Biological Report 90(1)

January 1990

Survey Designs and Statistical Methods

for the Estimation of Avian Population

Trends

Fish and Wildlife Service

u.S. Department of the Interior
Biological Report

This publication series of the Fish and Wildlife Service comprises reports on the results of research,

developments in technology, and ecological surveys and inventories of effects of land-use changes on fishery and

wildlife resources. They' may include proceedings of workshops, technical conferences, or symposia; and

interpretive bibliographies.

Copies of this publication may be obtained from the Publications Unit, U.S. Fish and

Wildlife Service, 1849 C Street, N.W., Mail Stop 130 - ARLSQ, Washington, DC 20240, or

may be purchased from the National Technical Information Service (NTIS), 5285 Port

Royal Road, Springfield, VA 22161.

ISSN 0895·1926

This publication may be cited as follows:

Sauer, J. R., and S. Droege, editors. 1990. Survey designs and statistical methods for the estimation of avian

population trends. U.S. Fish Wildl. Serv., Biol Rep. 90(1). 166 pp.
Biological Report 90(1)

January 1990

Survey Designs and Statistical Methods for

the Estimation of Avian Population Trends

edited by

John R. Sauer and Sam Droege

u. S. Fish and TiVildlife Service

Patuxent Wildlife Research Center and

Office ofMigratory Bird Management

Laurel, Maryland 20708

u.S. Department of the Interior

Fish and Wildlife Service

Washington, DC 20240
Preface

On 12-13 April 1988 a workshop on the analysis of avian population trends was held at the Patuxent Wildlife

Research Center in Laurel, Maryland. The workshop was cosponsored by the Branch ofMigratory Bird Research

of the Patuxent Wildlife Research Center and the Office of Migratory Bird Management. During this workshop,

we hoped to bring together some of the biologists and statisticians that coordinate and analyze data from major

bird surveys to discuss recent advances in analytical methods of estimating population trends. The workshop had

three sessions: one to describe some of the major surveys used to estimate population trends, one to discuss

analytical methods, and one to consider population trends of a selected species: scissor-tailed flycatchers (Tyrallllus

forficatus), for which a data set from the North AmericanBreedingBird Survey had been distributed to participants

before the meeting.

These proceedings present the results of the workshop. The papers are organized into three parts, following

the design of the workshop. Part I is composed of papers that describe the design of major avian surveys, along

with reviews of the constraints that the designs place on the analysis of population trends. Part II presents some of

the major analytical methods that are used to estimate population trends. There is a good deal of diversity among

the papers in this part, with some papers discussing overall approaches to surveys and their analysis, others

attempting to analyze the relations among the methods, and some presenting only a specific method of analysis.

Several papers broach general questions of sample size allocation for roadside surveys and associated technical

questions. Part III contains three analyses of the scissor-tailed flycatcher data set: two variants of the

route-regression method and a nonparametric analysis.

11
Acknowledgm~nts

We thank R. 1. Smith of the Office of Migratory Bird Management, and R. J. Hall and R. L. Jachowski of the

Branch of Migratory Bird Research, Patuxent Wildlife Research Center, for providing us with financial support

for both the workshop and the preparation of the proceedings. The Patuxent Wildlife Research Center provided

logistical support for the workshop. N. Bushby and N. Hestbeck of the Branch of Technical Services, Patuxent

Wildlife Research Center, coordinated the peer review ofmanuscripts. M. A. McKeogh assisted with many aspects

of the workshop and the preparation of the proceedings. We also thank D. S. Chu, D. K. Dawson, R. M. Erwin,

P. H. Geissler, and J. D. Nichols for assistance with the workshop.

Most of the participants in the workshop were called on to review at least one manuscript. We thank C. Bunck,

G. S. Butcher, D. Bystrak, J. T. Cary, B. T. Collins, D. K. Dawson, R. T. Engstrom, R. M. Erwin, M. R. Fuller,

P. H. Geissler, J. M. Hagan, G. A. Hall, J. Hatfield, M. A. Howe, F. James, D. W. Johnston, M. K. Klimkiewicz,

W. Link, C. E. McCulloch, J. D. Nichols, B. R. Noon, R. J. O'Connor, G. Pendleton, C. S. Robbins, C. R. Smith,

J. R. Spendelow, S. R. Taub, K. Titus, and J. Verner for their efforts in manuscript review. L. Hungerbuhler and

M. A. McKeogh typed several of the manuscripts.

1lI
Contents

Part I: Surveys Used to Estimate Avian Trends Page

The North American Breeding Bird Survey. Sam Droege 1

Audubon Christmas Bird Counts. Gregory S. Butcher. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Description of the Wisconsin Checklist Project. StanleyA. Temple and John R. Cary. . . . . . . . . . . . . . . 14

Use of breeding bird atlases to monitor population change. Chandler S. Robbins . . . . . . . . . . . . . . . . . . 18

Methodology of the International Shorebird Survey and constraints on trend analysis.

Marshall A. Howe 23

Evaluation of the Colonial Bird Register. R. Todd Engstrom 26

Descriptions of surveys: breeding bird censuses. David w: Johnston 33

Migration banding data: a source of information on bird population trends?

Deanna K. Dawson 37

Sources of migrant hawk counts for monitoring raptor populations.

Mark R. Fuller and Kimberly Titus 41

The Common Birds Census in the United Kingdom. Raymond J. O'Connor

Part II: Methods ofTrend Analysis

47

Topics in route-regression analysis. Paul H. Geissler and John R. Sauer . . . . . . . . . . . . . . . . . . . . . . . . . . 54

Estimation of annual indices from roadside surveys. John R. Sauer and Paul H. Geissler. . . . . . . . . . . . 58

Using rerandomizing tests in route-regression analysis of avian population trends.

Brian T. Collins 63

Estimating (relative) species abundance from route counts ofthe Breeding Bird Survey.

Lincoln E. Moses and Daniel Rabinowitz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Smoothed scatterplot analysis of long-term Breeding Bird Census data. Stephan R. Taub . . . . . . . . . . . 80

Methodological issues in the estimation of trends in bird populations with an example:

the pine warbler. Frances C. James, Charles E. McCulloch, and Loretta E. Wolfe. . . . . . . . . . . . . . . . 84

Using checklist records to reveal trends in bird populations.

Stanley A. Temple and John R. Cary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

Detecting trends in hawk migration count data. Kimberly Titus, Mark R. Fuller,

and Dan Jacobs 105

Evaluation of the sensitivity of breeding bird surveys using a stochastic simulation model.

Janles Cox 114

Influence of observer effort on the number of individual birds recorded on Christmas Bird Counts.

Gregory S. Butcher, and Charles E. McCulloch 120

Population trends in the least tern (Sterna antillarnm) from Maine to Virginia: 1975-1986.

R. Todd Engstrom, Gregory S. Butcher, and James D. Lowe 130

Trend analyses for raptor nesting productivity: an example with peregrine falcon data.

Paul H. Geissler, Mark R. Fuller, and Lynne S. McAllister. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 139

Current thinking on United Kingdom bird monitoring. Raymond J. O'Connor 144

IV
Part ill: Scissor-tailed Flycatcher Analysis

Analysis of scissor-tailed flycatcher population changes. Brian T. Col/ins. . . . . . . . . . . . . . . . . . . . . . . .. 157

Route-regression analysis of scissor-tailed flycatcher population trends. John R. Sauer 160

Trends in counts of scissor-tailed flycatchers based on a nonparametric rank-trend analysis.

Kinlberly Titus 164

v

Part I: Surveys Used to EstiInate

Avian Trends

For most of the surveys discussed in the workshop, trend analyses have traditionally involved (1) identifying a

dependent variable that indexes population size at one of a group of survey sites (or routes) and (2) estimating

population changes (trends) over time in some way that takes into account the intrinsic features of each site. In

this part'ofthe workshop, we discussed some ofthe sources ofinformation used to estimate bird population trends,

and we attempted to identify some of the constraints that the underlying sources of information placed on the

selection of a relevant dependent variable and the analytical method of estimating trends.

In some surveys, the dependent variables used to index population size at a site in a year are "natural," such as

the total number of individuals heard or seen over the 50 stops of a Breeding Bird Survey route, or the population

estimates from a Common Bird Census, Breeding Bird Census, or a colonial bird count. In other surveys, count

data must be adjusted to control for variation in observer effort, such as occurs in the Christmas Bird Count,

migration banding, hawk counts, or the International Shorebird Survey. Finally, in certain surveys, presence or

absence data must be converted to estimates of proportion-of-area-occupied data for analysis, as in breeding bird

atlases or checklist projects. All surveys, however, share a common problem: the yearly index values are not

statistically based estimates of population size, but instead are assumed to be related to population size by a

proportionality constant (Bart and Schoultz 1984). Standardization of survey procedures and a posteriori

adjustments of indices by effort, discussed in this section, represent attempts to minimize variation in this

proportionality constant. We recommend that readers see Lancia et al. (in press) for a detailed discussion of the

consequences of assuming a constant relation between population indices and population parameters.

Literature Cited

Bart, J., and J. D. Schoultz. 1984. Reliability of singing bird surveys: changes in observer efficiency with avian density. Auk

101:307-318.

Lancia, R. A., J. D. Nichols, and K. H. Pollock. Estimating the number of animals in wildlife populations. In T. H. Bookhout,

ed. Wildlife management techniques, 5th ed. The Wildlife Society, Bethesda, Md. (In press)

AVIAJ'I POPULATION TRENDS 1 - ,

The North American Breeding Bird Survey

by

Sam Droege

u.s. Fish and Wildlife Service

Office ofMigratory Bird Management

Laurel, Maryland 20708

Origin and Extent of Survey

The North American Breeding Bird Survey (BBS) is

coordinated and maintained by the U.S. Fish and

Wildlife Service and the Canadian Wildlife Service. Its

purpose is to provide long-term information about the

abundance and distribution of breeding birds in the

United States and Canada. Data exist for more than 500

species. More than 400 of these species have been

recorded on more than 50 survey routes (Erskine 1978;

Robbins et al. 1986; Droege and Sauer 1989).

The BBS was designed and initiated byC. S. Robbins.

In 1966 routes were first run in the eastern States and

Provinces. In 1%7 the survey was expanded to include

the central States and Provinces, and by 1968 the entire

continent was included.

Design ofthe BBS

Individual BBS routes are 24.5 miles long and

comprise 50 point counts spaced one-half mile apart.

Each point count, or stop, extends for 3 min. During this

period the observer counts all birds heard singing or

calling and any bird sighted within a quarter-mile radius.

The density of BBS routes in a State or Province varies

with the available number of observers. Density is

highest in the East and lowest in the Intermountain West

and northern boreal zones (Figure). Once an

appropriate density of routes has been estimated for a

State or Province, routes are stratified by degree blocks

of latitude and longitude (e.g., 1 per degree block in

Nevada and 16 per degree block in Maryland). Within

a degree block, starting points are chosen at random, as

are directions of travel. The actual roads the route

follows are constrained by their availability and traffic.

With a few exceptions, routes do not cross degree blocks

or physiographic strata boundaries.

Each route is run once a year by a skilled volunteer.

Most routes are run during June. However, routes in

portions of Arizona, California, Florida, Nevada, New

Mexico, and Texas are often run in May, and some

northern routes are run during the first week ofJuly.

Data are submitted by the observer to the Office of

Migratory Bird Management, U.S. Fish and Wildlife

Service, Laurel, Maryland, both on original field sheets

and on summary sheets. These data are checked for

transcriptional, mathematical, and biological errors on

arrival. Each summary sheet is cataloged, entered into

a data base, and then verified. Data are sent through

several computer routines that check for clerical and

mathematical errors. Every route is checked against

regional lists of expected species. Unusual species are

flagged on printouts of a route's data and sent to the

observer who double checks the work and, if necessary,

sends further documentation. Data collected under

poor weather conditions or by observers with

insufficient ability to accurately census birds are coded

but not used in subsequent analyses.

Constraints on the Analysis ofthe BBS

Differences in regional density of routes and

consistency in coverage (Figure) affect our ability to

track regional populations. Most BBS routes have had

gaps in coverage during the past 22 years. Gaps occur

when observers are unavailable or if a completed survey

is deemed unsuitable. Statistical analysis of BBS data

requires special care because of the relatively large

amount of missing data. Simple yearly means can

produce deceptive results, especially if data are missing

from routes containing very high or low densities of the

species being analyzed (Geissler and Sauer 1990). Every

region has, to some extent, missing data problems.

Additionally, almost every region has had new routes

added and old ones discontinued, further adding to the

problems of unbalanced data. The number of BBS

routes run also varies with year. During the first years of

the program, coverage was of relatively low intensity.

Small sample sizes reduced our ability to accurately

track population changes.
2 BIOLOGICAL REPORT 90(1)

BREEDING BIRO SURVEY ROUTES

3181 TOTAL OBSERVATIONS PLOTT

Figure. Distribution of Breeding Bird Survey routes.
Most of the United States and Canada have an

extensive network of secondary roads. The random

placement of BBS routes in these regions is relatively

straightforward. However, in some portions of the

mountainous west (e.g., the Great Basin) and the far

north (e.g., Northwest Territories), roads are fewer and

tend to occur in valleys and near watercourses. In

regions of low relief, riparian zones are likely to be

undersampled because most roads travel across rather

than within floodplains. The location and design of

regional road networks will influence calculations of

avian relative abundance. Unfortunately, we will not

know how well BBS routes represent regional habitats

until LANDSAT data and associated technologies

become more available.

Roadsides can be considered unique habitatsin some

regions. Most roads act as boundaries, creating edge

habitats populated by speeding metallic predators. They

often parallel superb song and hunting perches, such as

telephone lines and fences. How dissimilar roadside

stops are from random geographic points and how

dissimilar rates of roadside bird population change are

from the entire landscape's rate of change are currently

unknown. However, at least in regions oflow elevational

relief, all common diurnal species appear to be well

represented on BBS routes, although perhaps not

relative to their true abundance.

Observers vary in their ability to hear, identify, and

estimate the abundance of birds (Faanes and Bystrak

1984). Even among experienced observers, large

differences exist in how numbers are estimated (Bart

and Schoultz 1984). Wherever possible, observer

covariables should be included in statistical analyses of

BBS data (Geissler and Sauer 1990).

Flocking and colonial birds present special problems

because of their greater variation among yearly counts

(Sauer and Droege 1989). Variation often results from

localized concentrations of feeding birds (e.g., gulls,

herons, vultures) or stops near colonies (e.g., swallows).

This variation can be reduced if the number of stops at

which the birds were seen are used to calculate trends

rather than total number of individuals (Bart and

Klosiewski 1989). However, the number ofstops at which

a species is present is a measure of the area the species

occupies rather than a direct measure of its abundance.

The number of individuals recorded on a BBS route

is both a function of a species' detectability and its true

abundance. Detectability varies among species,

observers, and habitats, and with time of year, weather,

density, and the male's breeding status (Emlen 1971;

Berthold 1976; Marten and Marler 1977; Ralph and

Scott 1981). Due to these differences in detectability,

relative abundance is not directly comparable between

species. Detectability can also change with local

population density. Herice, the relation between BBS

AVIAN POPULATION TRENDS 3

counts and true density could be curvilinear or even a

step-function. Because the life histories of the surveyed

populations are almost always poorly known it is

difficult to make generalizations about the BBS's

sensitivity to true population change. Statistical analyses

ofthese data and their subsequent interpretation should

dwell on the patterns of population change rather than

on the magnitudes of calculated trends and variances.

Variations associated with weather conditions and

time-of-year are minimized by only accepting data from

routes run during acceptable conditions.

Several recent studies have sought to corroborate

population trends on BBS routes with data from

independent surveys. Carolina wren (77l!yothonls

iudovicianus) numbers from Indiana May Counts

closely correspond to BBS annual indices calculated

for the same region (E. Hopkins, personal

communication). Raptor counts from migration

stations and Christmas Bird Counts (CBC) show good

corroboration for those species commonly detected on

BBS routes (Schueck et al. 1989; Titus et al. 1989).

State long-term trends for mourning doves (Zenaida

macroura) from the Mourning Dove Survey are

correlated with BBS trends calculated from the same

States (J. R. Sauer, personal communication).

Preliminary work with several species recorded

commonly on both BBS routes and CBC's also yield

similar population trends (Butcher et al. 1990).

Population trends derived from the Quebec checklist

program are in almost complete concordance with the

sign of trends from BBS routes in Quebec (A. Cyr,

personal communication).

We will continue to investigate and catalog the

relative strengths and weaknesses of the BBS data set

and encourage others to do likewise.

Literature Cited

Bart, J., and S. P. K1osiewski. 1989. Use of presence-absencc

to measure changes in avian density. J. Wildl. Manage.

53:847-852.

Bart, J., and J. D. Schoultz. 1984. Rcliability of singing bird

surveys: changes in observer efficiency with avian density.

Auk 101:307-318.

Berthold, P. 1976. Methoden der bestandserfasung in der

ornithologie: ubersicht und kritische betrachtung. J.

Ornithol. 117:1--69.

Butcher, G. S., M. R. Fuller, L. S. McAllister, and P. H.

Giessler. 1990. An evaluation ofthe Christmas Bird Counts

for monitoring population trends ofselected species. Wildl.

Soc. Bull. (In press)

Droege, S., and J. R. Sauer. 1989. North American Breeding

Bird Survey Annual Summary 1988. U.S. Fish Wildl. Serv.,

BioI. Rep. 89(13). 16 pp.

Emlen, J. T. 1971. Population densities of birds derived from

transect counts. Auk 88:323-342.

Erskine, A. J. 1978. The first ten years of the Co-operative

Breeding Bird Survey in Canada. can. Wildl. Serv., Rep.

