that woulc

facilitate obtaining bird population data in a stand

ardized and consistent fashion across North America

However, habitat features vary substantially with&

and among forest types. Moreover, research needs o

management goals vary within, as well as across

regions, and a single protocol for all situations is ur

wieldy. It is important, nonetheless, that research a

other monitoring programs use a sampling desig

that is fundamentally compatible with the recorr

mended protocol of the Monitoring Working Group s

that data are comparable. That is, additional dat

may be collected to meet specific research goals, bu

where possible, the basic information prescribed b

the Monitoring Working Group should be include

within the framework of all future investigations.

The purpose of this research was to evaluate the e

ficacy of point count surveys in bottomland hardwoc

forests. Specific objectives were to determine: (:

whether the recommended. distance measures fc

point count circular plots are useful in bottomlar

hardwood forests, (2) the optimum duration for eat

point count, (3) the optimum number of points

sample at each locality, (4) the optimum number

counts at each point during a season, and (5) the mi:

imum sample size to accommodate the variation

bird species distribution and relative abundan

throughout the lower MAV.

METHODS

Study Areas

For this paper, data from three similar studies we

compiled, the most recent of which was designed

address questions related to estimating duration a!

Winston Paul Smith is a research wildlife biologist at the Southern Hardwoods Laboratory, U.S. Dopartmont of Agriculture, For

Service, Southern Forest Experiment Station, Stoncvillc, MS 38776; Danicl J. Twcdt is station lcadcr at Vicksburg Field Rascal

Station, National Wetlands Research Center, U.S. Department of the Interior, Fish and Wildlife Scrvicc, Vicksburg, MS :39160; Da

A. Wiedenfeld is a research associate at the Museum of Natural Scicncc, Louisiana State University, Baton Rouge, LA 7f~fj()3; Paul

Hamel is a zoologist, Ecological Services Division, Tcnnrsscc Dcpartmcnt of Environment and Conservation, Nashville, TN :]724.‘&()4

Robert P. Ford is Biodiversity Project Coordinator, Trnncsscc Conservation Loague, Nashville, TN 37209-3200; and Robert J. (:,ropc8t

an assistant professor, Department of Biology, Memphis State University, Memphis, TN 36152.
sample size of point counts sufficient to detect meaningful biological differences at different spatial scales

and levels of resolution. To estimate variability

throughout the lower MAV, a balanced study design

that included three point counts at each of three

localities within each of three habitats (wet, mesic,

and dry) was developed. This sampling design was

repeated in each of 3 regions (southern, central, and

northern) of the lower MAV (i.e., 3 x 3 x 3 x 3) for a

total of 81 point counts (fig. 1). Wet habitat sites were

characterized by cypress (Taxodium spp.) or tupelo

(Nyssa spp.). Mesic habitat sites were seasonally

flooded, lowland flatwoods, and dry habitat sites were

ridges or rarely inundated bottomland forests. Forest

tracts on public lands were selected primarily because

of dependable access. Each locality was 240 ha to accommodate three points that were at least 250 m

apart (Ralph and others, in press) and 2200 m from

the forest edge.

In addition, point counts from a 3-year study (1985-

87) in west Tennessee boitomlands, i.e., from the Tennessee River west to the Mississippi River, were included (Durham and others 1988, Ford 1990). Sites

were randomly chosen, but stratified among the seven

major drainages in west Tennessee: Wolf, Loosahatchie, Hatchie, Forked Deer, Obion, Tennessee, and

Mississippi Rivers. Each drainage was allotted a minimum of 10 percent of the sites. Within the Tennessee

River drainage, three of the six major tributaries were

randomly selected: White Oak Creek, Birdsong Creek,

and Big Sandy River. Each site was 240 ha of relatively homogeneous bottomland hardwood forest. Ford

(1990) provides a detailed description of study sites

and selection procedures.

Delta Experimental Forest (DEF) (fig. 11, Stoneville, MS, was the site of a 2-year study (1991-92) examining the influence of forest management on breeding bird density and diversity,2 DEF encompasses

about 1,050 ha and represents one of the few remaining large (2500 ha), contiguous, bottomland forests

within a loo-km radius of Stoneville. Delta Experimental Forest was initially logged during the

early 20th century and selectively logged until 1939

(Delta Council 1945).

‘Smith, Winston Paul. 1991. Wildlife use of bottomland

hardwoods 50 years after timber stand improvement or clearcutting: I. Density and species composition of the breeding and

.wintering avifauna. Study Plan No. FS-SO-4101-91. On file with:

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

Experiment Station, Stoneville, MS.

Point Count Protocol

With few exceptions, general guidelines and procc

dures for point count censusing of birds (Ralph an

others, in press) were followed. For each of th

separate studies, however, a brief description of spf

cific features that were fundamental departures fror

their recommended protocol was provided. The poir

counts within the lower MAV was of lo-minute durz

tion and occurred during the first 4 hours after daw

Figure l.--location ofpoint count sites within the southern, centrc

and northern regions of the lower Mississippi A&vi

Valley (enclosed area) and west Tennessee bottomlan8

fg,h,i): southern- a. Lake Fausse Pointe State Park,

Sherburne wildlife Management Area (two localitie

central- c. Tensas River National Wildlife Refu

(NWR), d. Panther Swamp NWR, e. Yazoo NWR; nort

ern- f: Delta Experimental Forest, Stoneville, M

g. Meeman-Shelby Forest, State Park and Wildl,

Management Area, h. Lower Hatchie NWR, i. Chic

asaw NWR. Not shown: portions of Mississippi River i

luvial Plain extending upstream along later

trihutari.es.

2
(i.e., before 10 a.m. CDT). Each point was visited once

during 7 through 16 May 1992. An assistant estimated distance to each bird according to predefined

landmarks and recorded data. Before each count

began, distance to selected landmarks was estimated

with a rangefinder (Ranging Optimeter 620, Ranging,

Inc., East Bloomfield, NY). Landmarks were used to

assign birds seen or heard to one of three concentric

distance bands: ~25 m, 25 to 50 m, or >50 m. When

necessary, the rangefinder was used to verify distance. Only birds that were observed using the forest

patch were included in the sample. This excluded

several species that typically flew over forest patches,

such as swifts, swallows, herons, and egrets.

Within the west Tennessee bottomlands, point

counts were obtained from 132 points distributed

among 56 forest tracts. In 1985 and 1986, point counts

were conducted during an 8-week period, which began

in early May and continued into early July; in 1987,

point counts occurred during a 4-week period beginning in late May. Each point was visited from one to

four times in 2 of the 3 years; no point was visited

more than twice each year. A point count consisted of

recording the number of all birds seen or heard (including birds that flew over the point) during each of

four, consecutive B-minute periods. Censuses usually

were completed during the 4-hour period following

sunrise (Durham and others 1988, Ford 1990).

On DEF, 25 points were established within each of

4 stands, 2 silvicultural treatments, and a paired control for each treatment. One treatment was a 1937

clearcut that naturally regenerated; the second underwent timber stand improvement cuts in 1937.

Each control had not been managed since the last

high-grade harvest in the mid-1930’s.

Within each stand, each point was systematically

sampled five to seven times during the 3-hour period

following sunrise from 8 to 21 May 1991, and from 30

May to 12 June 1992. A sampling schedule was implemented whereby each point within a stand was visited

on separate days at a different time on each of the

subsequent visits. Each census consisted of recording

all birds seen or heard per minute for a total of 4

minutes.

Common names follow the sixth edition of the

checklist of North American birds (American

Ornithologists’ Union 1983).

Data Analyses

Point Count Duration.-For both the lower MAV

and west Tennessee bottomlands, mean cumulative

number of individuals and mean cumulative number

of species were plotted against point count duration to

examine whether total individuals or species approached an asymptote during the sampling period.

In addition, general regression (REG Procedure; SAS

Institute, Inc. 1988, p. 773) was used to derive linear

regression models of number of new individuals or

number of new species encountered within each time

interval, as a function of duration of point count for

1985,1986, and 1987 censuses in west Tennessee. An

analysis of covariance (Zar 1984, p. 300) was used to

determine whether the slopes of corresponding linear

models (i.e., individuals or species) differed among

years. For this paper, efficiency was quantified as

number of new birds or species per unit effort.

Point Count Variation and Minimum Sample

Size.-A Chi-square test of independence was used to

examine whether the number of birds recorded in

each distance category was independent of habitat

(i.e., 3 x 3 contingency table); subdividing Chi-square

analysis (Zar 1984, p. 69) determined where significant departures occurred. For west Tennessee censuses, three-dimensional contingency table analyses

determined whether frequency distribution of detected birds among distance categories differed among

years or between lo- and 20-minute point counts (Zar,

1984, p. 72). Analysis of variance (ANOVA) (GLM Procedure; SAS Institute, Inc. 1988, p. 549) was used to

determine whether significant variation in number of

individuals or species counts occurred among regions,

habitats, or localities (nested within region), or

through interaction of region and habitat.