Ser. 42. 61 pp.
4 BIOLOGICAL REPORT 90(1)

Faanes, C. A, and D. Bystrak. 1981. The role of observer bias

in the North American Breeding Bird Survey. Pages

353-359 in C. J. Ralph and J. M. Scott, eds. Estimating

numbers of terrestrial birds. Stud. Avian BioI. 6.

Geissler, P. H., and J. R. Sauer. 1990. Topics in

route-regression analysis. Pages 54-57 in Survey designs

and statistical methods for the estimation of avian

population trends. U.S. Fish Wildl. Serv., BioI. Rep 90(1).

166 pp.

Marten, K., and P. Marler. 1977. Sound transmission and its

significance for animal vocalization. 1. Temperate habitats.

Behav. Ecol. Sociobiol. 2:271-290.

Ralph, C. J., and J. M. Scott, editors. 1981. Estimating

numbers of terrestrial birds. Stud. Avian BioI. 6. 630 pp.

Robbins, C. S., D. Bystrak, and P. H. Geissler. 1986. The

Breeding Bird Survey: its first fifteen years, 1%5-1979.

U.S. Fish WildI. Serv., Resour. Publ. 157. 1% pp.

Sauer, J. R., and S. Droeg~. 1989. Wood duck population

trends from the North American Breeding Bird Survey.

Pages 159-165 in L. H. Fredrickson et aI., eds. Proceedings

of the 1988 North American Wood Duck Symposium,

SI. Louis, Mo.

Schueck, L. S., M. R. Fuller, and W. S. Seegar. 1989. Falcons.

Northeast Raptor Management Symposium and

Workshop. NationaL Wildlife Federation. Sci. Tech. Ser.

13:71-80.

Titus, K., M. R. Fuller, D. F. Stauffer, and J. R. Sauer. 1989.

Buteos. Northeast Raptor Management Symposium and

Workshop. National Wildlife Federation. Sci. Tech. Ser.

13:53--64.
AVIAN POPULATIO~ TRENDS 5

Audubon Christmas Bird Counts

by

Gregory S. Butcher

Cornell Laboratory ofOrnithology

159 Sapsucker Woods Road

Ithaca, New York 14850

Origin and Current Design ofthe

Christmas Bird Count

The Christmas Bird Count (CBe) is the oldest and

largest wildlife survey in the world. It is sponsored by

the National Audubon Society, and the results are

published inAl7lericall Birds. It began in 1900 when 26

individuals responded to an editorial in Bird-Lore

magazine (Chapman 1900) by spending an hour or two

counting birds in their neighborhood on Christmas

afternoon. Since then, the increase in both the number

of counts (Fig. 1) and the number of participants

(Fig. 2) has been dramatic. In 1986-87, 41,249

individuals participated at 1,544 locations, including

1,508 locations in the United States (excluding

Hawaii) and Canada (Table 1).

All CBC's occur within a 15-mile-diameter circle.

Each local CBC coordinator chooses a single calendar

day within 2 weeks of Christmas Day for each year's

count. Each local CBC is conducted by between 1 and

1800

1600

1400

1200 C

::J

o

U 1000

Q) 800

-0

E

.£ 600

230 field observers, who spend between 1 and 650

party-hours and 0 and 4,100 party-miles looking for

birds (Butcher and McCulloch 1990). Each local CBC

also includes between 0 and 343 individuals recording

birds at home. Most party-miles are covered by car;

party-hours can include hours on foot, bicycle, skis,

snowshoes, snowmobile, car, boat, and airplane. Many

counts include nocturnal party-hours and party-miles.

Every CBC has one or more count coordinators, who

are responsible for recruiting participants, assigning

participants to areas, providing overall guidance to

participants, compiling the results, securing written or

photographic descriptions of rare species, and sending

the results to the National Audubon Society. Most

coordinators divide the count circle into areas and

assign one group to each area. Participants are

instructed not to count birds out of their assigned area.

Rarities may be staked out ahead of time, and known

birding "hot-spots" may be revisited several times

during the day. When areas are revisited (and when

counting at feeders), participants record only the

45000

40000

35000

c

o 30000 CL

~~

""§ 25000

CL

o 20000

<1>

-0

E 15000

::J

:z:

400

Year

10000

5000

1900 10 20 30 40

Year

50 60 70 80 85

Fig. 1. The number of Christmas Bird Counts done each year

has grown dramatically from 1900 to 1985.

Fig. 2. The number of participantsin Christmas Bird Counts has

grown dramatically from 1900 to 1985.
6 BIOLOGICAL REPORT 90(1)

Table 1. Physiographic regions ofthe United States and Canada, excluding Hawaii.

1 Subtropical

2 Floridian

3 Coastal Flatwoods

4 Upper Coastal Plain

5 Mississippi Alluvial Plain

6 Coastal Prairies

7 South Texas Brushlands

8 East Texas Prairies

9 Glaciated Coastal Plain

10 Northern Piedmont

11 Southern Piedmont

12 Southern New England

13 Ridge and Valley

14 Highland Rim

15 Lexington Plain

16 Great Lakes Plain

17 Driftless Area

18 SI. Lawrence River Plain

19 Ozark-Ouachita Plateau

20 Great Lakes Transition

21 Cumberland Plateau

22 Ohio Hills

23 Blue Ridge Mountains

24 Allegheny Plateau

25 Open Boreal Forest

26 Adirondack Mountains

27 Northern New England

28 Northern Spruce-Hardwoods

29 Closed Boreal Forest

30 Aspen Parklands

31 Till Plains

32 Dissected Till Plains

33 Osage Plain-Cross Timbers

34 High Plains Border

35 Rolling Red Prairies

36 High Plains

37 Drift Prairie

38 Glaciated Missouri Plateau

39 Great Plains Roughlands

40 Black Prairie

53 Edwards Plateau

54 Rolling Red Plains

55 Staked Plains

56 Chihuahuan Desert

61 Black Hills

62 Southern Rocky Mountains

63 Fraser Plateau

64 Central Rocky Mountains

65 Dissected Rocky Mountains

66 Sierra Nevada

67 Cascade Mountains

68 Northern Rocky Mountains

80 Great Basin Deserts

81 Mexican Highlands

82 Sonoran Desert

83 Mojave Desert

84 Pinyon-Juniper Woodlands

85 Pitt-Klamath Plateau

86 Wyoming Basin

87 Intermountain Grasslands

88 Basin and Range

89 Columbia Plateau

90 Southern California Grasslands

91 Central Valley

92 California Foothills

93 Southern Pacific Rainforests

94 Northern Pacific Rainforests

95 Los Angeles Ranges

96 Southern Alaska Coast

98 Willamette Lowlands

99 Tundra

maximum number of individuals of a particular species

seen at one time. Most count coordinators try to cover

as much ofthe count circle as the number ofparticipants

and accessibility will allow. Most count coordinators

serve for many years, and many participants cover the

same areas year after year.

Evaluation ofthe CBC Design

There are a number ofstandards that should be met,

to some degree, if a surveyor census technique is to be

used for studies of changes in relative abundance

through time orspace. The CBC does have standardized

techniques, some of which were presented earlier.

However, certain important factors, such as the route

covered and the amount of effort, are not standardized.

In some cases, count locations don't remain constant. In

thissection, I list the ideal for a standardized count, then

compare the CBC design with the ideal.

Year-fa-year Changes in Coverage

The most basic point is that a survey should be done

the same way in different areas and in different years.

However, on the CBC, the amount of effort spent

looking for birds (as measured by observers, hours, or

miles) varies dramatically between count locations

within years and within count locations between years

(Butcher and McCulloch 1990). Also, individuals cover

their area of the CBC circle in a variety of ways. For

example, some observers may use boats in some years

and not in other years. This methodological difference

can have a dramatic effect on the number of waterfowl

counted. Similarly, some participants stay near their

cars in cold or wet years, but walk longer distances in

warm, dry years. This difference can dramatically affect

the number oflandbirds counted. Wilds (1980) pointed

out an interesting biasthrough time: birders avoid urban

areas; thus, when a circle becomes increasingly urban,

birders move their efforts to the less-developed parts of

the circle. Despite all these variations that can occur, in

fact most observers cover the same areas year after year

in essentially the same way. In summary, coverage varies

from year to year, but remains very similar.

Count circles may change location yet keep the same

name. The change may be as little as 1% or as much as

100%. Count circles may stay in the same place, but

report different latitude-longitude coordinates or a

different name. Deciding whether a similar location in

different years is the same location requires detective

work and individual judgment.

Expertise and Diligence ofCBC Participants

In a standardized survey, reported numbers should

be representative of what is in the study area on the

day ofthe survey. For this to be true for CBC's, birders

should know how to identify birds and should be
competent and diligent at recording numbers of

individuals. In a standardized survey, a coordinator

might screen observers for ability and train them until

they meet minimal criteria. In the CBC, beginners

participate often without training. However, beginners

are almost always assigned to parties with an

experienced birder. Expert birders are patchily

distributed throughout the country; some counts will

have many expert birders, others will have few or none.

Groups that lack expert birders will make errors in

identification; however, gross errors are weeded out

by the extensive editing system supervised by the

National Audubon Society. Editors in this system

include count coordinators, State editors, and national

editors. An interesting bias relating to identification

errors exists through time: birders are getting better at

identifying birds because field guides are getting

better, optical equipment is getting better, and new

identification tips are proliferating. Thus, there maybe

a built-in bias towards increase in numbers for species

that are (or were) even moderately difficult to identify.

We know that participants are more interested in

finding rare species than counting common species;

however, participants do count common species, even

if it is not always their primary interest.

Representativeness ofCBC Locations

If a goal of a survey is to extrapolate results to a larger

region than the area being surveyed, then the areas

included in the survey should be representative of the

AVIAN POPULATION TRENDS 7

areas not included. For this to be true, surveys should be

widely distributed in the area of interest and should

include habitats that are representative of nearby areas

that are not surveyed. Ideally, census-survey locations

should be assigned on a stratified basis, perhaps by

degree block of latitude-longitude, by physiographic

region, or by vegetation type; within the stratified units,

survey locations are often randomly chosen.

CBC locations are chosen by local count compilers. As

Fig. 3 shows, CBC's are not randomly distributed. They

are concentrated near cities; however, they avoid inner

cities. Thus, suburban areas are over-represented and

remote areas under-represented, although some

high-quality areasfar from cities are sought out for CBC's.

Bystrak (1981) divided North America into a number

of· physiographic regions. Plants and birds should be

more similar to each other within those regions than

between them. I used an updated version of Bystrak's

regions to determine the percentage of coverage of each

region by CBC's, assuming that the entire CBC circle

was covered (Table 2). In the winters of 1982-83, only

five northern regions were covered less than 1% by

CBC's (Fig. 4).

Timing ofCounts

Birds are most sedentary during the breeding season

and least sedentary during spring and fall migration.

Thus, winter isthe second best ~eason for consistent bird

counts. CBC's can occur during early winter from

15 December to 5 January. Bad weather within this

..

..... . .\

:.: . .. .... s.. ,. .... .

-' .

......

.... :'

Fig. 3. A map of the locations of all Christmas Bird Counts in the 48 contiguous United States and southern C',anada submitted to

the National Audubon Society for the winter of 1982-83.
8 BIOLOGICAL REPORT 90(1)

period may cause movements ofbirds during the survey;

year-to-year differences in the severity of early-winter

weather may cause dramatic fluctuations in reported

numbers for some species. Bad weather on a local count

day may depress both observer effort and bird activity

(see references in Ferner 1984); however, counts are

rarely rescheduled due to bad weather. Luckily, many

CBC's occur at the same time each year.

Recommendationsfor Using CBC Data

Researchers who use CBC data should:

• be aware of potential identification problems for

their species of interest (e.g., accipiters [Daniels 1975],

Thayer's gull, Lams thayeri [Mark 1981]);

• be aware of potential counting biases (Arbib 1981;

Bock and Root 1981);

• be aware of biases in habitat coverage (Wilds 1980;

Bock and Root 1981);

• be aware of the possible effects of cold or wet

weather either before or during a CBC (see references

in Ferner 1984);

• decide whether to split or lump count locations that

have moved slightly between years;

• determine how effort affects the counts of their

species of interest (Butcher and McCulloch 1990);

• consider the use of reference species, especially

when individuals of a species might be encountered by

more than one type of effort (Raynor 1975; Bock and

Root 1981; Haney 1983); and

• use a number of CBC locations and years for any

study (Bock and Root 1981).

Why Use eRe Data?

The CBC is a birdwatching event, not a scientific event,

and always has been. Why then should scientists pay any

attention to the information collected during CBC's? The

major reason is that the CBC is one of only two surveys in

the world that collects information on relative abundance

over a large proportion of an entire continent for a large

proportion of that continent's species. The other survey

is, of course, the Breeding Bird Survey (BBS) ofthe U.S.

Fish and Wildlife Service and the Canadian Wildlife

Service. CBC data are useful because they complement

the BBS, which is conducted in early summer.

A number of species are encountered frequently on

CBC's that are rarely encountered on the BBS. These

include raptors (Table 3) and coastal and wetlands

species (Table 4). In 1977, 322 species were seen on 20

or more BBS routes (Robbins et al. 1986); in 1982-83,

362 species were seen at 20 or more CBC locations

(Table 5). A total of 451 species appear on one or both

lists. Based on an arbitrary standard, BBS data are

better for 241 species, and CBC data are better for

210 species (Table 5).

Ninety-nine species were seen on 100 or more BBS

routes and at 100 or more CBC locations; these species

can be used to compare population dynamics for the same

species from the two different surveys to see if similar

trends are produced from both data bases. Butcher and

Fuller (1986) studied population trends of seven species

using the two data bases. Six of the seven species showed

qualitatively similar trends. Only the eastern bluebird

(Sialia sialis) showed different trends on the two data

bases. The eastern bluebird is a weather-sensitive species

with widely varying population levels from year to year;

thus, it is inappropriate to try to summarize its population

dynamics with a single trend line.

Other sources of bird population information also

correlate well with CBC data. Butcher (1986) found that

the American black duck (Anas mbripes) population

trend derived from CBC data was similar to the trend

derived from the Mid-Winter Waterfowl Inventory of the

U.S. Fish and Wildlife Service. Robbins (1970, 1978) and

Robbins and Bystrak (1974) experimented with a

carefully controlled wintersurvey for 5years in Maryland.

They found that both population trends and yearly

population indices were similar, using data from their

experimental survey and nearby CBC's. Dunn (1986)

Table 2. Proportion ofphysiographic regions covered by CBC's. a

Coverage (%)

>10

1-10

0-1

1963-64

10

41

20

1982-83

a Locations of physiographic regions are shown in Fig. 4; the names of all physiographic regions are listed in Table 1.

The following are the six regions that are less than 1% covered by CBC's:

25 - Open Borcal Forest

29 - Closed Boreal Forest

63 - Fraser Plateau

67 - Cascade Mountains

68-Canadian Rock)' Mountains

99-Tundra.
AVIA"i POPULATIO~TRENDS 9

Fig. 4. This map shows the physiographic regions as determined for the Breeding Bird SUlvey of the U.S. Fish and Wildlife Service.

These regions were assigned by D. Bystrak, based primarily on Aldrich (1%3) and modified by Bureau of Agricultural

Economics(1933), Fenneman (1931, 1938), Kuchler (1%4), Erskine (1978), and othersources. The names ofthe physiographic

regions are listed in Table 1. The areas included within the broken hemylines are covered 1% or more by CBC circles (Table 2).
10 BIOLOGICAL REPORT 90(1)

Table 3. FrequellCY ofellcoullter ofraptors 011 the Christmas Bird COUllt alld the Breedillg Bird Survey.

Christmas Bird Breeding Bird

Count (1982--83) Survey (1977)

Total Total Total Total

~cies circles birds routes birds

Black vulture 229 11,330 102 551

Turkey vulture 384 38,189 490 3,271

Osprey 109 1,479 61 104

American swallow-tailed kite 0 0 4 11

Black-shouldered kite 92 1,818 25 60

Snail kite 7 4 1 7

Mississippi kite 0 0 38 152

Bald eagle

adult 428 4,308 12 20

immature 289 4,965

age unknown 54 768

Northern harrier 806 11,124 169 320

Sharp-shinned hawk 790 2,875 29 31

Cooper's hawk 663 1,908 53 62

Northern goshawk 292 445 5 5

Harris' hawk 28 269 12 35

Red-shouldered hawk 440 3,530 163 309

Broad-winged hawk 25 83 142 181

Swainson's hawk 8 15 124 321

White-tailed hawk 13 43 2 2

Zone-tailed hawk 1 1 2 2

Red-tailed hawk 1,206 33,786 588 1,287

Ferruginous hawk 126 427 21 37

Rough-legged hawk 610 3,491 1 1

Golden eagle

adult 186 516 41 54

immature 109 259

age unknown 48 108

Crested caracara 20 202 8 24

American kestrel 1,109 29,633 665 1,622

Merlin 255 475 10 11

Peregrine falcon 104 166 3 3

Gyrfalcon 21 29 0 0

Prairie falcon 211 516 19 19

compared year-to-year population changes of Ontario

feeder counts and CBC's and found comparable results

for 12 of 25 species. Species that attended feeders

infrequently and species that had relatively small

population changes from year to year showed no

comparable results.

Many other studies of population dynamics that

used CBC data verified long-term trends or dramatic

population eruptions that were known from other

sources. A number of species, especially insectivorous

passerines in the southeastern United States, were

shown using CBC data to be sensitive to severe winter

weather (James 1962, 1963); the weather sensitivity of

these species was confirmed by BBS data

(Robbins et a1. 1986).

In summary, for all its difficulties, the CBC has an

impressive record for producing useful analyses of the

population dynamics of North American birds. Taken

as a group, these studies prove that there is a mother

lode of useful information in the CBC data base.