Calculation of minimum sample size followed Neter

and Wasserman (1974, p. 492) for a specified a, 8,

number of factors and 0. Specifying 0, the noncentrality parameter, requires determining how much

factor level means (e.g., regions) must differ to represent a statistical difference (Neter and Wasserman

1974). For this paper, three different specifications

were selected for 0. The first reflected the observed

variation of variables among each of the main effects,

i.e., region, habitat, and locality. Here, the range of

mean values observed for a dependent variable relative to each effect (e.g., mean number of species in

each of the regions, or mean number of red-eyed

vireos [Vireo olivaceus) among habitats) was used to

calculate e. The other two specifications were arbitrary, but represent extremes with respect to resolution. Sample sizes for a difference of +0.25 are those

that would be required to detect statistical significance if the greatest difference among factor levels

was 0.25 birds or 0.25 species. These estimates could

be used for investigations focusing on endangered

species or threatened communities requiring monitoring protocols sensitive to relatively small changes.

Our final g value was based on a precision of + 25 percent of the mean; this corresponds logically with 0.25

birds or species but represents a coarser filter for investigating differences in species distribution and

.abundance. Large-scale monitoring programs would

perhaps use this level of precision as an indication of

local or regional changes.

3
Comparison of Duration, Number of Points, and

Number of Visits.-A matrix of mean cumulative

number of species for censuses with all possible combinations of six points and six visits was generated

using the bootstrap procedure (Efron 1982). Within

each stand, observations for each combination (e.g., 2

visits to each of 4 points) were obtained by randomly

sampling the “population” of point counts (e.g., 150

counts = 6 visits to 25 points) recorded each year. For

each randomly selected point count, location was constrained, whereas successive visits were randomly

selected. Each mean value was computed from 250

resampling iterations and represented an independent observation of a point times visit combination

within the selected stand.

A similar procedure involving 50 resampling iterations of a bootstrapping procedure was used to

generate an array of mean number of cumulative

species for all possible combinations of one to four

visits of varying duration (5 to 20 minutes) to each of

three points from all suitable point counts (1985-87)

in west Tennessee. As before, cumulative number of

species recorded between counts that represented

varying number of points and visits to each point were

compared. In addition, mean cumulative number of

species were compared for combinations of censuses

that represented equivalent effort but could be applied as either a single count, or recorded among multiple points or over multiple visits. For example, a 20-

minute census could be conducted as four consecutive

5-minute counts at the same point, or as four separate

5-minute counts conducted at a single point; as two

separate lo-minute counts at a single point; or as

single lo-minute counts at two separate points.

Analysis of variance (GLM Procedure; SAS Institute, Inc. 1988, p, 549) was conducted to determine

whether significant variation in cumulative number’

of species occurred as a function of number of points,

number of visits, or an interaction of points and visits.

For west Tennessee censuses, the General Linear

Model (GLM) procedure included an analysis of point

count duration and its interaction with number of

points or number of visits for counts of cumulative

duration of 20 minutes. Scheffe’s multiple comparison

procedure was performed to determine which main effect means differed. An a priori simultaneous comparison was conducted using a contrast statement

within the ANOVA (SAS Institute, Inc. 1988, p. 560)

to compare the 15 possible reciprocal combinations of

points and visits that were conducted on DEF.

RESULTS AND DISCUSSION

Although the proposed experimental design for the

lower MAV study provided for a balanced design of 81

point counts (3 regions x 3 habitats x 3 localities x 3

4

counts), all 3 types of habitat were not found in all

localities. The dry habitat was not identified at either

Lake Fausse Pointe State Park or Area 2 in Sherburne Wildlife Management Area, and wet habitat

was not located within Tensas River National Wildlife

Refuge. During the period 7 May through 16 May

1992, 82 lo-minute point counts were completed

(table 1).

Within the lower MAV, 1,621 individual birds distributed among 52 species were recorded. The majority of individuals (872) were represented by 8

species. The maximum number of individuals recorded during any single lo-minute count was 32,

whereas the minimum was 11; the average was 19.8

birds per count (table 2). The number of species

recorded varied from 9 to 18 with an average of 12.9

species per count (table 2).

In west Tennessee bottomlands, 195, 144, and 45

point counts were conducted in 1985, 1986, and 1987,

respectively. Overall, 12,471 individuals and 79

species were recorded. The number of individuals

recorded during any 20-minute census varied from 11

to 74 and averaged 32.48 (table 2). The number of

species recorded varied from 7 to 38 with an average

of 19.09 species per census (table 2).

Table l.-Locations and number of point counts for bird censuses

conducted within the Lower Mississippi Alluvial Valley, 7

through 16 May 1992

Site

Type of habitat

Wet Mesic Dry

__________.__.Number _.._.._.__.______

Southern region

Lake Fausse Pointe State

Park, Louisiana 4 4 0

Sherburne Wildlife Management

Area, Louisiana, Area 1 5 1 3

Sherburne Wildlife Management

Area, Louisiana, Area 2 5 4 0

Central region

Tensas River National Wildlife

Refuge, Louisiana 0 4 5

Yazoo National Wildlife

Refuge, Mississippi 2 3 3

Panther Swamp National

Wildlife Refuge, Mississippi 5 2 4

Northern region

Meeman-Shelby State Forest,

Tennessee 3 3 3

Chickasaw National Wildlife

Refuge, Tennessee 3 3 3

Lower Hatchie National Wildlife

Refuge, Tennessee 4 4 2

Total 31 28 23
Table Z.-Mean standard error (SE), and minimum and maximum numbers (range) of individual birds and species recorded during point

counts of 5-, IO-, 15. and 20-minute duration in each year and overall from 1985 to 1987 in west Tennessee bottomlands and during

point counts of 5- and lo-minute duration from 1991 to 1992 throughout the lower Mississippi Alluvial Valley

Location/year Duration of point count

5 minutes 10 minutes 1 5 minutes 20 minutes

Mean SE Range Mean SE Range Mean SE Range Mean SE Range

_..-_._ .___ ___ __.._.._.._..... __._ --_.... _ __..._..___.. _ -.-.-.--.- --.-Numberofindividuals ___._ _ --___.___ _ .________________ _ ________ _ ______________

West Tennessee bottomlands

1985 12.9 0.281 5-31 18.7 0.332 8-34 23.4 0.382 9-50 28.7 0.412 17-74

1986 17.2 0.440 O-30 24.9 0.588 15-42 30.3 0.684 I-50 36.4 0.787 23-57

1987 19.2 0.647 7-29 26.8 0.831 9-36 32.2 1.017 11-48 36.1 1.136 11-52

1985-87 15.3 0.262 o-31 22.0 0.340 8-42 27.0 0.391 l-50 32.5 0.431 11-74

Lower Mississippi Alluvial Valley

1991-92 15.0 0.041 7-27 19.8 0.444 11-32 * * * * 3 *

___._._. _ __..____ ___.____ ._--.-.-. _ _-._____ __ _____.__---..----- _- .--. --Numberofspecies---- ------- ---..__-.-_--_-_ .____.__.________ _ ___________ _ ______

West Tennessee bottomlands

1985 10.1 0.179 3-17 13.4 0.203 5-20 15.9 0.213 6-23 18.2 0.214 7-38

1986 12.7 0.245 O-20 16.2 0.327 lo-24 18.1 0.374 l-29 20.3 0.422 13-31

1987 12.5 0.409 6-17 15.8 0.482 7-21 17.9 0.481 8-25 19.1 0.482 8-25

1985-87 11.3 0.152 O-20 14.7 0.182 5-24 17.0 0.193 l-29 19.1 0.206 7-38

Lower Mississippi Alluvial Valley

1991-92 10.3 0.025 6-16 12.9 0.257 9-18 * * * * * *

*No data available.

Duration of Point Counts

One of our stated objectives was to determine the

most efficient point count interval, which has been

variously recommended as 3 minutes (e.g., U.S. Department of the Interior 19901, 5 minutes,3 4 or 8

minutes (Anderson and Ohmart 1981), or even 20

minutes (Blonde1 and others 1970). Cumulative number of new individuals and of new species encountered

per minute in the lower MAV were plotted (fig. 2).

Both the number of new individuals and new species

continued to increase throughout the lo-minute census (fig. 2). In west Tennessee bottomlands, neither

the cumulative number of birds (fig. 3a) nor species

(fig. 3b) approached an asymptote even after 20 minutes. Moreover, mean cumulative number of species

differed among censuses of varying lengths (F = 322.7,

df = 3, P <O.OOl); cumulative number of species increased with each additional 5 minutes of sampling (F

“Ralph, C. John; Droege, Sam: Sauer, John R. 1992. Managing

and monitoring birds using point counts: standards and applications. Unpublished report of the Monitoring Working Group,

Neotropical Migrant Bird Conservation Program. 12 p. On file

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

Southwest Forest and Range Experiment Station, RWU-4251,

Redwood Sciences Laboratory, Arcata, CA 95521.

23.80, df = 945, P 50.01). However, the relationship

between numbers of new birds or species relative to

sampling effort appeared similar across years. The

slopes of the regressions of number of new species as a

2 5

- INDIVIDUALS

--- SPECIES

I ! I I I I I I I

I 2 3 4 5 6 7 6 9

MINUTES

Figure P.-Cumulative number of new individual birds and

cumulative number of new bird species recorded per

minute during 82 -IO-minute point counts conducted

throughout the lower Mississippi Alluvial Valley, 7 May

through 16 May 1992.

5
.”

35 -

--- 1985 BIRDS

- 1986 BIRDS

--- 1987 BIRDS

- 0 5 IO I5 20

MINUTES

Figure 3.-Cumulative number of breeding birds (a) and cumulative number of bird species (bl recorded per 5-minute interval during 20-minute point counts conducted among

seven major drainages of west Tennessee in 1985, 1986,

and 1987.

function of elapsed time did not differ among years

(F = 0.836; df = 2,6; P >0.5). This was true also for

number of new birds (F = 0.520; df = 2,6; P >0.5). In

addition, plots of cumulative number of individuals

and species from the lower MAV appeared to behave

in a similar fashion to their west Tennessee counterparts (fig. 4).

Apparently, longer censuses would be required to

count all (or even most) of the individuals or species in

bottomland hardwood forests. Extending counting

periods, however, increases the likelihood of repeated

counts of the same individual because birds move or

because the observer forgets where birds were initially recorded (Scott and Ramsey 1981). This duplication

would only influence density estimates, however, because new species are necessarily recognized as unique during each point count. Still, point counts of

longer duration may not be the most efficient means

of sampling avifauna. The results of our study indicate that point counts of much longer duration than

20 minutes may be necessary to even approach the

total number of species. Although prolonged censuses

35, I

I

---- ------- -IN81105 YAVBIRO’ TNIAIA /H .H’ ---YlVTAXI /’/..////M

1

/’/’ __----____-----___--- ___--- # ’ ,*’-->--0-->- .A.’ ,‘. .’ 0

0 5 IO

MINUTES

Figure 4.-Overall average (1985-871 of cumulative number o/

breeding birds (TNBIRDS) and cumulative number 01

bird species (TNTAXAI recorded in west Tennessee bottomlands and cumulative number of all birds (MAVBIRDS) and bird species (MAVTAX4) including migrating birds recorded in the lower Mississippi Alluvial

Valley, 7 May through 16 May 1992.

may be a viable option for researchers, constraints of

personnel, time, and other responsibilities preclude

public or private land managers from implementing a

point count protocol that requires excessively long

counts at one point, particularly in light of site and

regional variability (see Minimum Sample Size) in

bottomland hardwood forests.

An alternative approach might be to determine an

acceptable percentage of the available avifauna to be

included in a sample and to determine the point count

interval that consistently includes this proportion.

For example, 80 percent of the species (84511045) and

about 75 percent of the individuals (1,226/1,621) encountered during an entire lo-minute point count in

the lower MAV were recorded within the initial 5-

minute period. Corresponding percentages for west

Tennessee (i.e., total species and total birds recorded

within the initial 5-minute interval relative to a lominute interval) were similar (77 percent and 70 percent, respectively) to the lower MAV. However, when

one compares the number of birds and species

recorded in a 5-minute census to the totals obtained

from 20-minute point counts, the percentages are appreciably lower (59 percent and 47 percent, respectively). Thus, to use this approach one needs to conduct point counts of sufficient duration to observe or

predict a marked downward inflection in the curve of

birds or species recorded per unit effort. To obtain

“asymptotic estimates” may require preliminary censuses that approach or even exceed 40 minutes. However, once the relationship between acceptable proportion of total individuals or species and point count

duration has been derived, our data suggest that expected percentages within samples of specified dura6
Table 3.-Mean cumulative number of individual birds and species predicted from

power function regression equations for lower Mississippi Alluvial Valley fMAV) (birds = 7.098 x /minutes1°.450s, R2 = 0.995: species = 5.867 x

(minutes/0.3460, R2 = 0.9981, and west Tennessee bottomlands (birds =

6.53 x /minutes/0,5’44, R2 = 0.993; species = 6.23 x lminutes1°.3742, R’ =

0.999)

Time

intervals Lower MAV

West Tennessee

bottomlands

Minutes

5

10

20

30

4 0

50

60

80

Mean number Mean number Mean number Mean number

of individuals of species of individuals of species

14.7 10.2 14.9 11.4

(15.OP (10.3) (15.3) (11.3)

20.0 13.0 21.4 14.8

(19.8) (12.9) (22.0) (14.7)

27.4 16.5 30.5 19.1

(32.5) (19.1)

32.9 19.0 37.6 22.2

37.4 21.0 43.6 24.8

41.4 22.7 48.8 26.9

45.0 24.2 53.6 28.8

51.2 26.7 62.2 32.1

*Observed means are given in parentheses.

tion should not vary among years or even among

regions. (Recall that the regression slopes of cumulative species or cumulative birds were similar among

years and among regions.)

To examine the feasibility of predicting the most efficient time interval for point counts, a power function

regression curve was fitted to the cumulative number

of individuals and to the cumulative number of

species recorded in the lower MAV as a function of

point count duration. Variation in both number of

birds (Y = 7.098 multiplied by lminutes)0~45cs, R* =

0.995) and number of species (Y = 5.867 multiplied by

{minutesPs4sc, R* = 0.998) was almost completely explained by the derived regression equations. For west

Tennessee bottomlands, comparable power function

relationships were observed for cumulative number of

birds (Y = 6.53 multiplied by lminutes10~5*44, R* =

0.953) and cumulative number of species (Y = 6.23

multiplied by IminutesP42,R* = 0.999). Using the

respective power function equations, cumulative

number of individuals or species that would be

recorded during point counts of up to 80 minutes were

predicted (table 3).

The number of birds and species recorded during 5-

minute censuses were compared to that predicted

from a wide range of point count intervals. For example, 5-minute counts from west Tennessee (table 3)

included 35 percent of the birds and 46 percent of the

species predicted for 40-minute censuses. (Recall that

5-minute censuses included 47 percent of the birds

and 59 percent of species recorded in 20-minute

counts.) Corresponding percentages for the lower

MAV were 40 percent and 49 percent. Thus, a two-fold

increase in sampling effort (40 minutes vs. 20 minutes) in west Tennessee reduced the percentages included in a 5-minute census by a factor of 0.3 for birds

[(47 percent - 35 percent1135 percent] and for species

I(59 percent - 46 percent1146 percent]. Corresponding

rates of reduction for 5-minute counts in the lower

MAV were 0.4 and 0.3, suggesting that the expected

benefit derived from longer censuses is much less

than proportional to the increased effort.

This reduction was illustrated more clearly when

efficiency (i.e., predicted increase in new birds or new

species per unit effort) of point counts of varying duration was examined (fig. 5). On the average, lower

MAV and west Tennessee point counts added 3.0 new

birds and 2.2 new species per minute during a 5

minute census. Efficiency was markedly lower for

both number of new birds and number of new species

beyond 5 minutes, however, decreasing by as much as

73 percent (range from 56 to 73 percent) and averaging 66 percent. Except for lo-minute counts (i.e., new

birds and new species added between 5 and 10

minutes) in west Tennessee, efficiency was below one

new bird or one new species per minute for all point

count intervals beyond 5 minutes (fig. 5). Efficiency

gradually declined beyond 10 minutes until it essentially reached a negative asymptote ‘at 40 minutes

(fig. 5).

To evaluate further the proposed 5-minute point

count protocol of Ralph and others (in press), all subsequent analyses of bird censuses from the lower

MAV were restricted to species or individuals recorded during the’first 5 minutes of each point count.

Unless otherwise noted, subsequent analysis of west

7
_--- ,NB,RO6

_------- TNTlre

-MbVBlROS

_.- HIVTeXe

------ __________

-----s--

------.-

---____________--_-_--- ---

0 % 1 I ’ ’ ’ ’

t I , I I I _

0 lb 20 30 40 so 60 70 00

MINUTES

Figure 5.-Effkiency, i.e., number of new birds (TNBIRDS, MAVBIRDS) or bird species (TNTAXA, MAVTAXA) recorded

per minute, of point counts of uor$ng duration within

hardwood forests of the lower Mississippi Alluvial Valley (MAW, 1992, and west Tennessee bottomlands (TN),

1985-1987.

Tennessee data included cumulative number of birds

and cumulative number of species recorded in each

20-minute census.