Creation of a Computerized CBC

Data Base

The Cornell Laboratory of Ornithology completed

computerization of a 3D-year data base of CBC

information in 1990, which will greatly aid the analysis

of population dynamics of species that are frequently

encountered on CBC's (the 362 species of Table 5).

Trends from the BBS data base are currently being
AVIAN POPULATION TRENDS 11

Table 4. Frequency of encounters ofcoastal and wetlands species on Christmas Bird Count and the Breeding Bird

Survey.

Christmas Bird Breeding Bird

Count (1982--83) Survey (1977)

Total Total Total Total

Species circles birds routes birds

Red-throated loon 161 4,656 0 0

Common loon 384 9,771 114 321

Pied-billed grebe 621 13,803 76 157

Horned grebe 320 10,557 11 29

Red-necked grebe 112 2,708 13 47

Eared grebe 185 52,813 22 443

Western grebea 137 70,078 8 449

Double-crested cormorant 375 139,924 45 379

American bittern 123 411 108 316

Great blue heron 894 26,525 481 1,305

Great egret 242 19,565 83 561

Snowy egret 160 13,974 37 263

Little blue heron 102 5,295 97 645

Tricolored heron 97 4,751 18 109

Cattle egret 138 35,426 149 5,582

Green-backed heron 182 1,650 493 1,158

Black-crowned night-heron 226 8,530 51 202

Tundra swan 171 55,018 0 0

Mute swan 122 4,486 3 15

Snow goose (white) 207 973,524 0 0

Snow goose (blue) 118 245,815 0 0

Canada goose 825 1,315,990 92 1,621

Wood duck 410 7,860 160 475

Green-winged teal 484 170,066 41 135

American black duck 567 151,686 54 234

Mallard 1,156 1,577,194 438 5,470

Northern pintail 469 743,705 62 636

Blue-winged teal 168 9,292 150 1,313

Northern shoveler 349 154,089 53 320

Gadwall 530 75,585 60 642

American wigeon 526 381,199 52 282

Canvasback 381 120,135 24 122

Redhead 313 40,026 35 234

Ring-necked duck 523 45,729 9 25

Greater scaup 253 156,303 1 10

Lesser scaup 541 115,772 41 830

Oldsquaw 203 176,165 1 31

Black scoter 150 14,528 0 0

Surfscoter 157 62,552 1 20

White-winged scoter 192 42,401 2 80

Common gOldeneye 752 92,937 23 84

Barrow's goldeneye 106 6,688 6 23

Bufflehcad 628 77,522 13 70

Hooded merganser 508 10,649 8 31

Common merganser 623 119,841 36 97

Red-breasted merganser 408 67,616 6 12

Ruddy duck 413 154,634 3 549

Osprey 109 1,479 61 104

Bald eagle

adult 428 4,308 12 20

immature 289 4,965

age unknown 54 768
12 BIOLOGICAL REPORT 90(1)

Table 4. Continued.

Christmas Bird Breeding Bird

Count (1982-83) Survey (1977)

Total Total Total Total

Species circles birds routes birds

Virginia rail 192 1,063 17 24

Sora 138 956 75 285

Common moorhen 157 10,337 21 131

American coot 657 451,716 112 1,747

Black-bellied plover 173 26,095 0 0

Greater yellowlegs 220 4,446 6 24

Lesser yellowlegs 131 2,492 21 165

Willet 103 34,611 69 573

Spotted sandpiper 196 1,660 203 441

Ruddy turnstone 118 7,538 0 0

Sanderling 167 38,156 0 0

Western sandpiper 143 103,135 0 0

Least sandpiper 207 47,906 2 2

Dunlin 211 192,570 1 1

Long-billed dowitcher 105 20,139 0 0

Common snipe 559 9,004 249 1,394

American woodcock 140 481 36 65

Laughing gull 105 148,781 33 831

Bonaparte's gull 282 139,919 4 31

Ring-billed gull 758 829,705 110 2,929

California gull 123 88,128 32 1,156

Herring gull 725 893,756 129 3,529

Iceland gull 91 6,600 0 0

Kumlien's gull 17 85 0 0

Glaucous-winged gull 101 138,918 11 672

Glaucous gull 143 1,232 1 160

Great black-backed gull 289 104,018 35 296

Forster's tern 156 16,882 24 115

Belted kingfisher 1,027 11,478 462 718

Marsh wren 341 5,680 85 355

Swamp sparrow 617 24,661 278 811

" Includes Clark's grebe.

Table 5. Species freque/lt~y encountered on the Breeding Bird Survey and the Christmas Bird COUllt.

Breeding Bird Survey (data from 1977)

Encounters

20-49

50-99

100+

20+

Number ofspecies

84

62

176

322

Number ofspecies BBS > 1/2 x CBCa

48

45

148

241

Christmas Bird Count (data from winter 1982-83)

Encounters Number ofspecies Numbcr of species CBC > 2 x BBSa

20-39

40-99

100-199

200+

20+

57

100

73

132

362

33

58

41

78

210

a This column is meant to indicate which species are better covered by the BBS and which by the Cnc. Because the nns is better standardized

than the cnc, a species is considered better covered by the cnc onlywhen it is encountered twice as frequently on the cnc as on the BBS.
recalculated each year; thus, when the CBC analyses can

be done, we will have an unprecedented amount of

quantitative information on thc population status and

trends of North American birds. This information will

be of tremendous value for establishing priorities for

North American bird conservation~In addition, the data

will prove useful in improving our understanding of the

population biology of North American birds.

Acknowledgments

The Christmas Bird Count Population Project at

Cornel1 has been generously supported by J. KixMiI1er,

G. W. Knight, N. B. Delavan Foundation, E. I. du Pont

de Nemours and Company, Exxon Corporation, FMC

Corporation, IBM Corporation, Johnson Foundation

(Trust), MARS Inc., Merck Family Fund, Williams

Companies Foundation Inc., and many others. J. Lowe

helped with data improvements and summaries; he

prepared the figures. T. Engstrom, S. Droege, J. Trapp,

and an anonymous reviewer helped to improve the

manuscript. In addition, I would like to thank the many

CBCvolunteers who colIected, recorded, and edited the

data; the National Audubon Society and the staff of

American Birds who coordinate the CBC and publish

the results; and C. Bock and J. Shipman, who provided

computerized CBC data.

Literature Cited

Aldrich, J. W. 1%3. Life areas of North America. J. Wildl.

Manage. 27:530-531.

Arbib, R. S. 1981. The Christmas Bird Count: constructing an

"ideal model." Pages 30-33 in C. J. Ralph and J. M. Scott,

eds. Estimating numbers of terrestrial birds. Stud. Avian

BioI. 6.

Bock, C. E., and T. L. Root. 1981. The Christmas Bird Count

and avian ecology. Pages 17-23 in C. J. Ralph and J. M.

Scott, eds. Estimating numbers of terrestrial birds. Stud.

Avian BioI. 6.

Bureau of Agricultural Economics. 1933. Natural land use

areas of the United States. U.S. Dep. Agric., Bur. Agric.

Econ.

Butcher, G. S. 1986. Populations of black ducks and mallards

in winter, 1950-1983. Report to U.S. Fish and Wildlife

Service, WaShington, D.C.

Butcher, G. S., and M. R. Fuller. 1986. Bird populations in

winter and summer: an evaluation of the Christmas Bird

Count and a comparison with the Breeding Bird Survey.

Report to U.S. Fish and Wildlife Service, Washington, D.C.

AVlAo,-" POPULATIOS TREl'iDS 13

Butcher, G. S., and C. E. McCulloch. 1990. Intluence of

observer effort on the number of individual birds recorded

on Christmas Bird Counts. Pages 5-13 in J. R. Sauer and

S. Droege, eds. Survey designs and statistical methods for

the estimation of avian population trends. U.S. Fish WildI.

Serv., BioI. Rep. 90( 1). 166 pp.

Bystrak, D. 1981. The North American Breeding Bird Survey.

Pages 34-41 in C. J. Ralph and J. M. Scott, eds. Estimating

numbers of terrestrial birds. Stud. Avian BioI. 6.

Chapman, F. M. 1900. A Christmas bird-census. Bird-Lore

2:192.

Daniels, G. G. 1975. An inquiry into Christmas Bird Count

Accipiter reports. Am. Birds 29:634-637.

Dunn, E. H. 1986. Feeder counts and winter bird population

trends. Am. Birds 40:61--66.

Erskine, A. J. 1978. The first ten years of the cooperative

Breeding Bird Survey in Canada. can. Wildl. Servo Rep.

Ser. 42. 61 pp.

Fenneman, N. M. 1931. Physiography of Western United

States. McGraw-Hill, New York.

Fenneman, N. M. 1938. Physiography of Eastern United

States. McGraw-Hill, New York.

Ferner, J. W. 1984. The Audubon Christmas Count as a

method of monitoring nongame bird populations. Pages

332-341 in W. C. McComb, ed. Proceedings of the

Workshop on Management of Nongame Species and

Ecological Communities, University of Kentucky,

Lexington.

Haney, J. C. 1983. The usc of reference species as a technique

in evaluating Christmas Bird Count data. Am. Birds

37:363-364.

James, D. 1962. The changing seasons. Audubon Field Notes

16:306-311.

James, D. 1963. The changing seasons. Audubon Field Notes

17:300-304.

Kuchler, A. W. 1964. Potential natural vegetation of the

conterminous United States: manual to accompany the

map. American Geographical Society, New York.

Mark, D. M. 1981. Thayer's gulls from western Christmas Bird

Counts: a cautionary note. Am. Birds 35:898-900.

Raynor, G. S. 1975. Techniques for evaluating and analyzing

Christmas Bird Count data. Am. Birds 29:626-633.

Robbins, C. S. 1970. Winter bird survey technique tested in

Maryland. Md. Birdlife 26: 11-20.

Robbins, C. S. 1978. Census techniques for forest birds. Pages

142-163 in Proceedings of the Management of Southern

Forests for Nongame Birds Workshop, Atlanta, Ga.

U.S. For. Serv., Gen. Tech. Rep. SE-14.

Robbins, C. S., and D. Bystrak. 1974. The winter bird survey

of central Maryland, USA. Acta Ornithol. 14:254-271.

Robbins, C. S., D. Bystrak, and P. H. Geis.~ler. 1986. The

Breeding Bird Survey: its first fifteen years, 1965-1979.

U.S. Fish Wild!. Serv., Resour. Pub!. 157. 196 pp.

Wilds, C. 1980. The Washington, D.C., Christmas Bird Count

as an indicator of environmental change. At I. Nat.

33: 10-11.
14 BIOLOGICAL REPORT 90(1)

Description of the Wisconsin Checklist Project

by

Stanley A. Temple and John R. Cary

Department ofWildlife Ecology

University ofWisconsin

Madison, Wisconsin 53706

Introduction

A birding checklist is a simple record of the species

of birds that a field observer found at a particular place

and time. This form of record-keeping has long been a

standard practice among American bird watchers

(Hickey 1943). Checklists have traditionally provided

information on geographic ranges and seasonal

occurrences of migrants. Only recently have checklists

been used to monitor temporal and spatial variation in

the abundance of birds (Temple and Temple 1976). In

theory, when many observers in an area keep concurrent

checklist records, their checklists will differ because of

differences in the abundance ofspecies and in observer

effort. Some species will be reported on every observer's

checklist, whereas other species will occur on those of

only a few observers. The reporting frequency for each

species in an area can be calculated as the percentage

of checklists on which the species has been reported.

Reporting frequencies are assumed to be primarily a

function of each species' relative abundance. A high

reporting frequency indicates that a species is relatively

abundant, whereas a low reporting frequency indicates

that a species is relatively uncommon.

To test whether or not an analysis of systematically

maintained checklist records could actually be used to

monitor variations in the abundance and distribution of

bird species, the Wisconsin Society for Ornithology

(WSO), under the guidance of S. A. Temple, began the

Wisconsin Checklist Project in 1982 (Temple 1982).

This paper described the details of how the Wisconsin

Checklist Project operated and summarized briefly

some of the project's major accomplishments.

Data Collection

The first and most important requirement for using

checklist records to monitor bird populations is a

group of reliable and competent field observers who

are willing to maintain systematic records. In

Wisconsin, many such skilled individuals are members

of the WSO, an organization with a long tradition of

volunteer participation in ornithological research

programs. In response to the call for participants

(Temple 1982), 431 of the approximately 1,200 WSO

members have volunteered to take part in the

Wisconsin Checklist Project. Of these volunteers, 257

have been regular contributors to the project; some

have submitted records for every reporting period

since the project began.

We asked participants to keep careful records of the

bird species that they detected during each week of the

year and in which county they had observed them. We

provided them with special forms on which to record

information for each week (Fig. 1). These forms are

designed so that information recorded on them can be

read directly by an optical scanning device. This

innovation made it possible for us to transfer large

amounts of information rapidly and easily from the

forms to magnetic tapes and disks that could then be

used by computers.

On the weekly reporting forms, participants

identified themselves and provided information on the

county in which they birded during the recording week,

the date of the Sunday that began the recording week,

and the level of intensity with which they searched for

birds. They then indicated which of 265 bird species they

had detected during the week by filling in the "bubble"

adjacent to the bird's name. They were not asked to

provide any assessment, either objective or subjecti~e,

ofthe abundance ofthe birds they detected. Completed

forms were submitted for annual analysis. Since the

project began in 1982we have received more than 30,000

weekly checklist forms- about 6,000 forms each year.

Analysis ofRecords

After the forms were scanned and the information

converted to a computer-accessible format, we

checked records for accuracy. We looked for
AVIAN POPULATION TRENDS 15

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Fig. 1. A sample of the optically-scannable checklist form on which volunteers recorded their observations.
16 BIOLOGICAL REPORT 90(1)

misspellings of an observer's name, erroneous dates

for the start of the recording week, nonexistent county

codes, and highly improbable reports of a bird species

outside o! its normal range or season of occurrence in

the State. Errors of the last type were of most concern

to us, and when they were found, we deleted them from

subsequent analysis. Over the first 5 years of the

project we caught 78 such suspicious records. Most of

those seemed to have been simply the result of a

participant accidentally filling in the wrong bubble on

the form. For example, we looked on with suspicion

and deleted a July record of a gray jay (Perisoreus

canadensis) from a southern Wisconsin county (far

beyond the jay's normal range), especially because

during that week the observer had failed to report a

blue jay (Cyanocitta cristata; the region's common jay).

The two jay species appear next to one another on the

checklist form, so we suspected a simple slip of the

pen. To double-check the accuracy ofthe data, we also

prepared yearly summaries of each participant's

reports and returned these summaries to the

participant for review. This review process allowed the

original observer to help us further in detecting and

correcting erroneous records.

Our analyses of the edited data followed several

paths. In order to detect seasonal variation in

abundance of birds in the State, we calculated weekly

reporting frequencies for each species. Results of this

type of analysis have been presented by Temple and

Temple (1984) and Temple and Cary (1987a). An

example is shown in Fig. 2.

In order to detect geographical variation in the

abundance of birds, we divided the State into 43

Northern Wisconsin

100%

50%

o F M A M 0 0 A SON 0

Southern Wisconsin

100%

50%

o F M A M 0 0 A SON 0

Fig. 2. An example ofthe seasonal variation in abundance ofthe

ruby-crowned kinglet (Regulus satrapa) keep in Wisconsin,

as revealed by an analysis of checklist records.

regions, each of which included an adequate number

of observers and checklists. We calculated each

species' reporting frequency in each region over a

specified period, such as the breeding season or the

entire year. Temple and Temple (1987) and Temple

and Cary (1987b) presented some preliminary results

of these types of analyses. An expanded analysis of

trends in relative abundance revealed by the checklist

records is included elsewhere in this volume (Temple

and Cary 1990). An example is shown in Fig. 3.

ImportantAssumptions

The Wisconsin Checklist Project is based on a

number of assumptions that underlie the use ofthe data

to detect variations in abundance.

• Volunteer observers are accurate in their reporting

habits, following instructionsrigidly and doing their best

to optimize their efforts to detect birds.

• Reporting frequencies are a function ofthe relative

abundance of birds. Temple and Cary (1990) address

this assumption later in this volume.

• Reporting frequencies are more affected by

changes in abundance than they are by other factors

(e.g., the behaviors of birds or observers) because ofthe

dynamic mixing of these other effects.

• At least over the short term, the efforts of the

observers remain relatively similar between years (i.e.,

their birding habits and skills change slowly).

D absent

< 2.7 %

~2.7-57 %

~> 57 %

Fig. 3. An example of the geographic pattern of relative

abundance of the common raven (Corvus corm:) keep, as

revealed by an analysis of checklist records; shading

categories portray reporting frequencies.
Conclusions

Our experiences with the Wisconsin Checklist

Project convince us that the approach has considerable

merit. The technique is readily accepted by volunteer

cooperators, and it produces interpretable data

reflecting seasonal, geographic, and year-to-year

variationsin bird populations. Temple and Cary (1987a)

produced a book that presented many of the results of

the project in a format that has proved useful and

appealing to bird watchers, thus demonstrating to

volunteers that their efforts have been worthwhile.

Acknowledgments

We are grateful to the 431 members ofthe Wisconsin

Society for Ornithology who have submitted checklist

records during the first 5 years of the Wisconsin

Checklist Project. A. J. Temple kept track of these

volunteers and the nearly 30,000 checklists they

submitted for analysis. The Wisconsin Checklist Project

received financial support from the A. W. Schorger

Fund ofthe Department ofWildlife Ecology, University

of Wisconsin - Madison.

AVIAN POPULATION TRENDS 17

Literature Cited

Hickey, J. J. 1943. Aguide to bird watching. Oxford Univcrsity

Press, London, U.K. 262 pp.