Radius Distance of Plots

In the lower MAV study, distance to each bird was

recorded as either ~25 m, between 25 and 50 m, or

>50 m (table 4). By inspection, it appeared that more

birds were recorded in the intermediate distance

category (521) than in the closest category (340) or

farthest category (365). The observed frequency distribution, however, did not depart significantly from a

null proportional distribution across distance

categories and among habitats (X 2 = 8.90, df = 4, P =

0.064) The proportion of birds recorded in each of the

distance categories was somewhat surprising because

the closest concentric ring represented a much

smaller area (1,964 m2) than the second (5,890 ms), or

the outer category (23,562 m2, assuming a maximum

detectable distance of 100 m). Accordingly, the percentage of birds recorded within each of the categories

should have increased with area, i.e., distance from

the observer.

When the observed distribution was compared to a

frequency distribution determined according to total

area sampled, there was a highly significant departure from the expected (X 2 = 1,313, df = 6, P <O.OOl).

There were significantly more birds (X 2 = 562, df = 2,

P <O.OOl) observed within 25 m than expected (101).

Between 25 and 50 m, 303 birds were expected rather

than 521 (X2 = 155, df = 2, P ~0.001). The 365 individuals recorded beyond 50 m was less than the expected 1,216 birds (X 2 = 595, df = 2, P <O.OOl).

In west Tennessee bottomlands. distance to each

bird was recorded in one of four concentric bands: ~30

m, 30 to 60 m, 61 to 322 m, or >122 m. Generally, spatial distribution of bird counts was similar to the

lower MAV with most of the individuals recorded in

the intermediate distance categories (table 5). However, the percentage of total observations recorded in

each distance category varied among years and between lo-minute and 20-minute point counts (table

5). Generally, more birds were detected beyond 122 m

during 20-minute counts, especially in 1985 and 1986.

In 1987, eight and five times more birds were

recorded beyond 122 m during lo- and 20-minute

point counts, respectively, than in previous years.

Most of the corresponding decrease occurred between

30 and 60 m with only half the number of birds

recorded in this category during 198’7 as recorded in

1985 or 1986.

At least some of the annual variation in west Tennessee can be attributed to habitat variation, particularly the landscape context of point counts. Censuses in 1987 occurred in much smaller forest patches

than those in 1985 or 1986. An important consideration in conducting point counts among sites that vary

greatly with respect to total area, or core area

(Temple 1986), is the number of edge-associated bird

species that are within sampling distance. Corvids, icterids, and many other edge associates (e.g., northern

cardinal [Cardinalis cardinalis]) typically have loud

vocalizations and thus are more readily detected at

greater distances than interior forest species. If the

difference in point counts relative to landscape context increases the proportion of loud edge associates,

then the percentage of birds detected in the farthest

distance category should increase. A significant relative increase in number of species detected at greater

distances would necessarily result in lower percentages in other categories. However, decreases were not

proportional among remaining categories and the previous scenario does not adequately explain why a

greater decrease occurred between 30 and 60 m (250

percent) as compared to ~30 m (20 percent) or 61 to

122 m (30 percent).

There are several other factors that potentially influence the number of birds detected at varying distances. For all species, detection rates decrease with

increasing distance from the observer (Jarvinen and

Vaisanen 1975, Scott and Ralph 1981, Verner 1985).

Moreover, detection distances and the effect of distance on detection differs among habitats and species

(Jarvinen and Vaisanen 1975, Scott and Ralph 1981,

Verner 1985). In the lower MAV study, a slightly

greater number of birds in the dry habitat were

detected at greater distances (table 4), perhaps because this habitat was more open. Also, an analysis of

numbers detected for three species showed no consistent pattern, except that fewer birds were detected

8
Table 4.-Number IN) and percentage of individual birds detecfed for each habitat

ujithin distance categov in the lower Mississippi Alluvial Valley, 7

through 16 May 1992

Distance category Wet

Habitat

Mesic DlY

Meters

~25

25 to 50

>50

N Percentage

127 28.3

217 43.4

130 28.3

N Percentage

126 32.3

166 40.4

110 27.3

N Percentage

87 25.7

138 40.5

125 33.8

Total 474 100.0 402 100.0 350 100.0

Table 5.-Percentage ,of individual birds (for each year and overall) detected within each distance

category during IO-minute and 20-minute point counts across seven major drainages in

west Tennessee bottomlands

1985 1986 1987 1985-87

Distancecategory lO* 20* lO* 20* lO* 20* lO* 20*

Meters ______ _____________._._________________ -----percent .___________. _ __________________________________

<30 18.8 17.4 17.5 16.1 13.8 12.8 17.6 16.3

30-60 36.3 35.1 34.8 32.0 16.2 15.5 32.8 31.2

61-122 39.7 36.7 42.5 41.2 29.3 29.2 39.3 37.6

>122 5.2 10.8 5.2 10.7 40.7 42.5 10.3 14.9

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

*Length of point counts in minutes.

farther away. Of the three species examined, only the

red-eyed vireo showed significant variation in detection relative to distance (F = 7.29; df = 2,31; P = 0.003).

Only one red-eyed vireo was ever detected at a distance greater than 50 m, probably owing to the difficulty of hearing or distinguishing its song at great

distances. Although the mean number of prothonotary warblers recorded appeared to decrease with increasing distance from the observer (1.81, 1.47, and

1.33 per count, respectively), detection of prothonotar-y warblers across distance categories was not significantly different (F = 1.34; df = 2,46; P = 0.272).

Northern cardinals, which sing loudly, were detected

in almost equal numbers at all three distances (F =

0.700; df = 2,64; P = 0.505).

Despite differences in detection rates among species

and the many other variables that may influence

detection probabilities (e.g., point count duration,

habitat features, landscape context), the relative frequency of birds recorded among distance categories in

west Tennessee and the lower MAV were similar in

some important ways. In both studies, significantly

more birds were recorded within 25 or 30 m than

would be expected from the corresponding proportions

of total area included within each of these concentric

bands. But in neither study did the relative frequency

of birds in the closest category represent an overwhelming percentage of the total. Rather, in both

studies 35 to 40 percent of the total birds encountered

were recorded in the second distance category (25 to

50 m or 30 to 60 m) except in west Tennessee in 1987.

Moreover, when the same (or similar) sites were

visited from one year to the next, the percentage of

birds recorded in each distance category remained

remarkably similar (table 5). For example, 5.2 percent

of lo-minute counts were recorded beyond 122 m in

1985 and 1986; corresponding values for 20-minute

counts were 10.8 percent and 10.7 percent. Preliminary data should probably be gathered for any specific

set of circumstances, but these data suggest that the

recommended distance measures for point count plots

(Ralph and others, in press) are useful in bottomland

hardwood forests of the lower MAV and associated

drainages.

Variation Among Point Counts and Minimum

Sample Size

There was significant variation in numbers of both

individuals and species per count for the lower MAV

(table 6). Mean number of individuals ranged from

10.8 birds per count in wet habitat within the central

region to 20.0 birds per count in mesic habitat within

the southern region. Corresponding values for species

9
Table G.-Means and standard deviation (S.D.) for individual birds and species per count among

regions, localities, and habitats in the lower Mississippi Alluvial Valley, 7 through 16

May 1992

Individuals Species

Region Locality Habitat n* Mean S.D. Mean SD.

Southern 1 wet 4 13.5 2.89 8.3 1.26

1 mesic 4 14.5 3.11 8.5 1.91

2 wet 5 17.4 2.30 11.0 1.87

2 mesic 1 20.0 0.00 11.0 0.00

2 dry 3 18.0 1.73 10.0 1.73

3 wet 5 19.6 4.28 11.2 2.17

3 mesic 4 16.3 1.89 11.3 0.96

Central 1 mesic 4 12.5 4.51 9.3 2.75

1 dry 5 16.0 2.12 12.0 1.87

2 wet 2 14.5 4.95 9.0 0.00

2 mesic 3 13.0 1.00 9.0 1 .oo

2 dry 3 14.0 2.00 9.7 1.53

3 wet 5 10.8 3.11 8.6 1.82

3 mesic 2 12.0 4.24 8.5 3.54

3 dry 4 13.0 2.45 9.5 1.29

Northern 1 wet 3 12.7 1.53 10.7 0.58

1 mesic 3 16.7 1.53 11.3 0.58

1 dry 3 15.3 2.08 11.0 1.00

2 wet 3 19.0 0.00 13.7 2.08

2 mesic 3 13.0 1.00 9.3 1.15

2 dry 3 15.7 3.79 11.7 2.52

3 wet 4 14.0 1.41 10.8 0.96

3 mesic 4 14.5 3.32 11.5 2.89

3 dry 2 14.5 0.71 10.0 0.00

Overall 8 2 15.0 0.37 10.3 0.22

*Number of point counts.

counts were 8.3 and 13.7, both in wet habitat, within

the southern and northern regions, respectively.

Point counts in the central region averaged the fewest

number of individuals per census (13.2, standard

deviation [S.D.] = 3.07); the southern and northern

regions averaged 16.8 (S.D. = 2.20) and 15.0 (S.D. =

2.16), respectively. The central region also averaged

the fewest species per census (9.6, S.D. = 1.93). Mean

number of species per census in the southern region

was 10.2 (S.D. = 1.74), whereas the northern region

averaged 11.2 (SD. = 1.70).

Variation Among Regions and Localities.-Overall

ANOVA models for both number of species and number of individuals were significant; differences between regions and localities nested within regions

were significant, but neither habitat nor the interaction between habitat and region were significant

(table 7). This result suggests that at the smaller scale

most of the variation in point counts occurs among

locations, but less so among habitats. This may be because continuously forested habitats in the lower

MAV are very similar; most habitats have comparable

elevation and microrelief, experience perennial inundation, and generally support forest cover types that

are similar in composition and structure. In contrast,

species composition and other habitat features presumably show appreciable variation among regions.

As suggested from our study, habitat distribution

among regions varies considerably. (Recall that wet

habitat appeared less abundant in the central region,

whereas dry habitat was difficult to find in the

southern region.)

Minimum Sample Size.-There are two major approaches to estimating minimum sample size. The

“non-power method” (Ott 1977), calculates the miniTable 7.-ANOVA tables (overall models) for the number of species

and individual birds per count*

Effect

Species

Region

Habitat

Region*Habitat

Locality (Region)

Within

df

2

2

4

6

67

F P>F

5.70 0.005

0.32 0.730

1.11 0.357

2.82 0.017

Individuals

Region 2 7.46 0.001

Habitat 2 0.61 0.546

Region*Habitat 4 0.31 0.871

Locality (Region) 6 2.33 0.042

Within 6 7

*Region and habitat were treated as main effects with.patch

nested within region.
mum sample size for a specified difference between

two means, given the variance in the data, but only

considers the probability of making a Type I statistical error (a; the probability of rejecting the null

hypothesis if it is true). The “power method” (Neter

and Wasserman 1974) calculates minimum sample

size relative to the probability of making Type I and

Type II errors. (The probability of making a Type II

error is designated by p. It is the probability of accepting the null hypothesis if it is false; 1-p is called the

“power” of the test. For more on the importance of considering power in statistical tests, see Forbes 1990.)

The power method dictates minimum sample sizes

greater than or equal to the non-power method and

thus is more conservative.

Minimum sample size estimates for the lower MAV

varied greatly according to the variable measured and

scale of resolution (table 8); only extremely large

sample sizes would accommodate all possible measurements. The sample size (given a particular variance) determines the magnitude of the difference between factor means that can be detected with

statistical significance. If the difference between two

means is small relative to their variance, the power of

the test will probably be low. To achieve greater power

in this situation usually requires very large sample

sizes, even approaching infinity. Unfortunately, selecting an acceptable power for each test may often be

largely subjective.

Regardless, one does not want all comparisons for

all species to be significant. If all tests were significant, there would be little information about the

relative importance of each factor in determining bird

distributions. Thus, it is necessary to choose a minimum sample size that is reasonable for identifying

biologically important factors, yet is achievable with

reasonable effort. Minimum sample sizes were calculated for a variety of differences among means, and for

several different variables: number of species, number of individuals, and for selected species exhibiting

different distributions and abundances throughout

the lower MAV (table 8). Furthermore, the appendix

summarizes minimum sample sizes for 20 selected

species (including the 8 most abundant species) with

locality level differences among all 3 regions.

For each variable in table 8, four minimum sample

sizes are presented. Note that these are minimum

sample sizes for each level of a factor. Thus, the total

sample size for a study comparing three regions would

be three times the number given in the table.4 The

4Note that the minimum sample sizes in table 8 were all calculated using a design with one factor and three factor levels. If

more or fewer levels were used, this number would be slightly

greater or smaller. The values in table 8 are a useful approximation.

numbers in the column for the “actual difference” are !

minimum sample sizes that would have been required

to detect the difference in factor means according to

the variation incorporated in the point counts conducted in the lower MAV. (Note that the MSE, mean,

and range were also calculated from these censuses.)

The actual difference could not be statistically significant for variables with sample sizes greater than

about 82, which was the number of counts conducted

in the lower MAV. For example, differences among

habitats (table 8) could only have been significant for

the prothonotary warbler or the red-eyed vireo.

Sample sizes for a difference of +0.25 birds are those

that would be required for statistical significance if

the greatest difference among factor levels was 0.25

birds (or 0.25 species). Since this value designates an

absolute change in abundance, the relative difference

1

I

identified as statistically significant will vary with the i

mean. When the mean is large, such as mean total

number of species or number of individuals, the relative difference represented by H.25 is small (about

2.4 percent and 1.7 percent of the means for regional

total species and total individuals, respectively). In

contrast, our regional estimate of mean number of I

wood thrushes was 0.23 per census (table 8); a difference of +0.25 individuals becomes an increase or

decrease of >lOO percent of the mean. This was the

case for the majority of species in the lower MAV, including 9 of the 20 more common species (appendix).

Perhaps a better approach for estimating minimum

sample sizes of individual species is to specify some

relative change in population abundance. For this

reason, a column was included in table 8 and the appendix that summarizes sample sizes for differences

of +25 percent of the mean. This translates into a

maximum difference among treatment means of 50

percent of the overall mean. One can readily compute

sample sizes for a wide range of relative changes in

abundance by simply increasing or decreasing the disparity between treatment means and overall mean

(i.e., pj - p; Neter and Wasserman 1974, p. 493).

Selecting an appropriate magnitude of relative

change will depend on the objectives of the research or

monitoring program. Sample sizes required to detect

1

differences of +25 percent of the mean were calculated

because they should frequently reflect biologically

meaningful changes and because they represent an i

achievable goal for most public and private land

managers. For more detailed research endeavors such

as modeling population dynamics or performing population viability analyses of threatened or endangered

species, consistent detection of smaller relative changes may be necessary.

Finally, to provide a different perspective on the /

question of sample size, minimum difference detected

among factor level means (given the MSE) with a 1
Table B.-Minimum sample sizes calculated for several variables according to the power method with several detectable difference llalues among factor level means; MSE, mean, range, and actual difference were calculated from observed variations among factor levels in this study (unless otherwise noted a = 0.05 and p =

0.10)

Variable MSE’ Mean’ Range’

Actual

difference!

MO.25

birdsl

&250/o

of mean**

Difference

detected

if n = 70++

Total species

Region

Locality”

Habitat

3.791

3.759

4.143

10.30 1.53 41 >500 5 1.192

9.60 1.87 2 9 >500 5 1.187

10.30 0.69 >500 >500 5 1.246

Total birds

Region

Localityts

Habitat

9.283 14.95 3.56 2 0 z-500 5 1.866

9.174 13.21 2.63 3 5 >500 6 1.855

11.272 14.95 0.87 >500 >500 5 2.056

Northern cardinal

Region 1.292

Locality** 1.144

Habitat 1.326

1.59 0.48 >200 >200 5 3 0.696

1.71 1.04 28 s200 4 4 0.655

1.59 0.27 >200 >200 5 3 0.705

Prothonotary warbler

Region 0.563

Locality*’ 0.571

Habitat 0.822

0.95 1.38 9 5 8 70 0.453

0.57 0.35 >200 5 8 >200 0.463

0.95 0.94 23 9 0 9 5 0.545

Red-eyed vireo

Region 0.858

Locality? 0.208

Habitat 0.445

0.52 0.79 15 37 >200 0.366

0.32 0.78 9 23 >200 0.279

0.52 0.36 4 4 4 4 >200 0.408

Wood thrush

Region

Localityts

Habitat

0.232 0.23 0.13 >200 27 >200 0.295

0.151 0.18 0.24 5 8 15 >200 0.238

0.235 0.23 0.03 >200 27 s200 0.297

*Mean square error of one-way analysis of variance, with three levels of treatment (for example, northern,

central, and southern region).

‘Mean birds per count; this value is the same for region and habitat.

$Range between the means for the highest and lowest levels of treatment.

%ample size that is required to get statistical significance for the actual observed difference among factor level

means (range).

¶Sample size that would be required to detect a significant difference of 0.25 birds (or species) above or below the

overall mean.

**Sample size that would be required to detect a significant difference between two treatments, i.e., between 25

percent above and 25 percent below the overall mean. (The difference between two treatment means of 50 percent

of the overall mean.)

“The difference (in number of birds) that could be significantly detected by a sample size of 70.