Temple, S. A 1982.-A Wisconsin bird survey based on field

checklist information: a WSO research project. Passenger

Pigeon 44:56-60.

Temple, S. A, and J. R. Cary. 1987a. Wisconsin birds: a

seasonal and geographical guide. University of Wisconsin

Press, Madison. 364 pp.

Temple, S. A, and J. R. Cary. 1987b. Climatic effects on

year-to-year variations in migration phenology: a WSO

research project. Passenger Pigeon 49:70-75.

Temple, S. A, and J. R. Cary. 1990. Using checklist records to

reveal trends in bird populations. Pages 98-104 in J. R.

Sauer and S. Droege, eds. Survey designs and statistical

methods for the evaluation ofavian population trends. U.S.

Fish Wild\. Serv., Bioi Rep. 90(1).166 pp.

Temple, S. A, and A J. Temple. 1984. Results of using

checklist records to monitor Wisconsin birds: a WSO

research project. Passenger Pigeon 46:61-70.

Temple, S. A., and A. J. Temple. 1986a. Geographic

distributions and patterns of relative abundance of

Wisconsin birds: a WSO research project. Passenger

Pigeon 48:58-68.

Temple, S. A, and A J. Temple. 1986b. Year-to-year changes

in the abundance of Wisconsin birds: results of the WSO

checklist project. Passenger Pigeon 48: 158-162.

Temple, S. A, and B. L. Temple. 1976. Avian population

trends in central New York State, 1935-72. Bird Banding

47:238-257.
18 BIOLOGICAI_ REPORT 90(1)

Use of Breeding Bird Atlases to Monitor Population Change

by

Chandler S. Robbins

U.S. Fish and Wildlife Service

Patuxent Wildlife Research Center

Laurel, Maryland 20708

Introduction

Breeding Bird Atlases are cooperative projects

designed to map the breeding distributions of all species

of birds within a prescribed area, such as a county, a

Nation, or even a continent, within a defined period of

years. Mapping is done using a grid of usually 5 or 10

km, and either every block in the grid or a random or

systematic sample of blocks is visited, with the intent of

recording presence or absence of each species during

the nesting season. Primary emphasis is on determining

if a species is present and assigning the highest category

of nesting evidence: Possible, Probable, or Confirmed.

The categories of evidence under each of these three

terms were defined in the first major grid-based avian

atlas (Sharrock 1976), endorsed with minor alterations

by the European Ornithological Atlas Committee in

1974, and accepted, again with very minor alterations,

by the Northeastern Breeding Bird Atlas Conference in

Woodstock, Vermont, in 1981, and by the North

American Ornithological Atlas Committee in 1987.

The original purposes ofBreeding Bird Atlases were

to map breeding distribution on a fine scale in such a

way that future expansions and contractions of range

could be documented. Additional benefits identified

with the early atlases were: discovery of ecologically rich

or unique habitats that deserve preservation, location of

new sites for rare and endangered species,

accumulation of a data base for further research, and

provision of a useful and exciting project that would

involve amateur birders during a season when they often

are not heavily involved with other birding activities.

History

The concept of grid-based natural history atlases

originated in the British Isles with the publication in

1962 of Perring and Walters' atlas of the British flora,

which used the national lO-km grid. The Biological

Records Centre, Abbots Ripton, England,subsequently

promoted the systematic mapping of flora and fauna

throughout the British Isles and assisted many other

European countries in initiating atlas projects.

After a trial avian atlas project in three British

counties (Lord and Munns 1970), the British Trust for

Ornithology undertook a 5-year atlas ofbreeding birds

in Britain and Ireland (Sharrock 1976). This was a

tremendous effort that included visits to all 3,862

10-km blocks in the British Isles and involved more

than 10,000 participants. Not only did this atlas

provide direct comparisons with the botanical atlas,

but also the bird atlas came with 12 habitat and climatic

overlays that greatly assisted interpretation of the bird

distribution maps.

The European Ornithological Atlas Committee was

formed in 1971 to standardize methodology and provide

assistance to other Nations. This committee adopted

standardized codes in 1972. During the next decade

national atlases were published for France, Denmark,

West Germany, Netherlands, and Switzerland, as well

as regional atlases for parts of England, Germany, and

Spain. At least two African atlases have been published

(Cyrus and Robson 1980; Nikolaus 1987) as well as a

national atlas for New Zealand (Bull et a1. 1985), a

national atlas for Australia (Blakers et a1. 1984), and

some more-detailed regional atlases for parts of

Australia (Thomas 1979; Emerson et a1. 1987).

Atlasing in the United States began with a

preliminary mapping of bird distribution in Montana by

its 47 one-degree blocks oflatitude and longitude (Skaar

1975). In the same year R. Stewart (1975) published a

much more detailed book on North Dakota breeding

birds based on townships (6 X 6 mi = about 9.7-km

grid); although he did not use atlas categories and

procedures, he personally visited nearly all townships in

the State and mapped every breeding species by

township. The first published North American atlas to

use the standard atlas codes and procedures was of two

adjacent counties in Maryland (Klimkiewicz and Solem

1978). Currently, State and Provincial atlases have been
published for Maine (Adamus 1988), New York

(Andrle and Carroll 1988), Ontario (Cadman et al.

1988), and Vermont (Laughlin and Kibbe 1985). Atlas

projects are in progress or completed for several

Western States, all Canadian Provinces, and for all

States east of the Mississippi River except Mississippi,

Alabama, South Carolina, and Wisconsin. For more

details on the history of atlasing see Robbins (1982).

Procedures

Organization and Recording Forms

Most atlas projects are sponsored by a State or

provincial ornithological or Audubon society in

collaboration with the conservation department. In

most States there is a full-time salaried State

coordinator for the duration of the fieldwork and

manuscript preparation. A network of volunteer county

coordinators solicits, trains, and encourages

cooperators as work progresses.

Participants are supplied with a map of their block

and a handbook. The handbook contains instructions,

explains codes, and often includes a table of "safe dates"

during which one can assume birds of that species are

on nesting territory. Definitions for all the codes are

given in Sharrock (1976), Laughlin (1982), and the North

American Atlas Handbook (Smith, in preparation).

Newsletters are mailed to participants once or twice

each year to maintain their interest by reporting

progress, providing helpful suggestions, and cautioning

against common errors.

Forms vary from project to project because each

State or Province has its own list of breeding species.

The field cards have columns for the Possible, Probable,

and Confirmed codes. Generally there is an annual

summary sheet that is appropriately coded for data

entry. Some States and Provinces have additional

columns for numerical estimates, dates of observations,

or quarter-block designation.

Grid Size

The original lO-km grid of the British Isles was

adopted in much of western Europe except in the small

countries, which used a 5-km grid, and in countries such

as France, Spain, and Portugal, which had national

topographic maps to their own unique" scales. Canada

adopted a lO-km grid, but most of the eastern United

States has used a close approximation to a 5-km grid. In

many northeastern States (Connecticut, Delaware,

Maryland, Massachusetts, New York, Pennsylvania, and

Rhode Island) every 5-km block is visited. In others

(Illinois, New Hampshire, Vermont, Virginia, West

Virginia, and many others), a random or systematic

AVIAN POPULATION TRENDS 19

sample of blocks is visited. In addition, some other

blocks are targeted for special biological or politicaI

reasons, but this special set is not included in the

statistical summary of results. A few States (Maine and

Texas) are using entire 7.5-min topographic maps as

their grid; but using such a coarse scale will make it

difficult to detect future changes. Even if 90% of the

habitat in a block were to disappear, the remaining 10%

probably would retain at least one pair of most or all of

the original species. For this reason, the North American

BreedingBirdA tlas Handbook recommends a 5-km grid

for the United States, even if this means covering only a

sample of atlas blocks instead of all of them. A random

or systematic sample of 2.5-km quarter-blocks is

recommended to enhance opportunities of detecting

future changes or to correlate bird distribution with

other environmental variables. Quarter-blocks are

discussed further in connection with applications.

Field Techniques

Participants are urged to study their topographic

map and to visit all habitats that occur in their block.

They are urged to concentrate their fieldwork in the

early morning when most species reach their peak of

activity. Participants also are encouraged to make some

nocturnal trips for species that are most active at night.

Initial efforts are to record as many species as possible.

On subsequent trips an attempt is made to upgrade each

species from Possible to Probable status or from

Probable to Confirmed. Most observers continue to visit

their block until additional trips provide few new species

and few upgrades of status.

Observers are encouraged to keep working within

a block until they have found at least 75% of the

expected species. Coordinators in many States try to

estimate the number of species that occur in each

block, or at least an average number for the State or

Province. Some States have a goal of getting

confirmation for 50% of the species recorded in each

block; other States have put more emphasis on

Probable records and try to get 75% ofthe species into

Probable or Confirmed status. For much of the East

there has been a goal of 70 or 75 spec'ies, with no more

than a quarter of these in Possible status.

Some atlas projects have set a minimum goal of 16 h

of coverage. Because of differences in observer

expertise, either a species goal or reaching the limit of

one's competence (failing to add other species or to

achieve additional upgrades) seems more realistic.

Numerical E5timates

There is no compulsory procedure for estimating the

number of birds of each species within atlas blocks. The
20 BIOLOGICAL REPORT 90(1)

North American Breeding Bird Atlas Handbook (Smith, in

preparation) lists various methods that have been used.

For the United States and southern Canada, Breeding

BirdSurvey(BBS) data for the more common species give

index values by physiographic regions within each State

and Province. These indices should provide better

comparisons with future years than estimates of actual

numbers made by unskilled amateurs.

Miniroutes (Bystrak 1980) were developed

specifically for adding a quantitative dimension to atlas

studies. These are abbreviated BBS routes of 15 to 25

standardized 3-min counts at half-mile intervals along

secondary roads. The counts must be made by

experienced observers who are familiar with songs and

calls of all species likely to be encountered. When

miniroutes are run in all blocks or in a specified sample

of atlas blocks, they can provide an index of either

abundance or distribution (depending on whether birds

are counted or just checked for presence and absence).

For a discussion of application of miniroutes see

Robbins and Dowell (1986).

The atlases of France (Yeatman 1976) and Ontario

(Cadman et al. 1988) have used observer estimates of

number of pairs of each species in each block. These

estimates are made in powers of 10: 1, 2-10, 11-100,

101-1,000, and so forth. The estimates required

considerable interaction between coordinators and

observers because of a strong tendency to

underestimate the number of birds in an atlas block.

The British, in their winter atlas (Lack 1986),

successfully used actual counts obtained per 6 h of

fieldwork in winter, and these counts were adjusted by

use of a conspicuousness factor that was generated for

each species. So far, no similar procedure has been tried

in North America.

Editing and Computerization

Records are submitted annually at the close of the

breeding season, through the county coordinators to the

atlas coordinator, for critical review before data entry.

After computer editing, listings and maps are prepared

and the data reviewed again at various levels. After

fieldwork is completed, species accounts to accompany

the maps are written by selected authors.

Standardization

General procedures, including codes and

terminology, have been fairly well standardized from the

beginning. With a few exceptions the grid used has been

close to 10 km or 5 km, depending largely on the size of

the country, State, or Province. However, it has not been

feasible to adopt the Universal Transverse Mercator

(UTM) grid worldwide, as some had hoped. The

number of years of fieldwork has usually been 5 or 6.

So far, numerical treatment has not been

standardized, In some countries extensive bird

population information from other sources, such as

counts of colonial waterbirds or breeding densities by

habitats, has been incorporated into the atlas book. At

the other extreme, some atlases have made no attempt

to present numerical data beyond the percentage of

blocks with each species.

The text accompanying the atlas maps differs widely

in scope and content, ranging from no species text at all

in the Maine atlas to major State bird books that include

all species (not just breeding birds) in Delaware and

Massachusetts (in various stages of compilation).

Applications

Mapping Range Expansions and Contractions

This continues to be one of the major objectives of

atlas programs. Atlas work already has demonstrated

the potential for showing changes in breeding

distribution at the county as well as the State level. In

Maryland, for example, the 198J-87 atlas showed major

changesin many species from the range maps published

by Stewart and Robbins (1958), These changes included

more than doubling of the Maryland breeding range of

the tree swallow (Tachycineta bic%r), fish crow (COl'\lUS

ossifragus), golden-crowned kinglet (Regu/us satrapa),

veery (Catharus fuscescens), Nashville warbler

(Vennivora mjicapilla), blue-winged warbler (V. pinus),

and swamp sparrow (Me/ospiza georgiana); substantial

increases in breeding range of the mallard (Anas

p/atyrhynchos), black vulture (Coragyps atratus), cliff

swallow (Himndo pyrrhonota), and blue grosbeak

(Guiraca caem/ea); and extirpation ofthe yellow-bellied

sapsucker (Sphyrapicus varius) , Bewick's wren

(Thryomanes bewickii), Bachman's sparrow (Aimophi/a

aestiva/is), and the native peregrine falcon (Fa/co

peregrinus). In addition, the Canada goose (Brama

canadensis) and the house finch (Carpodacus

mexicanus), unknown as Maryland breeders in the

1950's, now nest in almost every county.

Detecting and Moniton'ng Population Changes

The BBS does a great job of monitoring the common

roadside species by major regions of the continent, but

it cannot be depended on at the county level, where

sample sizes are too small,

The two atlases of Howard County, Maryland, have

demonstrated that even over a period as short as 10

years (1973-75 to 1983-87) many changes in bird

distribution and abundance occur, and these can easily
be detected with a quarter-block (2.5-km) grid, even

without the use of numerical counts. It is hard to obtain

a meaningful estimate of the number of hours spent on

atlas fieldwork, especially when atlasers reside in the

block to which they are assigned, or make casual

observations on the way to work each day. Thus, total

hours reported spent on atlasing is not as good a basis

for comparison over a period of years as the sum of the

bird lists for all atlas blocks in the county. In the Howard

County example, the total number of records (sum of

the species lists) for the 136 quarter-blocks in 1973-75

was 8,297, compared to 9,683 for 1983-87, a 16.7%

increase. This increase in efficiency of coverage (1.167)

was multiplied by the number of blocks in which a

species was recorded during the first atlas period to

obtain an estimate ofthe expected number of blocks for

the second period, assuming no change in population.

A major departure from this estimate indicates an

increase or decrease in the local population of the

species in question. Thirty-four Howard County species

showed significant changes by chi-square tests

(P < 0.05); a few of these are shown in the Table. The

entire list appears in Acta Zoologica Fe/llzica (Robbins

et al. 1989).

By breaking a county into regions, or by combining

data from several nearby counties, one can apply

nonparametric statistics and assign confidence limits to

the probability of change. Use of miniroutes, which is

recommended in the N011h Americall Breedillg Bird

Atlas Halldbook (Smith, in preparation), will further

AVIA.1IJ POPULATION TRENDS 21

increase the potential for detecting changes within a

State, Province, or county.

OtherApplications

There are many other applications of atlas data that

were not conceived when atlas programs began. These

include:

• documenting the effects of habitat fragmentation,

• defining boundaries of ecological regions (bird

districts) on the basis of their bird populations,

• land use planning so as to preserve areas ofspecial

conservation value,

• detailed correlation of bird distribution with forest

cover types and other forms of land use,

• compilation of automated data banks that can be

used for a multitude of research and conservation

applications,

• providing an impartial means of defining rarity, and

• combining data from many adjacent States and

Provinces to obtain a larger perspective on current

population status and on changes in distribution and

abundance.

The Future

Atlasing has proven to be extremely popular among

tens of thousands of amateurs, who give freely of their

time and expertise. Not only are many of the most

sophisticated amateurs in North America contributing

their talents, but also they are training countless

Table. Challges ill lIumber of quarter-blocks ill Howard COUllty, Marylalld, where selected species were detected ill

1973-75 alld 1983-87. Expected values for 1983-87 are 1.167 times the 1973-75 values, represellting the increase

ill totallllll1zber ofrecords received.

1973-75 1983-87 1983-87

Species Observed Expected Observed

Increases

Canada goose (Branta canadensis) 8 9 46

Mallard (Anas platyrhynchos) 49 57 86

Black vulture (Coragyps atratllS) 18 21 73

Pileated woodpecker (DryOCOPllS pileatllS) 29 34 86

Cliffswallow (Hinmdo pyrrhonota) 6 7 20

Hooded warbler (Wilsonia citrina) 43 50 74

House finch (CarpodaClls mexicanlls) 0 0 129

Decreases

American black duck (Anas rnbripes) 10 12 3

Whip-poor-will (Caprimulgus vocifems) 30 35 21

Horned lark (Eremophila alpestris) 47 55 18

Bank swallow (Riparia riparia) 9 11 2

Vesper sparrow (Pooecetes graminells) 51 60 22

Grasshopper sparrow (Ammodramus savannarnm) 103 121 78

Eastern meadowlark (StIlmella magna) 126 147 103

Change

(%)

+411%

+ 51%

+248%

+ 153%

+186%

+ 48%

-75%

-40%

-(,7%

-82%

-(,3%

-36%

-30%
22 BIOLOGICAL REPORT 90(1)

thousands in the techniques ofatlasing and are diverting

the attention of many birders into serious ornithological

data gathering. The rather crude atlasing of today will

probably be followed by more sophisticated atlas

endeavors in the future, with a more quantitative

approach. I expect atlases of the next century to be as

much advanced over today's atlas endeavors as today's

Christmas Bird Counts are over the initial published

Christmas Bird Censuses of the early 1900's.

Acknowledgments

I thank the members of the Howard County Chapter

ofthe Maryland Ornithological Society, especially D. C.

Dupree, J. Farrell, M. K. Klimkiewicz, and J. K. Solem,

for the intensive atlas coverage of their county during

1973--75 and 1983-87. I also thank S. Droege, J. R.

Sauer, and C. R. Smith for their constructive comments

on the manuscript.