$*Because locality was nested within region, no overall minimum sample size can be calculated for locality. The

minimum sample sizes in this table were calculated from one-way ANOVA of the three patches within the central

region because of the balanced sample size design.

sample size of 70 are presented in table 8. A sample Although the values for minimum sample size vary

size of 70 initially was selected for this exercise be- widely, most of the values are 170 and many fall into

cause it was the largest sample size value presented the range of 40 to 60, especially for differences that

in the table of curves (Neter and Wasserman 1974, p. probably are biologically significant. For species that

827). Since then, however, it was recognized that 70 have large differences relative to their overall mean

point counts was an achievable goal and would (e,g., prothonotary warbler), minimum sample size

probably accommodate the needs of most public and could be much smaller, especially if the study were

private land managers. designed carefully with respect to selected variables

12
and factor levels. For example, an analysis of region

and habitat choices by prothonotary warblers at 3 factor levels would require 27 counts (9 counts per factor

level). Conversely, species that have more variation

and exhibit smaller differences (e.g., northern cardinal) would require larger sample sizes.

Despite the variation among species, however, 50

point counts per factor level should be sufficient to

detect most of the biologically meaningful differences.

Thus, a study comparing species distribution and

abundance among 3 forest fragment size categories

would require a minimum of 150 counts (50 counts per

treatment or factor level). To avoid pseudoreplication

(Hurlbert 1984), an independent observation (i.e.,

single point count or the mean of 2 or ‘more censuses)

should be obtained from each of the 150 forest

patches. In this example, using only three levels of

fragment size categories (in contrast to five categories) would both reduce the total number of point

counts needed and increase the number of habitat

fragments available in each size category.

Finally, other means of reducing sample size are to

accept a higher probability of rejecting the null

hypothesis when it is true (i.e., accept an a >0.05) or to

accept a lower probability of rejecting the null when it

is false (i.e., increase p or reduce the power of the test,

Neter and Wasserman 1974). Most biologists recognize the need to report the a level associated with

each statistical test. It is equally important to report

the power of each test when the null hypothesis is not

rejected (Forbes 1990). This provides the reader with

explicit information regarding the likelihood that the

null hypothesis was not rejected because of small

sample size.

Allocation of Sampling Effort

Points Versus Visits in Delta Experimental Forest.-

AI1 possible combinations of six visits to each of six

points initially were compared by using ANOVA to

model cumulative number of species as a function of

number of points visited, number of visits to each

point, and their interaction across all four stands.

Each year was considered independently because

total species recorded in DEF during 1991 (Species

total [S] = 39) and during 1992 (S = 55) were substantially different, mostly because of late flooding. in

1991. Significant variation in mean cumulative

species occurred among number of points and number

of visits to each point both in 1991 (F = 91.30, df = 35,

P ~0.0001) and 1992 (F = 89.78, df = 35, P <O.OOOl)

(table 9). There was no significant interaction between number of points and number of visits but the

ANOVA model explained about 97 percent of the

variation in mean cumulative number of species both

in 1991 (I?* = 0.9673) and 1992 (R* = 0.9668) (table 91.

Tableg.-Summary data from ANOVA analysis where mean

cumulative number of species recorded during point

counts of birds was modeled as a function of number of

points and number of visits to each point during 1991

and 1992, Delta Experimental Forest, Stoneville, MS

1991

Source

Model

Error

Corrected

total

R2 = 0.9673

d f

3 5

108

143

Sum of Mean

squares square F value P>F

1482.01 42.34 91.30 0.0001

50.09 0.46

1532.10

Source

Points

Visits

Points*

visits

d f

5

5

2 5

Type III

Sum of Mean

squares square F value P>F

944.61 188.92 407.35 0.0001

535.49 107.10 230.92 0.0001

1.90 0.07 0.16 1 .ooOO

1992

Source

Model

Error

Corrected

total

RZ = 0.9668

d f

3 5

108

143

Sum of Mean

squares square F value P>F

3584.08 102.40 89.78 0.0001

123.18 1.14

3707.26

Source d f

Type III

Sum of Mean

squares square F value P>F

Points 5 2276.96 455.39 399.26 0.0001

Visits 5 1306.12 261.22 229.03 0.0001

Points*

visits 2 5 1.00 0.04 0.04 1.0000

*The coeffkient of determination.

In 1991, cumulative number of species increased

significantly with each added point through five

points (fig. 61, but six points did not differ from five

points (F = 3.19, minimum significant difference =

0.7853, df = 108, P <O.Ol). Similarly, cumulative number of species increased with each revisit up to four

visits, but four visits did not differ from five visits (F =

3.19, minimum significant difference = 0.7853, df =

108, P ~0.01). Total increase in cumulative number of

species with counts conducted at 6 points (across all 6

visits) averaged 7.4 and represented an addition of 20

percent of the species pool to our estimate (table 10).

Total increase in cumulative number of species with

six visits (across all 6 point&/averaged 5.49, adding

only 14 percent of the species pool to our estimate. In

1992, significant increases in cumulative number of

species occurred with each added point through all six

13
0

A

n

0

A

0

n

A

0

A

0

Cl

0

A

PI

0

A

0

e

l A

0 A

A l

m

l

0

0

0

A A

A

0

0

Cl

0 I POINT

A 2 POINTS

0 3 POINTS

B 4 POINTS

A 5 POINTS

l 6 POINTS

I I I I I I

IO1 I I I I I I

I 2 3 4 5 6

I 2 3 4 5 6

NUMBER OF VISITS NUMBER OF VISITS

Figure6.-Cumulative number of bird species recorded during Figure 7.-Cumulatke number of bird species recorded duriq

1991 censuses for all possible combinations of six visits 1992 censuses for all possible combinations of six visit:

to each of six points on Delta Experimental Forest, to each of six points on Delta Experimental Forest

Stoneville, MS. Stoneville, MS.

points, whereas significant increases with revisits occurred through four visits as in 1991 (F = 3.19, minimum significant difference = 1.2316, df = 108, P

<O.Ol) (fig. 7). Average total increase in cumulative

number of species with 6 points in 1992 was 11.82,

representing a 21 percent increase in our species estimate; 6 visits increased the total cumulative number of species by 8.9, a 16 percent increase in total

number of species (table 10).

Although no interaction was detected between

points and visits,, when all possible paired reciprocals

(e.g., 1 point and 2 visits vs. 2 points and 1 visit) were

compared, more points visited yielded significantly

greater cumulative number of species than more

visits to each point both in 1991 (F = 4.34, df = 15, P

<O.OOOl) and in 1992 (F = 4.07,’ df = 15, P ~0.0001).

Moreover, in all individual paired comparisons, more

points visited invariably yielded more species than

more visits to each point in both 1991 (fig. 8) and 1992

(fig. 9). Also, as number of points and visits apl

A

m

0

0

A

l

A

0

A

0

0

0 1 POINT

A 2 POINTS

0 j POINTS

m 4 POINTS

A 5 POINTS

0 6 POINTS

proached their maximum values, increases in either

had increasingly less effect on cumulative number 01

species recorded in 1991 (fig. 8) and 1992 (fig. 9).

Despite the suggestion that five points or four visits

to each point represented sufficient sampling effort,

(i.e., increases beyond either level did not significantly

increase total number of species), our performance

relative to capturing the variation in DEF was not impressive. In both years, the maximum proportion of

the total species pool (estimated by total species

recorded for the entire DEF) included in our censuses

(i.e., sampling effkiency) continued to ilicrease gradually with additional points, but approached only 55

percent in 1991 and 52 percent in 1992 (fig. 10). Increasing revisits beyond five visits in 1991 did not improve our ability to capture more of the species pool

(fig. 11); in 1992 a sixth visit increased the efficiency

by 1.4 percent (Api = 0.014). In both years, increased

effkiency (Api) began to decrease rapidly beyond

three visits and three points.

14
.

0

.

0

m

0

l

0

. B

.

’ a O

l

n g 0 O

í 0 a

I 3 O

NUMBER Of POINTS AND VISITS

Figure R-Comparison of cumulative number of bird species Figure 9.-Comparison of cumulative number of bird species

recorded between 15 possible paired reciprocals (i.e., 1 recorded between 15 possible paired reciprocals (i.e., 1

point-2 visits vs. 2 points-l visit) of number of points point-2 visits vs. 2 points-l uisit) of number of points

visited and number of visits to each point, Delta Ex- uisited and number of visits to each point, Delta Experimental Forest, Stoneuille, MS, 1991. perimental Forest, Stoneville, MS, 1992.

NUMBER Of POINTS bN0 VISITS

Table lO.-Mean cumulative number of species obtained from bootstrap estimates

of all possible combinations of six points and six visits to each point

during 1991 and 1992 bird censuses, Delta Experimental Forest,

Stoneuille, MS

Visits

Points 1 2 3 4 5 6

1991

1

2

3

4

5

6

__ ___. _ ..___ _ ____.--- _ -..-- Mean cumulative number of species -----------.---._----.-

9.66 12.37 13.97 14.92 15.43 15.83

13.15 15.76 16.88 17.69 18.23 18.65

14.92 17.25 18.49 19.34 20.06 20.36

16.12 18.39 19.70 20.66 21.06 21.54

16.94 19.37 20.70 21.43 22.20 22.67

17.69 20.10 21.40 22.26 22.95 22.39

1992

1 11.16 14.92 17.12 18.52 19.55 20.14

2 15.85 19.81 21.72 23.12 23.99 24.75

3 18.56 22.49 24.31 25.49 26.61 27.30

4 20.47 24.21 26.20 27.50 28.37 29.36

5 21.86 25.64 27.56 29.09 30.03 30.74

6 23.05 26.75 28.96 30.26 31.22 32.07

Points Versus Visits Versus Duration in West Ten- was 20 minutes, the duration of the longest individual

nessee.-Point counts from west Tennessee bottom- counts made in west Tennessee. Four combinations

lands provided an opportunity to examine simul- were subjected to the test: four 5-minute counts at a

taneously the three important variables in a single single point, two lo-minute counts at a single point,

study. We used ANOVA to model mean cumulative single lo-minute counts at two separate points, and a

number of species as a function of number of points, single 20-minute count. In the analysis, the combinanumber of visits to each point, and duration of point tion of two 5-minute counts at two separate points

counts. The comparison was conducted by selecting was not calculated. Limits of the original data

combinations of numbers of points, visits, and dura- prevented calculation of another desirable combinations for which the total length of observation time tion for the analysis, single B-minute counts at four

15
o’60< 0.55 -

0.50 -

T

6‘ \ ‘0.45 -

t

s

2

2 0.40 -

k

0.35 -

0.30 -

0

0

0

l 8

m

0

l

0

l

0

0 m

l

0

0

0

0

0

0

0

0

1991 1992

Pi 0 m

*p, 1991 1992

-I 0 0

0.25-

I 2 3 4 5 6

NUMBER OF POINTS

-0.

- 0.

- 0.

- 0

- 0

-c

I O

0 9

0 6

i;

a

.04

.02

I.00

0.5!

0.5t

0.4:

Y

$I

e

z

2 0.4

2

k

lu

0.3

0.3

>-

3-

5-

os-

‘0 -

Figure IO.--Proportion of 1991 and 1992 species pool included in Figure Il.-Proportion of 1991 and 1992 species pool included in

point count censuses (EFFICIENCY, pd and change in point count censuses (E$‘FlCIENCY, pi) and change in

eficiency (ApJ relative to number ofpoints visited at a efjiciency (~pil relative to number of visits to each point

locality, i.e., forest stand (averaged across all six visits), at a locality, i.e., forest stand (averaged across all six

Delta Experimental Forest, Stoneville, MS. points), Delta Experimental Forest, Stoneville, MS.

points. The resulting ANOVA model was significant

(F = 21.80; df = 2,137; P <O.OOOl), explaining about 24

percent of the variation (R* = 0.24) in mean cumulative number of species (table 11). Simultaneous comparisons of the mean cumulative number of species indicated that four counts of 5-minute duration yielded

2.8 more species than either combination of two lominute counts, and the latter yielded 2.2 more species

on average than a single 20-minute count (table 12).

Comparisons of number of points and number of visits

of constant duration supported conclusions from the

lower MAV that more points yielded greater cumulative number of species than more visits.

CONCLUSIONS AND RECOMMENDATIONS

Considerable variation in number of individual

birds and number of species was observed among

localities, habitats, regions, and even between years

at the same site; yet, the general response functions of

number of individuals or number of species recorded

per unit time during point counts in bottomland

I.

1

0 0

i

0.06

0 l

0

0

0

l

a

B

p. 1991 1952

-I 0 m

B

- 0.06

I I I I I I I

I 2 3 4 5 6

NUMBER OF VISITS

hardwood forests appear to be comparable among

years and across geographic regions. Point counts

should be at least 5 minutes in duration, an initial 3-

minute count plus an additional 2 minutes. Point

counts longer than 10 minutes are not justified because of a reduction in efficiency, unless logistics

preclude additional points during a morning sampling

period. Excluding logistic considerations, visiting

more points is always better than an equivalent number of visits to the same point or extending the duration of a point count by an equivalent amount of time.

Because of the horizontal and vertical density of

vegetation in bottomland hardwoods, birds should be

recorded in three concentric bands: ~25 m, 25 to 50 m,

and >50 m. Given the variability and range of factor

levels considered in this study, a sample size of 50

point counts per factor level should be sufficient to

detect most of the meaningful differences in bottomland hardwood forests of the lower Mississippi Alluvial Valley and associated drainages. These recommendations are compatible with the protocol

recommended by the Monitoring Working Group.

16
Table 11 .- Summary data from ANOVA analysis where mean

cumulative number of species recorded during point

counts of birds was modeled as a function of total duration of counts for counts of cumulative duration of Zp

minutes of observing, during the breeding season, 1985-

87, in west Tennessee bottomlands

Source df

Sum of Mean

squares square F value P>F

Model

Error

Corrected

total

RZ = 0.2414

2 297.63 148.82 21.80 0.0001

137 935.13 6.83

139 1232.76

Source df

Type w

Sum of Mean

squares square F value P>F

Duration 2 297.63 148.82 21.80 0.0001

Table 12.-Mean cumulative number of species from point counts of birds in west

Tennessee bottomlands, 1985-87, for censuses representing varying

numbers ofpoints, numbers of visits to each point, and total duration of

sampling effort (time) in minutes for counts of cumulative duration of

20 minutes

Combination

Sample Mean number

size species* S.D. Minimum Maximum

1 20-min visit to

a single point 57 18.82 2.28 12.48 22.68

2 lo-min visits to

a single point 55 20.89 2.86 14.10 26.32

1 lo-min visit to

each of 2 points 18 21.62 2.75 16.06 25.00

4 5-min visits to

a single point 10 23.91 2.73 19.16 27.96

*Each observation represents the result of 50 simulation runs by bootstrapping

from the original data points of that combination of points, visits, and duration.

LITERA’I’URE CITED

American Ornithologists’ Union. 1983. Check-list of

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

Press, Inc. 877 p.

Anderson, Bertin W.; Ohmart, Robert D. 1981. Comparison of avian census results using variable distance transect and variable circular plot techniques. Studies in Avian Biology. 6: 186-192.

Blondel, J.; Ferry, C.; Frochot, B. 1970. La methode

des indices ponctuels d’abondance (IPA) ou des

releves d’avifaunepar “stations d’ecoute.” Alauda.

35: 55-71.

Burdick, David M.; Cushman, Douglas; Hamilton,

Robert; Gosselink, James G. 1989. Fauna1 changes

and bottomland hardwood forest loss in the Tensas

watershed, Louisiana. Conservation Biology. 3:

282-292.

Delta Council. 1945. Delta bottomland hardwoods.

Stoneville, MS: Delta Council. 23 p.

Durham, Daryl B.; Abernethy, Robert K.; Eagar,

Daniel C. [and others]. 1988. Application of the

Habitat Evaluation System to modeling bottomland

hardwood forest communities in west Tennessee.

Transactions of the North American Wildlife and

Natural Resources Conference. 53: 481-490.

Efron, Bradley. 1982 The jackknife, the bootstrap,

and other resampling plans. Monogr. 38. Philadelphia, PA: Society for Industrial and Applied Mathematics, Conference Board of the Mathematical

Sciences-National Science Foundation regional

conference series in applied mathematics. 92 p.

Forbes, L. Scott. 1990. A note on statistical power.

Auk. 107: 438-439.

Ford, Robert P. 1990. Habitat relationships of breeding birds and winter birds in forested wetlands of

17
west Tennessee. Knoxville, TN: University of Tennessee. 129 p. M.S. thesis.

Hurlbert, Stuart H. 1984. Pseudoreplication and the

design of field experiments. Ecological Monographs.

54(2): 187-211.

Jarvinen, Olli; Vaisanen, Risto A. 1975. Estimating

relative densities of breeding birds by the line transect method. Oikos. 26: 316-322.

Neter, John; Wasserman, William. 1974. Applied

linear statistical models. Homewood, IL: Richard D.

Irwin, Inc. (p. 493,549,827). 842 p.

Ott, Lyman. 1977. An introduction to statistical

methods and data analysis. North Scituate, MA:

Duxbury Press. 730 p.

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

others]. [In press]. Field methods for monitoring

landbirds. Gen. Tech. Rep. San Francisco: U.S.

Department of Agriculture, Forest Service, Pacific

Southwest Forest and Range Experiment Station.

SAS Institute, Inc. 1988. SASSTAT User’s Guide.

Release 6.03 ed. Car-y, NC: SAS Institute, Inc. (p.

560). 1,028 p.

Scott, J. Michael; Ralph, C. John, eds. 1981. Estimating numbers of terrestrial birds. Studies in Avian

Biology. 6: l-630.