Literature Cited

Adamus, P. R. 1988. Atlas of the breeding birds in Maine,

1978-1983. Maine Department of Inland Fisheries and

Wildlife, Augusta. 366 pp.

Andrle, R. F., and J. R. Carrol!. 1988. The atlas of breeding

birds in New York State. Cornell Laboratory of

Ornithology, Ithaca, N.Y. 551 pp.

Blakers, M., S. J. J. F. Davies, and P. N. Reilly. 1984. The atlas

of Australian birds. Royal Australasian Ornithologists

Union. 738 pp.

Bull,P. c., P. D. Gaze, and c.J. R. Robertson. 1985. The atlas

of bird distribution in New Zealand. Ornithological Society

of New Zealand, Wellington North. 2% pp.

Bystrak, D. 1980. Application ofminiroutes to bird population

studies. Md. Birdlife 36:131-138.

Cadman, M. D., P. F. J. Eagles, and F. M. Helleiner. 1987.

Atlas of the breeding birds of Ontario. University of

Waterloo Press, Waterloo, Ont. 617 pp.

Cyrus, D., and N. Robson. 1980. Bird atlas ofNata!. University

of Natal Press, Pietermaritzburg. 320 pp.

Emerson, W. B., C. M. Beardsell, R. I. Normand, and R. H.

Lyon. 1987. Atlas of Victoria birds. Melbourne, Australia.

271 pp.

K1imkiewicz, M. K., and J. K. Solem. 1978. The breeding bird

atlas of Montgomery and Howard Counties, Maryland.

Md. Birdlife 34:3-39.

Lack, P. 1986. The atlas of wintering birds in Britain and

Ireland. British Trust for Ornithology, Tring, U.K.,447 pp.

Laughlin, S. B., editor. 1982. Proceedings ofthe northeastern

breeding bird atlas conference. Vermont Institute of

Natural Science, Woodstock. 122 pp.

Laughlin, S. B., and D. P. Kibbe. 1985. The atlas of breeding

birds of Vermont. Vermont Institute of Natural Science,

Woodstock. 456 pp.

Lord,J., and D. J. Munns. 1970. Atlas of breeding birds ofthe

west Midlands. Collins, London, U.K. 276 pp.

Nikolaus, G. 1987. Distributional atlas of Sudan's birds with

notes on habitat and status. Bonner Zoo!. Monographien

25, Bonn, West Germany. 322 pp.

Perring, F. H., and S. M. Walters. 1%2. Atlas of the British

flora. Botanical Society of the British Isles. T. Nelson,

London, U.K. 432 pp.

Robbins, C. S. 1982. Overview ofinternational atlasing. Pages

3-10 in S. B. Laughlin, ed. Proceedings ofthe Northeastern

Breeding Bird Atlas Conference, Vermont Institute of

Natural Science, Woodstock.

Robbins, C. S., and B. A. Dowell. 1986. Use ofminiroutes and

Breeding Bird Survey data to estimate abundance. Pages

28-40 in S. M. Sutcliffe, R. E. Bonney,Jr., and J. D. Lowe,

eds. Proceedings of the Second Northeastern Breeding

Bird Atlas Conference, Laboratory ofOrnithology, Cornell

University, Ithaca, N.Y.

Robbins, C. S., S. Droege, and J. R. Sauer. 1989. Monitoring

bird populations with Breeding Bird Survey and atlas data.

Ann. Zoo!. Fennici. 26:297-304.

Sharrock, J. T. R. 1976. The atlas of breeding birds in Britain

and Ireland. British Trust for Ornithology, Tring, UK.

477 pp.

Skaar, P. D. 1975. Montana bird distribution. P. D. Skaar

(privately printed), Bozeman, Mont.

Stewart, R. E. 1975. Breeding birds of North Dakota.

Tri-college Center for Environmental Studies, Fargo,

N. Dak. 295 pp.

Stewart, R. E., and C. S. Robbins. 1958. Birds ofMaryland and

the District of Columbia. U.S. Fish Wildl. Serv., N. Am.

Fauna 62. 401 pp.

Thomas, D. 1979. Tasmanian bird atlas. Fauna of Tasmania

Handbook 2, University of Tasmania, Australia. 171 pp.

Yeatman, 1... 1976. Atlas des oiseaux nicheur~ de France. Soc.

Ornithologique de France, Paris. 282 pp.
AVIAN POPULATION TRENDS 23

Methodology of the International Shorebird Survey and

Constraints on Trend Analysis

by

Marshall A. Howe

u.s. Fish and Wildlife Service

Patuxent Wildlife Research Center

Laurel, Maryland 20708

Origin and Extent of the Survey

The International Shorebird Survey (ISS), managed

by Manomet Bird Observatory (Massachusetts) since

1972, enlists the services of volunteer observers to

conduct surveys of migrating shorebirds in the Western

Hemisphere (Manomet Bird Observatory 1980). In

1974, the definition ofISS was broadened to include the

Maritimes Shorebird Survey (MSS; Morrison 1976)

conducted during the 1970's by the Canadian Wildlife

Service. However, because of difficulties in obtaining

the MSS data, the ISS includes only the Manomet survey

for the purposes of the present discussion. The purpose

of the ISS has always been to identify and document

areas of major importance to shorebirds during fall

migration (in recent years some spring surveys have

been conducted). The ISS has focused on the eastern

United States, especially between Maine and North

Carolina, but includes sites as distant as Kansas, the

Caribbean, and Latin America.

Design ofthe Survey

Participants in the ISS are recruited on the basis of

recommendationsfrom persons known to be experienced

at shorebird identification. In this way, a reasonable level

of quality control is assured. Unlike the Breeding Bird

Survey and some other surveys, sampling areas in the ISS

are not preselected randomly. In part, this is because the

surveywas not designed to obtain estimates of population

change. Furthermore, the difficulty of access to many

wetland habitats used by shorebirds makes random

selection impractical. Observers are encouraged to select

sites on the basis of traditional shorebird use and

convenience of access. A special effort is made to ensure

coverage of sites known to attract large numbers of

shorebirds every year. Observers are asked to derme the

limits of the survey site and visit it at lca~t three times per

month between 1 July and 31 October, the period that

encompasses the majority of the migration period for

most species. Few observers achieve such intensive

coverage, however, and many sites are surveyed only a few

times annually or missed entirely in some years. The total

number of each species is determined by either direct

count or subjective estimation and coded appropriately.

In tidal areas there is an effort to conduct the survey

during the same segment ofthe tide cycle to minimize the

influence of local movements in response to tide

fluctuations. Each survey is summarized on forms

provided by Manomet Bird Observatory. The forms are

then checked and edited by Manomet staff, and the data

are computerized.

To date, more than 600 volunteers have collected

data for the ISS at more than 500 sites in the United

States. Annually, an average of 1,500 surveys at 150 sites

has been conducted. Because turnover rates of

migrating birds at different sites are poorly known, it is

difficult to estimate the total number of shorebirds

censused annually. In a recent analysis of only 64

Atlantic coastal sites, the minimum annual estimate

(assuming no turnover) for the commonest species, the

semipalmated sandpiper (Calidris pusilla), was 34,500

(Howe et al. 1989). The actual number observed at this

subset of sites was probably 3-4 times this number.

International Shorebird Survey sampling design and

certain properties of the data present difficulties for

population trend estimation. I summarize those

difficulties and evaluate the degree to which they can be

overcome. Some of these issues are addressed in more

detail elsewhere (Howe et al. 1989).

Problems Associated with Sampling

Design

To be suitable for trend analysis, the data must meet

the assumption that the birds sampled are

representative ofthe total population or some definable
24 BIOLOGICAL REPORT 90(1)

subset. Although the nonrandom distribution of ISS

sites threatens this assumption, there are potentially

more serious problems. For example, many species

prefer inland, freshwater sites, opportunistically

selectingwetlandsthat have suitable water conditions at

any given time. Also, the number of ISS sites is limited,

and the influence ofmajor stopovers not included in the

network is unknown.

Finally, during migration, shorebirds move through

stopover sites at varying rates determined by

physiological and climatological factors. Major flights

could be overlooked if the interval between samples is

too long. All of these factors potentially influence the

accuracy and precision of ISS counts, introducing

variation that is independent oftrue population change.

By accepting certain assumptions and using the ISS

data least sensitive to the sources of variation listed

previously, it seems that these concerns can be

minimized. In a preliminary study (Howe et al. 1989) we

confined trend analyses to a subset of 12 species we .

believe to be obligate users of coastal habitats

(eliminating opportunistic species) and examined only

ISS sites on the Atlantic coast. We felt that this approach

would reduce the risk of overlooking major segments of

the population. Concern about the nonrandom

distribution ofsites is allayed somewhat by our present

knowledge of the ecology of migrating shorebirds.

Several studies (e.g., Smith and Houghton 1984; B. A.

Harrington, unpublished data) showed that at least

some individuals ofsome species showed between-year

fidelity to migration stopovers. Also, many stopover

sites attract large numbers of shorebirds annually. If

traditional use ofthese sites is the rule for many species

of shorebirds, our confidence in the sample counts as

indicators oftrue population trendsis greatly increased.

With regard to the temporal aspect of migration,

analysis ofISS data shows that most species exhibit fairly

sharp population peaks. If none of the samples occurs

during the peak period, the populationsfor that site will

be seriously underestimated. Several studies of

semipalmated sandpipers (Page and Middleton 1972;

Lank 1983; Dunn et al. 1988) suggested that, early in

southbound migration, lengths ofstayofindividual birds

at stopovers averaged 2 to 3 weeks. Ifwe assume this is

typical of migrating shorebirds, a regime ofsampling at

10-day intervals should on average yield one to three

counts during the peak period.

Problems Associated with Data

Structure

Conducting route-regression trend analysis (Geissler

and Noon 1981) requires a single count representing the

population at a given site in a given year. Some ISS sites

may have several dozen censuses conducted during a

year, covering a period in which populations may

fluctuate dramatically. These counts must be converted

to one value representing an annual site index. We

examined four approaches to calculating site indices

and determined that the best measure was the log of the

average of all counts within a 21-day period centered at

the regional migration peak (Howe et al. 1989). Sites

with less than two counts during that period were

eliminated from analysis. This approach yielded the

smallest variances for population trend estimates. All

indices that incorporated data from both peak and

nonpeak periods yielded extraordinarily high variances.

Even the best population index yielded within-site

variances much higher than those typical ofsurveys like

the BBS, which samples populations that are more or

less uniformly distributed in space and time. This is a

potential problem for trend analysis because of the

importance of the variance in the back-transformation

from the log scale. However, analyses ofsimulated data

sets with comparably high variances yielded trend

estimates not significantly different from the trends

assumed for the simulation (Howe et al. 1989). This is

an encouraging sign, suggesting that the variance

problem may not be critical.

A final concern about the ISS data set analyzed for

1972-83 was the large number ofsite-year combinations

for which data were unavailable. This situation could

result in certain years contributingdisproportionately to

the trend estimate. We analyzed the entire data set for

the sanderling (c. alba), which was recorded at an

average of52 sites each year, and found that all possible

pairs of years were uniformly represented despite the

spotty coverage. Inconsistent coverage in the future,

however, could occasionally lead to biases toward

certain years and produce erroneous trend estimates.

Conclusions

The International Shorebird Survey, though not

rigorously designed to monitor shorebird populations,

produces data that can presently be used to derive

imprecise population trend estimates for obligate

coastal species. It is unlikely that species that often use

transitory wetlands during migration can be monitored

effectively. Forthe present, it is assumed that population

changes at coastal ISS sites are representative of

population changes throughout the coastal system (this

assumption is presently being field-tested). Observer

variation in estimating the size of large flocks is also a

potential problem, the magnitude ofwhich is not known.

More precise estimates of population trends could be

achieved by increasing the frequency of surveys at the

most important sites, particularly during peak periods,

and by assuring continuity of coverage across years.

Formal incorporation of coastal Canadian sites into the
ISS would help improve population trend estimates for

several species.

Literature Cited

Dunn, P.O., T. A. May, M. A. McCollough, and M. A. Howe.

1988. Length of stay and fat content of migrant

semipalmated sandpipers in eastern Maine. Condor

90:824-835.

Geissler, P. H., and B. R. Noon. 1981. Estimates of avian

population trends from the North American Breeding Bird

Survey. Pages 42-51 in C. J. Ralph and J. M. Scott, eds.

Estimating the numbers of terrestrial birds. Stud. Avian

BioI. 6.

Howe, M. A., P. H. Geissler, and B. A. Harrington. 1989.

Population trends of North American shorebirds based on

AVIAN POPULATION TRENDS 25

the International Shorebird Survey. BioI. Conserv.

49:185-199.

Lank, D. B. 1983. Migratory behavior of semipalmated

sandpipers at inland and coastal staging areas. Ph.D.

dissertation, Cornell University, Ithaca, N.Y.

Manomet Bird Observatory. 1980. Study of autumn shorebird

migration in the Atlantic corridor 1974-1978-a study of

red knot status and migration routes in the United States

east of the Rocky Mountains. Final report to U.S. Fish and

Wildlife Service, contract 14-16-0009-79-075.

Morrison, R. 1. G. 1976. Maritimes shorebird survey 1975,

preliminary report. Canadian Wildlife Service, Ottawa.

Page, G., and A. L. A. Middleton. 1972. Fat deposition during

autumn migration in the semipalmated sandpiper.

Bird-Banding 43:85-96.

Smith, P. W., and N. T. Houghton. 1984. Fidelity of

semipalmated plovers to a migration stopover. J. Field

Ornithol. 55:247-49.
26 BIOLOGICAL REPORT 90(1)

Evaluation of the Colonial Bird Register

by

R. Todd Engstrom

Comell Laboratory (Jf Omithology

159 Sapsucker Woods Road

Ithaca, New York 14850

History ofthe Colonial Bird Register

In the early 1970's E. J. Fisk and R. Downing started

counting least terns (Sterna antillamm) along the

eastern seaboard because they were concerned that

the species was declining (Downing 1973; Fisk 1975).

They organized a loose network of volunteers to count

terns, although they did not have a standardized

system of data collection.

At about the same time, the prospect of offshore oil

drilling and increasing human use of coastal habitats in

the eastern United States posed threats to wildlife,

especially colonial birds. In response to these threats,

the U.S. Fish and Wildlife Service initiated surveys of

colonial waterbirds on the Great Lakes (Scharf 1979)

and along all coastal areas of the United States (Erwin

1979; Erwin and Korschgen 1979; Korschgen 1979;

Sowls et al. 1980; Portnoy et al. 1981; Keller et al. 1984;

Spendelow and Patton 1988).

A central data repository was an attractive idea when

interest in waterbird colonies was high and financial

support was available. The surveys of Fisk and Downing

provided a model for an organization of volunteers to

collect data. The Colonial Bird Register (CBR) was

established on 1 August 1975 by joint agreement

between the National Audubon Society and the Cornell

Laboratory of Ornithology. The CBR relied on a

network of volunteer organizations and individuals to

contribute data.

From 1975 through 1984, the National Audubon

Society paid most ofthe expenses ofthe CBR, including

the salary of the director. The Cornell Laboratory of

Ornithology covered administrative costs and provided

office space. Between 1979 and 1986, the U.S. Fish and

Wildlife Service also supported the CBR through a

series of cooperative agreements. User fees for data

retrieval were expected to defray operational costs of

the CBR; however, the fees, averaging a few hundred

dollars per year, never covered the costs.

CBR Design and Content

One of the first priorities of the CBR was to devise a

form to standardize data reporting. This form had to be

detailed enough to provide data for a variety of analyses

and easy to use. Data categories on the form have been

modified twice over the years (McCrimmon 1978; Erwin

et al. 1984; Appendix).

The latest CBR form (Appendix) has space for

information on the date of colony visit, colony location,

habitat, colony disturbances, colony-site ownership and

management, survey technique, and nesting stage. The

vehicle from which birds are counted (e.g., airplane,

helicopter, boat) is also recorded.

The CBR contains data on locations and estimates of

abundance for gull, tern, heron, and egret colonies,

some of which are composed of several species

(McCrimmon 1978; Erwin et al. 1984). A single record

is made for each visit to a colony, although a colony may

be visited more than once in a year. An estimate of

abundance for each colony can be a count ofactive nests,

adults, or the number of breeding pairs. Records for 48

colonial seabird and wading bird species are included in

the CBR (Table). The CBR also contains records for

some swallows and more than 30 noncolonial species

counted incidentally among truly colonial species.

Data are edited and entered into the computerized

data base, which is in the ISIS data base management

system on the Cornell University mainframe computer.

Data can be retrieved by various geographical divisions

(e.g., colony, county, State) or by species.

CBR data come primarily from State wildlife

agencies, the U.S. Fish and Wildlife Service, and private

organizations such as The Nature Conservancy and

State Audubon societies. The survey of colonial

waterbird colonies from Maine to Florida in 1976 and

1977 forms the foundation of the computerized portion

of the CBR (Erwin and Korschgen 1979; Portnoy et al.

1981) - data from Texas from 1973 to 1980 comprise

another large portion (Texas Colonial Waterbird
AVIAN POPULATION TRENDS 27

Table. Names ofspecies included in the Colonial Bird Register (CBR).

Common

Leach's storm-petrel

American white pelican

Brown pelican

Great cormorant

Double-crested cormorant

Olivaceous cormorant

Magnificent frigatebird

Least bittern

Great blue heron

"Great white" heron

Great egret

Snowy egret

Little blue heron

Tricolored heron

Reddish egret

cattle egret

Green-backed heron

Black-crowned night-heron

Yellow-crowned night-heron

White ibis

Glossy ibis

White-faced ibis

Roseate spoonbill

Wood stork

Common eider

Black-necked stilt

American avocet

Laughing gull

Franklin's gull

Ring-billed gull

california gull

Herring gull

Glaucous-winged gull

Great black-backed gull

Gull-billed tern

caspian tern

Royal tcrn

Sandwich tern

Roseate tern

Common tern

Arctic tern

Forstcr's tern

Least tern

Sooty tern

Black tern

Black skimmer

Razorbill

I31ack guillemot

Atlantic puffin

Scientific

Oceanodroma leucorhoa

Pelecanus erythrorhynchos

Pelecanus occidentalis

Phalaerocorax carbo

Phalaerocorax auritus

Phalacrocorax olivacellS

Fregata magnijicens

lxobrychus exilis

Ardea herodias

Ardea herodias occidentalis

Casmerodius albus

Egretta thula

Egretta caemlea

Egretta tricolor

Egretta mfescens

Bubulcus ibis

Butorides striatus

Nycticorax nycticorax

Nycticorax violaceus

Eudocimus albus

Plegadisfalcinellus

Plegadis chihi

Ajaia ajaja

Mycteria americana

Somateria mollissima

Himantopus mexicamlS

Recurvirostra americana

Lams atricilla

Lams pipixcan

Lams delawarensis

Lams californiws

Lams argentatus

Lams glaucescens

Lams marinus

Sterna nilotica

Sterna caspia

Sterna maxima

Sterna sandvicensis

Sterna dougallii

Sterna hinmdo

Sterna paradisaea

Sterna forsteri

Sterna antillanun

Sterna fuscata

Ch/idonias niger

Rynchops niger

Alca torda

Cepplllls wylie

Fmtercula arctica

CBR records'

33

133

163

43

862

106

8

31

4,514

40

2,382

1,791

1,273

1,322

459

1,610

313

1,708

281

520

395

413

333

152

483

27

21

1,296

109

307

181

1,800

9

980

794

361

365

217

216

2,623

116

824

4,097

34

136

1,659

7

237

11

, The number of records over all years. This is not the same as the number of colonies; some colonies were visited more than once in a season.

Society 1982). Most recently, organized counts such as

the annual survey of Long Island, New York (MacLean

et a1. 1988), have generated the most data for the CBR,

followed by reports from many national wildlife refuges.

The number of records in the CBR ranges from 0 in

West Virginia to nearly 2,200 in New York (Fig. 1).
28 BIOLOGICAL REPORT 90(1)

Number of records

Do to 9

EJ 10 to 99

100 to 499

m500 to 999

.>1,000

Fig. 1. The number of computerized CBR records of visits to colonies by State. This does not include 263 records from Canada.

Some colonies are visited several times in a season.

Coverage among States varies from complete annual

surveys of all colonial birds to spotty reporting of only a

few colonies. The greatest number of CBR records

came from 1976 (3,100) and 1977 (2,600). Data

contributions dropped in 1978, and since then average

1,000 to 1,500 records per year (Fig. 2).

Evaluation ofthe CBR

The CBR was designed to consolidate the results of

local and regional colonial waterbird surveys.

Accessibility of the data for researchers was a high

priority. As data accumulated, examination of

population trends became possible (Erwin et al. 1984).

3,500

Quantitative data must be collected in a consistent way

over time for population trend analysis. Usefulness of

the CBR depends on the quality and consistency of the

monitoring effort and data base accessibility.

Monitoring Effort

The CBR includes data for a heterogeneous group

of species from a large geographical area for an

indefinite period. The diffuse data aren't extensive for

any single purpose, although some species in some

regions have enough data for preliminary analysis

(Engstrom et al. 1990). Most contributors to the CBR

were concerned primarily with bird populations for a

3,000

2,500 en

-0

.....

a

u

a>

..... 2POO

.....

a

.....

a> 1,500 ..Cl

E

::>

z: 1,000

500

a

Fig. 2. The number of computerized CBR

records by year. Note: only records of least

tern colonies are represented in 1986.

197374 75 76 77 78 79 80 81 82 83 84 85 1986

Year
single State and used custom-made sampling schemes.

The assortment of goals and methods at the State level

made the centralized CBR data base a patchwork. The

ultimate uses of the data need to be articulated clearly

from the origin of the data base (Moroney 1984), then

monitoring must adhere to standard practices of data

collection and recording.

The technical problems associated with the

monitoring effort that supplies the CBR (Erwin et al.

1984) include: (1) observers vary in quality; (2) counting

techniques vary among colonies, States, and years; (3)

colony names are inconsistent among years and

observers; (4) inactive colonies are not recorded as

"zeros," making trend analysis problematic; and (5)

geographical coverage of colonies varies among States.

The Data Ba5e

Centralization and computerization have made CBR

data easily accessible to conservation organizations,

academic researchers, and State wildlife management

agencies. The data base greatly speeds organization of a

wide array of counts and independent studies into

regional summaries. Standardized reporting aids

comparison of data from different sources, if the forms

were filled out completely. Data that were collected by

different methods can be compared by using correction

factors, although these factors should be used with

caution. For example, empirically derived correction

factors have been used to compare aerial counts with

ground counts (Erwin 1979; Thompson 1982; Engstrom

et al. 1990).

Contributors want to see the results of contributing

their data to a centralized data base. Products of the

CBR, such as an analysis of trends in the population of

a species or maps of colony locations, are expensive.

Data contributors may be reluctant to go through the

burden of filling in standardized forms and then be

asked to pay for an analysis of their own data.

Additionally, some contributors, especially university

biologists, want to protect their data from use by other

researchers because they intend to publish their results.

In the extreme case, the data base is left in the untenable

position of gathering data that cannot be used. This

problem can be circumvented by making an agreement

between the contributor and the data base to protect the

data for a specified time.

Recommendations

1. The goals, methods of analysis, and census or survey

guidelines that relate to those goals should be clearly

defined at the outset of any colonial waterbird

monitoring program. Technical instructions for

counting seabirds have been established in Great

AVI~'Ii POPULATlo;-,; TRENDS 29

Britain and Canada (Anonymous 1969; Nettleship

1976). A summary of colonial waterbird counting

techniques similar to that for terrestrial birds could be

written by an individual or organization or could be

produced in a symposium (Ralph and Scott 1981;

Verner 1986).

2. State programs to monitor colonial waterbirds

should be coordinated. As nongame wildlife

departments have developed, coastal surveys have

become important State programs. The goals, field

techniques, and common types of analysis ofthe various

State surveys should be identified and standards

adopted. The precision of counts should be improved to

permit rigorous analysis. Any common goals and

cooperative projects that emerge would help to define

the structure of a useful database that is compatible

among States.

3. Given a focus on which species to monitor and the

purpose for monitoring, the operational requirements

of a colonial bird monitoring program should include

(a) conducting field tests of census methodology, (b)

coordination of survey efforts within a region, (c)

training participants, (d) promoting the program, and

(e) periodically publishing results.

4. Operational requirements of a colonial waterbird

data base should include (a) data entry and editing

(especially checking the data for duplicate colony names

and errors), (b) maintaining the data in an accessible

data-storage system, (c) communicating with

cooperators, (d) publishing results, and (e) performing

retrievals for data users.

5. Ifa centralized data base is desirable, the products of

the data base and sources of funding for those products

should be clearly identified from the beginning.

Conclusions

The Cornell Laboratory ofOrnithology discontinued

the CBR in 1988 because ofinsufficient funds to manage

the data base or to provide the guidance necessary to

ensure high quality data. The CBR was the cornerstone

of an ambitious landmark effort to survey and monitor

colonial birds. Although new data are not being added,

the extant CBR data base is available for research

purposes. It is important to learn the lessons provided

by the CBR beeause a high quality monitoring program

for colonial birds is needed now as much as it was when

the CBR was initiated (Parnell et al. 1988).

Acknowledgments

I thank G. S. Butcher, R. M. Erwin, and J. D. Lowe

for helpful comments on the manuscript.
30 BIOLOGICAL REPORT 90(1)

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terns and black skimmers in the east. Am. Birds

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Engstrom, R. T., G. S. Butcher, and J. D. Lowe. 1990.

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Erwin, R. M., P. H. Geissler, M. L. Shaffer, and D. A.

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Ralph, C. J., and J. M. Scott, editors. 1981. Estimating

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Scharf, W. C. 1979. Nesting and migrating areas of birds ofthe

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Sowls, A. L., A. R. DeGange, J. W. Nelson, and G. S. Lester.

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Wildl. Serv., FWS/OBS-37/80. 371 pp.

Spendelow, J. A., and S. R. Patton. 1988. National atlas of

coastal waterbird colonies in the contiguous United States:

1976-82. U.S. Fish Wildl. Serv., BioI. Rep. 88(5). 326 pp.

Texas Colonial Waterbird Society. 1982. An atlas and census

of Texas waterbird colonies 1973-1980. Caesar Kleberg

Wildlife Research Institute, Kingsville, Tex.

Thompson, B. C. 1982. Distribution, colony characteristics,

and population status of least terns on the Texas coast.

Ph.D. dissertation, Texas A&M University, College

Station.

Verner, J. 1985. A'>SCssment of counting techniques. Pages

247-302 in R. F. Johnston, ed. Current ornithology, Vol. 2.

Plenum Press, New York.
AVIAN POPULATION TRENDS 31

Appendix. Colonial Bird Register form.

COLONIAL BIRD REGISTER at the Laboratory of Ornithology • Cornell University

159 Sapsucker Woods Road • Ithaca NY 14850·1999 • (607) 255·4999

GENERAL INSTRUCTIONS: Please fill out form In pencil, so that we can easily make necessary changes during our editing, Shaded

areas are for editor's use only. We appreciate any information you can provide us concerning nesting bird colonies. Please send a report

even If you cannot provide all the requested mformation. If you know the colony has been previously censused and you know the official

colony name or 10 number, then some of the requested information can be omitted. If you visit a colony more than once during a year,

you may staple the forms together and omit duplicated mfQrmatlon on subsequent forms,

VISIT AND LOCATION INSTRUCTIONS

NAME If you don't know an official name, use a nearby landmark (town, lake, river, etc.). Please provide a colony map/sketch that

mcludes the landmark used for the name. COLONY/SUBCOLONY When in doubt about whether two groups are separate colonies, we

suggest that you lump them together for reporting purposes and perhaps identify them as subcolonles, Please show subcolony locations

on a colony map/sketch. TIME: Use military time, e.g. 1:00 PM =1300, 9:00 AM = 0900,

COLONY VISIT INFORMATION

Date (mm/dd/yy) _

Time started (military) __-'--__

COLONY LOCATION INFORMATION

Colony name

Length of visit _"_ hours

Recorder's name

Latitude

__ O __ 'N

Longitude

__ O __ 'W Subcolony #

Recorder's address

Other observers _

Colony visit # (thiS year)

Nearest community _

County _

State/Province/Territory Country _

Colony 10 numbers: Federal State Other _

COLONY SITE INFORMATION

General habitat: (CIRCLE ONE) penmsula(O) barrier Island(1) saltwater non-barrier island(2) freshwater island(3)

shoreline /lake,pond,lmpoundment(4) shorel ine / ocean,estuary(5) riparian / rlver.stream(6) other(7)

SpeCifiC habitat' (CIRCLE ONE) salt marsh(2) fresh marsh(4) mangrove(34) shrub swamp(36) wooded swamp(11) bog(10)

spoil/fill area(6) sand bar(20) salt lIat(21) sandy beach(37) gravel beach(38) dune(39) rocks(40)

deciduous forest(41) evergreen forest(42) mixed lorest(43) rooftop(27) parkmg lot(44)

pier/ Jetty / dock/breakwater(45) other(16) _

Nest substrate (CIRCLE ONE) evergreen trees(1) deciduous trees(2) mixed trees(3) dead trees(37) shrub(4)

grass/sedge/rush/herb(38) dead herbaceous/wrack(39) salt Ilat(25) soil(34) sand(12) gravel(32)

cobble(14) rocks/crevlces(15) rocks/cllff(16) burrow(1?) man-made structure (explain 19)

other(20) _

% Vegetation cover within colony %

COLONY DISTURBANCE

For each possible factor indicate whether disturbance is absent. current, or potential.

"Current" means that a disturbance probably influenced your survey and population

estimate and IS probably affecting this year's productivity. "Potential" means that although

there was no eVidence of current disturbance, It IS still possible that the disturbance could

affect this year's productiVity For each factor present prOVide more details in the

REMARKS section c E c c

'" 0 .~

"' 2 0

c 2:- <ii co OJ rn '6 "0

" OJ <;;

"' 0 "0 "' c E U ii => OJ "iii "0 r: g a. '" OJ

'" 0- > > r: c 0

none C C C 0 0 0 0

current [j C C 0 0 0 0 0

potential C [J C 0 C 0 0 0

O,d poor weather Influence your population estimate this visit?

Yes C No C If yes, give details in REMARKS section.

COLONY MANAGEMENT

Owner

Owner's address

Colony Management (CIRCLE YES or NO

or UNKNOWN)

posted Y N U

fenced Y N U

patrolled Y N U

predator-control Y N U

fencing (CIRCLE)

none string electric snow

other _
32 BIOLOGICAL REPORT 90(1)

INSTRUCTIONS FOR REPRODUCTIVE INFORMATION: We are asking for two types of survey information: (1) what did you actually see

(adult count, active nest count) and (2) what is your best estimate of the number of breeding pairs using this site this year. Please give

details of how this estimate was obtained in the REMARKS section. Please list each species on a separate line (do not write in shaded

areas). Choose codes for "counted from" and "survey technique" and write them in the boxes. Adult count (individuals) and active nest

count refer to actual numbers seen on the day of the visit. Estimated # of breeding pairs refers to the total size of the colony (this mayor

may not be the same as active nests) e.g. a tern colony of 1apairs is washed out shortly before a visit, during this visit 15 adults are seen

but only 2 pairs have started to renest. Adult count (individuals) = 15, active nest count = 2, estimated # of breeding pairs = 1a. For nesting

stage check the appropriate box or boxes, if unknown please write "unknown" across the boxes. If you band birds, please specify adult,

young, both, or none in the last column. Extra information should be put in the REMARKS section. Thanks' COUNTED FROM:

3-airplane, 4-helicopter, 5-boat, 6-motorized land vehicle, 7-foot (periphery of colony), 8-foot (within colony), 9-other (describe in

REMARKS section) SURVEY TECHNIQUE: 1-total adult count, 2-total nest count, 3-photography, 4-partial adult count, 5-partial nest

count, 6-quadrat sample, 7-line strip sample, 8-visual estimate, 9-best guess, a-other (describe in REMARKS section)

nesting stage (check appropriate

boxes & circle most frequent stage)

OJ

c:

0 ,.,c:

1 =... ,." OJ I al 0_

.0 > c..c: u ,., <Il_

.!!l 0> '" <Il c: c:

0 Icn~ OJ 0> '" " "0 ." u

~O>

- <Il ..: c: 0 0> " , 0> DC: 0.= 0> .$ 0> c: " ,., c: u c: 0>"

""~ c: c: 0 0 u ';;' 0> c: OJ 0> £g, ,., OJ -= 0 c: .~

<ii Uo> 0> ';;' :;; c: ,., a; 0> ." 0> 0 OJ OJ OJ c: .!!' .0 i' c: <ii -g£f -" u c: c: 2c: c: ." " '" .c: c: OJ c: OJ- "'.- OJ ';;; 0> <ii "'- " 1ii " c: <ii '" Eil 0> .0-6 '" 0 0 .0 0 > c: C. C. OJ -" .c: u 2 ,., 5! '" :;:;:l ".;:; Q) c. :-::m "0 <Il~

10 20 22 "''' OJ.o

u

OJ

E

,., .c OJ

,g > C<Il " :s species u 2 <Il 5§ 2 ,., ci. "u c: OJ 0 ~s

" c. ".- 0

" J' -g~ " <Il

Colony name _

Date

Visit # __ of __ for this year

Sheet # __.of __ for this visit

REPRODUCTIVE INFORMATION

I

I

I I i

I i ,

i

i

REMARKS: attach additional sheets if necessary COLONY MAP/SKETCH: (should show colony in relation

to landmarks or Lat. & Long.) attach additional sheets if

necessary
AVIA."I,' POPULATIO:"l TRE:"IDS 33

Descriptions of Surveys: Breeding Bird Censuses

by

David W. Johnston

5219 Concordia Street

Fai1ax, Virginia 22032

Introduction

In recent years many community ecologists have

strongly emphasized the need for ecological

investigations that extend over long periods, an

approach also embodied in the Long-term Ecological

Research Program (LTER) of the National Science

Foundation (Callahan 1984). Such long-term records

are perceived as providing valuable insights into a

predictive capability for population and community

changes over time. For example, studies of birds,

particularly those conducted during the breeding

season that span several consecutive years or even

decades, should provide good indicators of long-term

population changes and dynamics. Changes in some

North American bird populations were suggested as

early as 1969 by Aldrich and Robbins (1970). More

recently, based on a small number of long-term census

data sets, Briggs and Criswell (1979), Hall

(1984 a,1984 b), and Johnston and Winings (1987)

reported declines in breeding bird populations at some

eastern forest sites. Nonetheless, Bohlen (1984:27)

correctly cautioned that "not enough long-term

censuses have been conducted to provide conclusive

evidence that...species... are seriously dwindling

overalL"

By searching the published literature, I found 15

long-term data sets of breeding bird censuses for

deciduous forest sites, mostly in the eastern United

States. The setsrange from 10 to 50 years, one extending

back to 1927 and about half including the 1980's. The

present report identifies these sites and their

characteristics. Breeding bird data from the sites are

discussed as being appropriate for use in monitoring

avian population changes over long periods.