Scott, J. Michael; Ramsey, Fred L. 1981. Length of

count period as a possible source of bias in estimating bird densities. Studies in Avian Biology. 6: 409-

413.

Temple, Stanley A. 1986. Predicting impacts of

habitat fragmentation on forest birds: a comparison

of two models. In: Verner, Jared; Morrison, Michael

L.; Ralph, C. John, eds. Wildlife 2000: modeling

habitat relationships of terrestrial vertebrates.

Madison, WI: University of Wisconsin Press: 301-

304.

U.S. Department of the Interior. 1990. Breeding bird

survey: instructions for conducting the breeding

bird survey. Laurel, MD: U.S. Department of the

Interior, Fish and Wildlife Service. 4 p.

Verner, Jared. 1985. Assessment of counting techniques. In: Johnston, Richard F., ed. Current ornithology. New York: Plenum: 247-302. Vol. 2.

Zar, Jerrold H. 1984. Biostatistical analysis. 2d. ed.

Englewood Cliffs, NJ: Prentice-Hall, Inc. 718 p.

18
Appendix

Minimum sample sizes for selected species recorded during point counts in the

lower Mississippi Alluvial Valley. Sample size was calculated according to the

power method (for CL = 0.05 and p = 0.10) with several detectable difference

values among factor level means

Species MSE* Mean*

Actual fl.25 525 percent

difference* birds of mean

Acadian flycatcher+

Region 0.59

Locality

Southern 0.89

Central 0.22

Northern 0.58

Habitat 0.62

American redstart

Region 0.02

Locality

Southern 0.04

Central 0.03

Northern 0.00

Habitat 0.02

Blue-gray gnatcatcher+

Region 0.55

Locality

Southern 0.41

Central 0.39

Northern 0.91

Habitat 0.83

Brown-headed cowbird

Region

Locality

Southern

Central

Northern

Habitat

Carolina chickadee’

Region

Locality

Southern

Central

Northern

Habitat

Carolina wren’

Region

Locality

Southern

Central

Northern

Habitat * Hooded warbler

Region

Locality

Southern

Central

Northern

Habitat

Indigo bunting

Region

Locality

Southern

Central

Northern

Habitat

0.43

0.00

0.78

0.45

0.48

1.16

2.32

1.91

2.58

1.22

0.76

0.77

0.78

0.67

0.64

0.08

0.15

0.00

0.11

0.09

0.19

0.00

0.04

0.51

0.18

0.915 5 3

1.038 >200

0.607 9

1.107 >200

0.915 >200

0.024 >200

0.038 6 5

0.036 5 3

0.000 >200

0.024 z-200

0.744 9

0.308 >200

0.429 44

1.464 >200

0.744 >200

0.415 2 3

0.000 >200

0.750 5 0

0.464 3 3

0.415 44

0.805 8 5

1.077 >200

0.893 z-200

0.464 >200

0.805 >200

1.402 >200

1.615 >200

1.357 19

1.250 >200

1.402 23

0.098 65

0.192 33

0.000 z-200

0.107 >200

0.098 z-200

0.110 5 0

0.000 >200

0.036 58

0.286 65

0.110 33

6 5 8 0

9 5 9 0

23 6 5

6 5 5 0

7 0 8 5

9

9

9

>200

9

5 8

4 4

4 4

9 5

9 0

4 4

>200

8 5

44

5 0

>200

>200

>200

>200

>200

8 5

8 5

8 5

8 0

7 0

9

15

>200

9

9

19

>200

9

53

19

>200

>200

>200

>200

>200

100

>200

>200

4 4

>200

>200

z-200

>200

>200

>200

>200

>200

>200

>200

>200

4 4

3 3

4 4

4 4

3 3

>200

>200

>200

>200

s200

>200

>200

z-200

>200

>200

19 j
Minimum sample sizes for selected species recorded during point counts in the

lower Mississippi Alluvial Valley. Sample size was calculated according to the

power method (for a = 0.05 and / 3 = 0.10) with several detectable difference

values among factor level means-Continued

Species MSE* Mean*

Actual zt0.25 225 percent

difference* birds of mean

Kentucky warbler

Region 0.09

Locality

Southern 0.07

Central 0.03

Northern 0.18

Habitat 0.09

Northern cardinal+

Region 1.29

Locality

Southern 0.91

Central 1.14

Northern 0.85

Habitat 1.33

Northern parula

Region 0.22

Locality

Southern 0.22

Central 0.24

Northern 0.00

Habitat 0.25

Prothonotary warbler+

Region 0.56

Locality

Southern 0.74

Central 0.57

Northern 0.32

Habitat 0.82

Red-bellied woodpecker’

Region 0.82

Locality

Southern 0.37

Central 0.66

Northern 0.92

Habitat 0.82

Red-eyed vireo

Region 0.36

Locality

Southern 0.50

Central 0.21

Northern 0.24

Habitat 0.44

Rufous-sided towhee

Region 0.02

Locality

Southern 0.04

Central 0.04

Northern 0.00

Habitat 0.02

Summer tanager

Region 0.25

Locality

Southern 0.04

Central 0.38

Northern 0.25

Habitat 0.26

0.110 80 9 >200

0.077 27 9 >200

0.036 44 9 >200

0.214 z-200 19 >200

0.110 23 9 >200

1.585 >200 >200 53

1.769 9 95 33

1.714 27 z-200 44

1.286 >200 90 53

1.585 >200 >200 53

0.220 27 23 >200

0.462 9 23 >200

0.214 9 27 >200

0.000 >200 >200 >200

0.219 >200 27 >200

0.951 9 58 70

1.885 53 85 23

0.571 >200 58 >200

0.464 15 33 >200

0.951 23 90 95

1.256 >200 90

1.115 9 37

1.143 85 70

1.500 29 95

1.256 100 90

0.524 15 37

1.038 44 53

0.321 9 23

0.250 23 27

0.524 44 44

0.024 >200 9

0.038 65 9

0.036 58 9

0.000 >200 >200

0.024 58 9

0.244 53 27

0.038 65 9

0.321 15 37

0.357 >200 27

0.244 >200 27

53

33

53

44

53

>200

50

>200

>200

>200

>200

>200

>200

>200

>200

>200

>200

>200

z-200

>200

20
Minimum sample sizes for selected species recorded during point counts in the

lower Mississippi Alluvial Valley. Sample size was calculated according to the

power method (for a = 0.05 and p = 0.10) with several detectable difference

values among factor level means-Continued

Mean*

Actual

difference*

ti.25

birds

+25 percent

Species MSE* of mean

Tufted titmouse’

Region

Locality

Southern

Central

Northern

Habitat

Wood thrush

Region

Locality

Southern

Central

Northern

Habitat

Yellow-billed cuckoo

Region

Locality

Southern

Central

Northern

Habitat

Yellow-throated vireo

Region

Locality

Southern

Central

Northern

Habitat

0.52 0.878 58 5 3 8 0

0.47 0.615 15 5 0 >200

0.44 0.893 15 4 4 5 6

0.45 1.107 3 3 4 4 37

0.56 0.876 >200 5 8 85

0.23 0.232 z-200 27 >200

0.32 0.308

0.15 0.179

0.26 0.214

0.23 0.232

3 3

15

2 7

2 7

>200

>200

>200

5 8

>200

>200

0.43 0.659 8 0 4 4 100

0.45 0.885 >200 4 4 6 5

0.36 0.607 2 7 3 7 100

0.42 0.590 2 3 4 4 >200

0.45 0.659 >200 4 4 >200

0.05 0.049 100 9 >200

0.00 0.000 z-200 >200 >200

0.10 0.107 3 7 9 >200

0.04 0.036 8 0 9 >200

0.05 0.049 >200 9 >200

*Calculated from the observed variation among factor levels.

‘The most abundant species, i.e., those whose totals comprised >50 percent (8720,621) of all

birds recorded during point counts conducted throughout the lower Mississippi Alluvial Valley, 7

through 16 May 1992.

21 I
Smith, Winston Paul; Twedt, Daniel J.; Wiedenfeld, Paul B. [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.

To compare efficacy of point count sampling in bottomland

hardwood forests, duration of point count, number of point counts,

number of visits to each point during a breeding season, and minimum sample size are examined.

Keywords: Census, neotropical migrant birds, power method,

sampling protocol.
The use of trade, firm, or corporation names in this paper is for the information and convenience of the reader. Such use does not constitute offkial endorsement or approval by the U.S. Department of Agriculture of any product or

service to the exclusion of others that may be available.

Persons of any race, color, national origin, sex, age, religion, or with any

handicapping condition are welcome to use and enjoy all facilities, programs,

and services of the USDA. Discrimination in any form is strictly against agency

policy, and should be reported to the Secretary of Agriculture, Washington, DC

20250.

*U.S. GOVERNMENT PRINTING OFFICE: 1993-766-017/80013