Sources ofthe Data Sets and Site

Characteristics

Censuses of breeding landbirds from the United

States date back to the 1914-20 surveys ofthe Bureau of

Biological Survey (Cooke 1923), and since 1937, the

National Audubon Society has sponsored annual

breeding bird censuses at scattered sites and in different

habitats in North America. In this paper, only data from

deciduous forests in the eastern and midwestern United

States have been analyzed, chiefly because of the

availability of their long-term sets (at least 10 years;

Table 1). I have not found long-term quantitative sets

for other major ecological community-types in North

America except for the reports of Hall (1984 a,1984 b)

from eastern spruce forests.

Most of the data sets have been published in

Audubon Field Notes, American Birds, and The Atlantic

Naturalist (see Table 2 for additional published

sources). The 15 sites were geographically widespread,

ranging from south Georgia to New Hampshire and

from Maryland to Illinois. Some censuses date back

50 years (Table 1; Figure).

These census sites were in ecologically similar

habitats. Only mature forests (from the authors' or

compilers' original descriptions) were used, and none

were used in which forest succession was evident.

Some sites contained a few scattered pines (and thus

might not have been a true climax), but all sites were

reportedly dominated by mature hardwoods of various

species (e.g., oak, hickory, beech, maple) in various

combinations. The estimated ages of sites and

dominant tree species differed somewhat among the

sites (Table 2).

The environments of some sites reportedly remained

unchanged throughout the duration ofthe censuses, both

inside and outside the site (Table 2). Other sites in Table 2

either incurred some nonsuccessional habitat changes,

the magnitude of which might be questionable, or lacked

convincing documentation for changes over the duration

of the censuses to determine actual or possible

perturbations. I contacted as many of the compilers or

authors as possible, thus obtaining first-hand, crucial

information on the continuity ofthe site within contiguous

forests. For the most part, the same site and area were

censused in subsequent years, and boundaries remained
34 BIOLOGICAL REPORT 90(1)

Table 1. Deciduousforest sites for long-temz breeding bird censuses andyears ofcensuses.

First Last Numbcrof Span

Sites year year censuses (years)

Unchanged-parts of large forest tracts,

remaining unisolated during census periods

Wilkesboro, North carolina 1954 1973 20 20

Hubbard Brook, New Hampshire 1%9 1984 16 16

Dranesville, Virginia 1973 1986 14 14

Wormsloe Plantation, Georgia 1%3 1973 11 11

calhoun County, Michigan 1938 1947 10 10

Clayton, Georgia 1%9 1978 10 10

Changes known or undetermined

Trelease Woods, Illinois 1927 1976 42 50

cabin John, Maryland 1947 1986 34 40

Rock Creek Park, District of Columbia 1948 1986 29 39

calvert County, Maryland 1960 1986 26 27

Glover-Archbold Park, District of Columbia 1959 1986 25 28

Cleveland, Ohio 1932 1947 15 16

Greenbrook Sanctuary, New Jersey 1949 1983 11 37

Connecticut Arboretum, Connecticut 1953 1985 11 33

Licking County, Ohio 1937 1947 10 11

unchanged over the census years. At only one site was the

census area changed: Rock Creek Park, where the

original census area of 32.4 ha established in 1948 was

reduced to 26.3 ha in 1%3.

Census Methods

Although some censustakers and annual compilers

might have changed at a given site over the years,

methods for censusing and tabulating the breeding

bird populations did not; census methods reportedly

followed the widely used spot-mapping-singing

male-territory mapping technique. This technique, as

described by Williams (1936) and Stewart and Aldrich

(1949), is based on the spacing of (most) forest

breeding birds into territories identified chiefly by the

locations of males, their conspicuous vocalizations,

and behavior patterns. By mapping locations of all

(singing) males (and pairs) in a census area on

different days during the breeding season, one can

identify territories reasonably accurately. By assuming

that each territory contains or will contain a pair of the

species, the number of occupied territories can be

translated into the number of pairs of each species

breeding on a given site.

The Data Sets

For the purpose of a comparative analysis, the

number of breeding pairs at each site as reported by

the compiler or author has been standardized to the

number of pairs per 100 acres. For each year the total

breeding pairs at a site can be subdivided into two

components: neotropical migrant species and

short-distance migrant-resident species. Thus, for

each census-year at a given site, three numbers are

available (all in breeding pairs per 100 acres): total

birds, neotropical migrants, and short-distance

migrants or residents.

Some of the data sets are clearly periodic, that is,

censuses are equally spaced over the years (Table 1).

For the sets in Table 1 and the Figure, periodic ones

are those in which censuses were reported for every

year over the time span; other sets had gapsin the years

censused (i.e., aperiodic).

Potential Limitations of the Data Sets

Despite the perceived value of these long-term data

sets, the following potential limitations for estimating

population trends are recognized (see also discussions

by various authors in Ralph and Scott 1981):

• the aperiodicity of some of the data sets;

• variation in census effort (man-hours expended);

• inadequate vegetation-site description at the

inception ofthe first census; thus, the true ecological age

of the site might be unknown;

• biased estimates, such as overestimation of

population density by assuming that a singing male on

territory truly represents a breeding pair, or

underestimating population density ifsome males were

not singing on a territory and hence were not counted;
AVIAN POPULATION TRENDS 35

Table 2. Deciduousforest breeding bird census sites, sizes, andbriejdescriptions.

Site

Wilkesboro, North Carolinab

Hubbard Brook, New Hampshirec

Dranesville, Virginiab

Wormsloe Plantation, Georgiab

Calhoun County, Michiganb

Clayton, Georgiab

Trelease Woods, Illinoisd

Cabin John, Marylandb

Rock Creek Park, District of Columbiab

Calvert County, Marylandb

Glover-Archbold Park, District ofColumbiab

Cleveland, Ohiob

Greenbrook Sanctuary, New Jersel

Connecticut Arboretum, Connecticutf

Licking County, Ohiob

a Extracted from compilers' or authors' account.

b Data from Audubon Field Notes and American Birds.

C Holmes et al. 1986.

Kendeigh 1982.

e J. Serrao 1985 and personal communication.

Butcher et al. 1981.

Reduced to 26.3 ha in 1963.

Criswell and Gauthey 1980.

Size (ha)

16.2

10

11

10

5

6.1

24

7.6

11.3

14.2

26.3

66.8

23

14.2

Description"

Hardwoods with some white pine. No

major habitat changes inside or

outside

Unfragmented temperate deciduous

forest

Uniform hardwoods; part of 140-ha

tract

Oak-pine forest; part of 130-ha tract

Oak-hickory forest; part of 40-ha

woodlot

Montane hardwood forest; part of

extensive national forest

Mature mixed hardwoods; field edge

extant since 1927

l00-year-old riverine forest; road

constructed nearby in 1958h

80-year-old hardwood forest with

scattered pine; road construction

along one edge

Mixed hardwoods, upland; some

logging inside and outside tract in

1976

Mixed hardwoods, upland; unknown

habitat changes outside

Climax beech-maple with some

hemlock; unknown changes

outside

Mixed northern hardwoods; "most

neighboring woodlands have

remained intact"

Hemlock hardwoods; always isolated

Oak-hickory forest, upland; some

clearing on one edge in 1945

• failure to discover any perturbations (physical or

biological) occurring inside or immediately contiguous

to the site; and

• variation in observer competency.

All the sets in Table 1 are probably more or less

equally affected by these possible limitations, so the sets

are considered here to be comparable.

I am aware that some relevant information about

the sites might be unavailable or indeterminate. For

example,

• How and why were the sites originallyseIected? For

easy transportation distance? Historically, were these

sites simply "good birding spots?"

• Are these sites truly representative of eastern

deciduous forests and their breeding bird popu'lations,

both geographically and temporally?

• Are population data from the sites comparable

because of the known differences in site-sizes,

geography, and plant-species composition?

Analysis of the Data Sets

In another paper in these workshop proceedings,

S. R. Taub presents a procedure for examining these

data sets to discover whether they reveal any temporal

trends.
36 BIOLOGICAL REPORT 90(1)

SITE

Wilkesboro, NC

Hubbard Brook, NH

Dranesville, VA

Wormsloe PI., GA

Calhoun Co., MI

Clayton, GA

Trelease Woods, IL

Cabin John, MD

Rock Creek Park, DC

Calvert Co., MD

Glover-Archbold Park, DC

Cleveland, OH

Greenbrook, NJ

Conn. Arboretum, CT

Licking Co., OH

1920 1930 1940 1950 1960 1970 1980 1990

YEAR

Figure. Span of years for breeding bird censuses at deciduous forest sites.

Literature Cited

A1drich,J. W., and C. S. Robbins. 1970. Changing abundance

of migratory birds in North America. Pages 17-26 in H. K.

Buechner and J. H. Buechner, eds. The avifauna of

northern Latin America. Smithson. Contrib. Zoo\. 26.

Bohlen, J. 1984. New threat of a silent spring. Defenders

59:20-29.

Briggs, S. A, and J. H. Criswell. 1979. Gradual silencing of

spring in Washington. At\. Nat. 32:19-26..

Butcher, G. S., W. A Niering, W. J. Barry, and R. H. Goodwin.

1981. Equilibrium biogeography and the size of nature

preserves: an avian case study. Oecologia 49:29-37.

Callahan, J. T. 1984. Long-term ecological research.

BioScience 34:363-367.

Cooke, M. 1'. 1923. Report on bird censuses in the United

States 1916 to 1920. U.S. Dep. Agric., Dep. Bull. 1165.

36pp.

Criswell,J. H., and J. R. Gauthey. 1980. Breeding bird census:

mature deciduous flood plain forest. Am. Birds 34:46.

Hall, G. A 1984a. A long-term bird population study in an

Appalachian spruce forest. Wilson Bul\. 96:228-240.

Hall, G. A 1984b. Population decline ofneotropical migrants

in an Appalachian forest. Am. Birds 37:14-18.

Holmes, R. 1'., 1'. W. Sherry, and F. W. Sturges. 1986. Bird

community dynamics in a temperate deciduous forest:

long-term trends at Hubbard Brook. Eco\. Monogr.

50:201-220.

Johnston, D. W., and D. 1. Winings. 1987. Natural history of

Plummers Island, Maryland, XXVII. The decline offorest

breeding birds on Plummers Island, Maryland, and vicinity.

Proc. Bio\. Soc. Wash. 100:762-768.

Kendeigh, S. C. 1982. Bird populations in east central Illinois:

fluctuations, variations, and development over a

half-century.lll. Bio\. Monogr. 52:1-136.

Ralph, C. J., and J. M. Scott. 1981. Estimating numbers of

terrestrial birds. Stud. Avian Bio\. 6. 630 pp.

Serrao, J. 1985. Decline of forest songbirds. Rec. N.J. Birds

11:5-9.

Stewart, R. E., and J. W. Aldrich. 1949. Breeding bird

populations in the spruce region of the central highlands.

Ecology 30:75-82.

Williams, A B. 1936. The composition and dynamics of a

beech-maple climax community. Eco\. Monogr.

6:319-408.
AVIAJ"i POPULATION TRENDS 37

Migration Banding Data: a Source ofInformation on Bird

Population Trends?

by

Deanna K. Dawson

u.s. Fish and Wildlife Service

Patuxent Wildlife Research Center

Laurel, Maryland 20708

Introduction

Interest by North American banders in the capture

and banding of birds during migration greatly increased

following World War II when mist nets manufactured in

Japan became available (Low 1957). Mist nets allowed

nonselective capture of a wide variety of bird species,

thus providing a more accurate sample of passerines

than other capture techniques. The first serious attempt

to capture and band large numbers of passerine

migrants at a known concentration point occurred at

Cape May, NewJersey, in 1951-53, when approximately

18 mist nets were operated for one weekend each

September (Baird et a1. 1958). The success of this

operation led to an organized effort to band migrants at

concentration points along the Atlantic Coast.

Operation Recovery, as the project became known, was

initiated in 1955, with seven netting stations at coastal

locations in Maryland, Massachusetts, New Jersey,

Rhode Island, and Virginia in operation during parts of

August and September (Baird et a1. 1958). At the outset

of the project, the coordinators envisioned that the

movements of migrants could be tracked southward.

The paucity of recoveries prompted the expansion of

objectives to include study of the effects of weather on

migration, the relation between visual counts of

migrants and mist net captures, and the use of bird

weights as an aid in interpreting migration data (Baird

et a1. 1958).

Banders were originally requested to concentrate

banding activities within a 2-week period, during late

August in the Maritime Provinces to late September

south of Maryland. As interest in the project and the

number of experienced banders increased, many

stations expanded coverage and banded throughout the

fall migration period, and several stations began to

operate during spring migration. In addition, stations

were established at a number of inland sites. The

number of bandirg stations participating in Operation

Recovery varied from year to year; by 1960,

approximately 50 stations were in operation during the

fall migration, capturing and banding more than 60,000

birds (Bird Banding Laboratory 1%1).

The U.S. Fish and Wildlife Service (FWS) sponsored

Operation Recovery, entering the data into computer

files and providing analytical support, through 1%9.

Thereafter, the Eastern Bird Banding Association

(EBBA) published summaries for stations that

continued to operate, first in EBBA News, later in North

American Bird Bander (NABB). In recent years about

40 banding stations in the eastern United States have

submitted annual summaries of fall banding activities

for publication in the NABB Atlantic Flyway Reviews.

No comparable summaries of migration banding

activities elsewhere in North America are currently

being published; however, relatively few banders in

other regions operate stations regularly during

migration (S. M. Russell, personal communication).

Since the start ofOperation Recovery, banding effort

has varied widely among the participating stations,

ranging from a few mist nets operated occasionally in a

bander's backyard to 50 or more nets run daily from

dawn until dusk at established bird observatories. In

recent years, approximately 15 stations in the eastern

United States operate on a regular basis during fall

migration; fewer stations operate during spring

migration. Banders are strongly encouraged to record

the number of nets and hours in operation for each day

of banding (Baird et a1. 1958; Robbins 1968). All

unmarked birds captured are identified and banded

with FWS aluminum bands. Whenever possible, the age

and sex are determined, using characters such as

plumage, eye or bill color, wing chord length, and degree

ofskull pneumatization. Banders are required to submit

schedules to either the FWS Bird Banding Laboratory

(for birds banded in the United States) or Canadian

Wildlife Service (for birds banded in Canada), reporting

species, age, sex, and date and location of capture for
38 BIOLOGICAL REPORT 90(1)

each band used. All bandings of nongame bird species

since 1954 are stored in computer files at the Bird

Banding Laboratory in the form of monthly summaries

of all birds of the same species, age, and sex, banded

under the same permit number, within the same lO-min

block of latitude and longitude.

Concerns About the Use ofMigration

Data for Trend Estimation

The measurement of trends in numbers of different

passerines has long been recognized as a possible use of

data from migration stations (Bergstrom and Drury

1956). Most banders, in summarizing a season's results,

compare captures to those of previous years, indicating

they believe that mist net samples provide an index of

actual population levels for species. However, few

banders have attempted to statistically analyze

long-term changes in numbers of migrating birds (but

see Husse1l1981; Jones 1986; Stewart 1987).

Migration data receive little use for estimating

passerine population trends for several reasons.

Although many banders now own microcomputers and

routinely enter data they collect, few possess the

statistical knowledge and software required for data

analysis. Even among those knowledgeable in statistics,

two major concerns exist regarding the use of migration

data for monitoring population change. First, because

populations of migrants cannot be associated with

specific breeding or wintering areas, it is not known if

the same source population is-being sampled each year

by a banding station. Second, factors other than

population change contribute to variability in annual

counts ofmigrating birds. Regional and local changes in

habitat could influence use of a stopover site by

migrants. In a study conducted in riparian habitats in the

lower Colorado River valley, Anderson et al. (1983)

found that fall avian communities were more closely

associated with vegetation parameters than were

breeding season avian communities. However, habitat

use patterns for most passerine species at migration

stopover points have not been documented (but see

Parnell 1969; Yahner 1983; Hutto 1985; Martin and Karr

1986). Very few migrants are recaptured at the banding

station ofinitial capture in subsequent years, suggesting

that birds do not exhibit fidelity to stopover sites.

Further research is required to determine if migrating

passerines select stopover sites in which they are

captured or if presence at a site is irrespective of its

habitat attributes.

Weather greatly affects the migration of birds

(Richardson 1978). Regional patterns can influence the

timing and volume of flights and the migration routes

and stopover points used; local conditions can influence

length of stay by migrants at stopover sites (and hence

their capture probabilities), daily operation of banding

stations, and the effectiveness of mist nets.

Bird species vary both in their seasonal patterns of

migration (timing and distribution, Hall 1981) and in

their diurnal activity patterns at stopover sites (Atwood

et al. 1988). Thus, variation in banding effort among

years, both in total effort and in the distribution of effort

throughout the day and season, can cause variability in

seasonal captures of birds that is not related to

population change.

On the other hand, species identification of birds

sampled with mist nets is generally not subject to the

observer biases inherent in surveys based on visual or

aural counts ofbirds. However, the accurate assessment

of bird age and sex requires a level of expertise that

varies among banders. In addition, the reliability of the

characters used in age and sex assessment varies with

time of year and among bird species. Thus, these data

should be used with caution.

Deriving an Index to Seasonal Totals

In using migration data to estimate long-term

changes in bird populations, the first task is to define

a dependent variable, an index that characterizes the

number of individuals of a species that pass through a

banding site in a season. An obvious index is simply the

total captures of the species for the season. This index

is probably only reasonable if weather and effort at a

site are consistent among years. Hussell (1981)

developed multiple regression techniques that attempt

to correct migration indices for the effects of weather

factors; however, these techniques are complex and

difficult to apply.

Seasonal captures can also be adjusted statistically to

account for variation in effort among years. A commonly

used adjustment for effort is to weight total captures of

a species by total effort (expressed as net-hours) during

the season. If the distribution of effort throughout the

season varies among years, a more refined adjustment

for each species may be desired. Total counts (captures)

of a species weighted by effort expended during the

period in which the species is migrating through a site

(or during the period in which 95% ofthe individuals of

the species are counted or captured) has been used as a

seasonal index of migrating raptors in analyses of

population trends (Titus and Mosher 1982). Another

approach is to statistically standardize the number of

days of operation among years by estimating captures

for a species for days when the banding station was

closed. Hussell and Risley (1978) replaced missing data

with the long-term average of counts for a species for

that date, then summed daily counts for the species over

the entire season. We (D. K. Dawson and J. R. Sauer,

unpublished) have proposed a technique that involves
breaking the migration period into intervals (of 5 days

or longer), calculating mean daily captures per

net-hours for a species within each interval, using the

mean as an estimate of captures for missing days within

an interval, and summing these weighted daily captures

over the banding season (Dawson and Sauer,

unpublished). This adjustment may be particularly

appropriate for species for which between-interval

variation in daily captures per unit effort is significantly

greater than within-interval variation. Variability in

effort among days can be accounted for by including

captures only from the hours during which the station is

operated regularly or by weighting effort during

different periods ofthe day (Ralph 1976). Although it is

possible to adjust captures to account for variability in

effort, the reliability of the indices is reduced as the

number of days increases for which estimates are

substituted for actual captures. Thus, if monitoring

population changes is to be one ofthe goals ofmigration

banding, banders should strive to have effort as

consistent as possible among days and among years.

Research Plans

A study has been initiated by the FWS to evaluate

the potential for estimating bird population trends

from migration banding data. We propose to adapt the

route-regression methodology developed for analysis

of population trends from Breeding Bird Survey data

(Geissler and Noon 1981; Robbins et al. 1986) to

estimate trends for banding stations in the Atlantic

Flyway (stations operated on a regular basis during fall

migrations over the past 10 years), then derive

composite estimates for the region based on weighted

averages of the estimates from individual stations.

Combined estimates of trends may more accurately

reflect actual population trends than do trends from

anyone station. The validity of the trends will be

assessed by comparisons with trends for the species

estimated from Breeding Bird Survey (BBS) data. We

also plan some analyses of patterns of year-to-year

change and long-term trends within species among

banding stations and among species within stations

that win further enable us to assess whether migration

banding data are reflecting population changes or

simply reflecting local phenomena.

Because ofthe efforts ofthe many dedicated banders

and the resources of the Bird Banding Laboratory

required to process and store an the data they collect,

the large set of data conected at migration banding

stations should be evaluated as to its potential for

monitoring bird populations. Migration banding data

could be useful in corroborating trends estimated for

species using data from the Breeding Bird Surveyor

Christmas Bird Count and in estimating trends for

AVIAN POPULATION TRENDS 39

species that are not well sampled by these monitoring

techniques. Migration banding data offer two additional

potential uses: (1) to monitor trends of adults returning

from wintering grounds (using spring migration data),

and (2) to monitor trends in the number of adults and

young surviving the breeding season (using data

collected during fan for species for which age class can

be accurately assessed). For species with declining

populations, examination of trends from two seasons

and for the two age classes might suggest if declines are

associated with the breeding grounds or with the

wintering grounds.

Acknowledgments

S. Droege, J. M. Hagan, G. A. Hall, and J. R. Sauer

provided helpful comments on earlier drafts of the

manuscript.

Literature Cited

Anderson, B. W., R. D. Ohmart, and J. Rice. 1983. Avian and

vegetation community structure and their seasonal

relationships in the Lower Colorado River Valley. Condor

85:392-405.

Atwood, J. L., J. M. Hagan, and T. L. Lloyd-Evans. 1988.

Diurnal activity patterns in fall migrant landbirds at a

coastal stopover site. Paper presented at Centennial

Meeting of Wilson Ornithological Society, Rosemont, Pa.

Baird, J., C. S. Robbins, A M. Bagg, and J. V. Dennis. 1958.

"Operation Recovery"-the Atlantic coastal netting

project. Bird-Banding 29: 137-168.

Bergstrom, E. A, and W. H. Drury, Jr. 1956. Migration

sampling by trapping: a brief review. Bird-Banding

27:107-120.

Bird Banding Laboratory. 1961. Operation recovery, 1960.

EBBA News 24:129-131.

Geissler, P. H., and B. R. Noon. 1981. Estimates of avian

population trends from the North American Breeding Bird

Survey. Pages 42-51 in C. J. Ralph and J. M. Scott, eds.

Estimating numbers ofterrestrial birds. Stud. Avian BioI. 6.

Hall, G. A 1981. Fall migration patterns of wood warblers in

the southern Appalachians. J. Field Ornithol. 52:43-49.

Hussell, D. J. T. 1981. The use of migration counts for

monitoring bird population levels. Pages 92-102 in C. J.

Ralph and J. M. Scott, eds. Estimating numbers of

terrestrial birds. Stud. Avian BioI. 6.

Hussell, D. J. T., and C. J. Risley. 1978. Monitoring bird

populations by migration sampling. Unpublished report to

Canadian Wildlife Service. 143 pp.

Hutto, R. L. 1985. Seasonal changes in the habitat distribution

of transient insectivorous birds in southeastcrn Arizona:

competition mediated? Auk 102:120-132.

Jones, E. T. 1986. The passerine decline. N. Am. Bird Bander

11:74-75.

Low, S. H. 1957. Banding with mist nets. Bird-Banding

28:115-128.

Martin, T. E., and J. R. Karr. 1986. Patch utilization by

migrating birds: resource-oriented? Ornis Scand.

17:165-174.

Parnell, J. F. 1%9. Habitat relations of the Parulidae during

spring migration. Auk 86:505-521.
40 BIOLOGICAL REPORT 90(1)

Ralph, C. J. 1976. Standardization of mist net captures for

quantification of avian migration. Bird-Banding 47:44-47.

Richardson, W. J. 1978. Timing and amount of bird migration

in relation to weather: a review. Oikos 30:224-272.

Robbins, C. S. 1968. Net hours: a common denominator for

the study of bird populations by banders. EBBA News

31:31-35.

Robbins, C. S., D. Bystrak, and P. H. Geissler. 1986. The

Breeding Bird Survey: its first fifteen years, 1965-1979.

U.S. Fish Wildl. Serv., Resour. Publ. 157. 196 pp.

Stewart, P. A 1987. Decline in numbers of wood warblers in

spring and autumn migrations through Ohio. N. Am. Bird

Bander 12:58-60.

Titus, K., and J. A Mosher. 1982. The influence ofseasonality

and selected weather variables on autumn migration of

three species of hawks through the central Appalachians.

Wilson Bull. 94:17&-184.

Yahner, R. H. 1983. Seasonal dynamics, habitat relationships,

and management of avifauna in farmstead shelterbelts. J.

. Wildl. Manage. 47:85-102.
AVIAN POPULATION TRENDS 41

Sources of Migrant Hawk Counts For Monitoring Raptor

Populations

by

Mark R. Fuller

U.S. Fish and Wildlife Service

Patuxellt Wildlife Research Center

Laurel, Maryland 20708

and

Kimberly Titus1

U.S. Fish and WildlIfe Service

Virginia Cooperative Fish and Wildlife Research Unit

Department ofFisheries and Wildlife Sciences

Virginia Polytechnic Institute and State University

Blacksburg, Virginia 24061

Introduction

Many species of Falconiformes and Strigiformes

become concentrated during spring and fall migration,

at which time they can be counted much more readily

than when they are dispersed during the breeding

season. Depending on species and season, migrant

raptors can be seen in numbers oftens to thousands per

day at certain geomorphologicalsites and during certain

meteorological conditions (Haugh 1986). Since the

1960's, yearly counts of migrants have been used to infer

changesin the population sizes ofraptors (e.g., Spofford

1969; Hackman and Henny 1971; Robbins 1975; U.S.

Department of the Interior 1976). With the yearly

accumulation of counts, including counts from new sites

since the late 1970's and early 1980's, biologists recently

have undertaken more detailed analyses of counts with

the goal of describing population trends (Dekker 1984;

Husse1l1985; Mueller et a1. 1988; Ward et a1. 1988). In

this paper we briefly relate the history of standardized

counts of migrant raptors (hawk counts), describe

counting and data recording procedures, and discuss

the analysis and interpretation of data from six sites in

North America (Titus et a1. 1989, 1990).

Ipresent address: Alaska Department of Fish and Game, Division of

Wildlife Consetvation, P.O. Box 20, Douglas, Alaska 99824.

History ofHawk Counts

Since the late 1800's, American ornithologists have

described concentrations of migrating raptors (Stone

1887;Trowbridge 1895; Broun 1935; Allen and Peterson

1936). By the early 1970's the number of sites at which

migrant raptors were observed had exceeded 100, and

biologists and amateurs had begun to count raptors,

record data, and study migration (Haugh and Cade

1966; Hofslund 1966; Mueller and Berger 1%7; Ward

and Berry 1972; Evans 1975). Interest in raptor

migration grew to such an extent that a planning

committee organized the 1974 North American Hawk

Migration Conference (April 1974, Syracuse, New

York). Harwood (1975a) wrote that it was organized, in

part, to resolve problems associated with using the

results of hawkwatching. He cited as impediments to

further study of raptor migration the inconsistencies in

data recording methods and the inability to readily

obtain reports from a number of sites or from a number

ofyears. The conference resulted in the formation ofthe

Hawk Migration Association of North America

(HMANA) to enhance communication among

observers and to act as a repository for data (Harwood

1975b). The Newsletter of HMANA was initiated to

disseminate count summaries and articles. Also, a

committee developed brief instructions and a form on

which observations and counts could be recorded.
42 BIOLOGICAL REPORT 90(1)

Observers were encouraged to complete a form for each

day during which counts were made at a site, and then

send the form to a regional editor who would check the

records for clarity and completeness before sending

them to the central repository. The HMANA stored the

forms and made them available for study.

Robbins (1975) presented the only paper at the 1974

North American Hawk Migration Conference that

addressed using hawk counts for monitoring

populations. He described the early history of

hawkwatching, and referred to a program of the early

1950's sponsored by the Bureau of Sport Fisheries and

Wildlife to accumulate counts of migrants as a

population index. Robbins (1975) cited the analysis of

hawk counts that he and J.L. Ruos conducted for

consideration in a migratory bird treaty with Mexico

(Anonymous 1971). Robbins (1975) emphasized the

necessity of standardization and consistency of

observation and reporting procedures if hawk counts

were to be used for monitoring population changes.

The HMANA first distributed forms in the fall of

1974, and in 1975 volunteers returned forms from 85

count sites. From the outset HMANA recognized the

usefulness of maintaining a computerized data bank.

The U.S. Fish and Wildlife Service (Service)

collaborated with HMANA to evaluate the potential for

transferring data from HMANA forms to a computer.

The Service was hopeful that a hawk count data bank

could be used in its efforts to monitor migratory bird

population trends. Evaluations using forms from 1974

through 1978 revealed that there were still many

inconsistencies in observation methods, data recording

(e.g., use of different measurement units), and editing,

and that there were many errors and incomplete data.

Based on the evaluation and consultation with HMANA

members and research and management biologists,

more detailed instructions for recording data and a

revised form (Figure) were adopted (Fuller and

Robbins 1979). In 1979 the Service's Office ofMigratory

Bird Management began collaborating with staff at six

countsitesto obtain more complete and consistent data.

Titus et al. (1990) began evaluating the data from the six

sites which include: fall countsfrom 1976 to 1987 at Cape

May, New Jersey (Allen and Peterson 1936); spring

counts from 1979 to 1987 at Derby Hill, New York

(Haugh and Cade 1966); fall counts from 1972 to 1987

at Hawk Mountain, Pennsylvania (Broun 1935), and

1972 to 1987 at Hawk Ridge, Minnesota (Hofslund

1966); and spring counts from 1979 to 1987 at Sandy

Hook, New Jersey (Clark 1978), and 1980 to 1987 at

Whitefish Point, Michigan (Devereux et al. 1985).

Records from these six sites are more complete and

contain fewer errors than records from sites

contributing to HMANA in the 1970's. There are some

missing data in the records from the six sites (Titus et al.

1989) and, in some cases (e.g., weather data), missing

data can preclude or confound some analyses (Hussell

1985). However, the records from these sites have been

useful for exploring methods for detecting trends in

counts of migrant raptors (Titus et al. 1990). The Office

of Migratory Bird Management continues to

collaborate with contributors from the six sites to

accumulate hawk count data (J. Trapp, personal

communication). Data from 1980 through the present at

all sites except Hawk Mountain have been summarized

at Patuxent Wildlife Research Center (Titus et al.

1989, 1990). The long-term (1934 to present) data from

the Hawk Mountain Sanctuary data bank are being

analyzed in detail by Sanctuary staff. The six data sets

vary in size, with sites such as Cape May and Hawk

Mountain averaging slightly more than 100 days of

observation per year, while counts at Whitefish Point

have been conducted about 60 days per year.

Currently HMANA is evaluating its criteria for

accepting data forms and for maintaining a computerized

data bank (M. Harwood, personal communication).

Some data are available for study from HMANA (c/o

Myriam Moore, Secretary, P.O. Box 3482, Lynchburg,

.Virginia 24503), and the association can provide

addresses of individuals working at each of the sites.

Hawk Counts and Data Recording

Hawk counts have been conducted at specific sites

simply because observers are often rewarded with larger

numbers of raptors than can be seen in the adjacent

countryside. Descriptions of 16 sites and examples of

species counts are given by Harwood (1975c:5-28).

Also, counts of migrants are collected at some sites by

persons who are trapping and banding birds (Mueller

and Berger 1967; Ward and Berry 1972; Evans 1975).

The data analyzed by Titus et al. (1990) were gathered

by biologists who only counted hawks.

Haugh (1986) summarized how topographical

features such as mountain ridges and shorelines, the air

currents (e.g., updrafts) associated with these features,

meteorological conditions, and seasonal timing are

believed to influence concentrations of migrants. The

behavior of migrating raptors has been studied recently .

by Kerlinger (1985), Kerlinger and Gauthreaux (1985),

and Kerlinger et al. (1985). Information about migrant

behavior and the influence of weather, seasonality, and

daily rhythms on flights is often used by hawkwatchers

to plan counts for those times when large flights are

likely. However, we do not know of any statistically

based sampling designs in the scheduling of counts.

Titus et al. (1989) presented examples of how sampling

designs could be applied to hawk counts.

We are not aware of routines for sampling portions

of the sky, or for sampling intervals during the time an
AVIAN POPULATION TRENDS 43

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and the U.S. Fish and Wildlife Service to establish a computerized data bank on hawk migration.
44 BIOLOGICAL REPORT 90(1)

observer is on the watch site. There are some general

guidelines for hawkwatching and counting (e.g., Dunne

et a1. 1984; Heintzelman 1986), and it is known that

factors such as fatigue (Sattler and Bart 1984), multiple

observers (Hussell 1985), and location of observer

(Kochenberger and Dunne 1985) can affect counts.

However, we have not found instructions for dealing

with these factors when conducting counts for the

purpose of monitoring.

The HMANA instructions relate to the types of

data to be gathered and describe how to complete the

standard form to be used at all sites contributing to the

HMANA data bank. The instructions stress

completeness and neatness when filling in the form

(Figure). There is a column to indicate that a comment

has been made elsewhere in reference to information

on the form, and observers are encouraged to supply

additional information. We stress the importance of

completing the section containing observation

conditions because it includes factors that can

contribute to variability among counts. There is space

for an estimate ofmaximum visibility and for recording

temperature (a metric conversion table is included). A

"sky code" is provided for describing the percentage

of cloud cover or type of precipitation, along with a

code for wind speed and wind direction. There is a

code for estimating the altitude of flight. Finally, there

is space to record the numbers of observers each hour,

and the total minutes during which counts were made

each hour.

Considerations for Using Hawk Counts

The observation and recording ofhawk counts causes

some variability in data from within and among sites.

Fuller and Robbins (1979) and Titus et a1. (1989) found

illegible data entries and missing data due to periods of

no observation, as well as missing entries during

observation periods. For example, 3-5% of the weather

data were not gathered or were omitted on the forms

from the six sites (Titus et a1. 1989). Weather data can

be important in accounting for variability in hawk counts

(HusseIl1985).In addition to observer recording errors,

observer fatigue (Sattler and Bart 1984), and number of

observers (HusselI 1985), variability in counts is

confounded by the species of raptor (Sattler and Bart

1984), the altitude of the passing migrants (Sattler and

Bart 1984; Kerlinger 1985; Smith 1985), and the number

ofbirds in a flight (Kochenberger and Dunne 1985).

Yearly counts vary widely among species and sites.

For example, the mean count of golden eagles (Aquila

clrf).saetos) was 9.1/year (11 = 8) at Whitefish Point, and

the mean count of sharp-shinned hawks (Accipiter

striatus) was 39,632/year (11 = 12) at Cape May (Titus

et aI. 1990). Titus et a1. (1989) found a coefficient of

variation (C.V.) of about 40% for the counts of all

species from six sites. However, for many individual

species, the C.V. stabilized around 25% after counts

from 10 years or more were accumulated for a site. The

extent to which data about sources of variability can be

useful for interpreting counts is evidenced in multiple

regression analyses by Hussell (1985). He found that

69% of the variability in counts (birds per hour) of

sharp-shinned hawks, and 52% of the variability for

broad-winged hawk (Buteo p/atypterns) counts, was

explained by independent variables (e.g., weather, date,

year). The careful recording of data relevant to sources

of variability is important for using hawk count data to

monitor populations.

Suggestions for Future Work

Study of several other aspects of raptor migration

would be useful for population monitoring (Fuller 1979;

Haugh 1986). There is little information about the origin

and destination of migrants. Banding data provide only

very general information about the long-distance

movements of migrants (Clark 1985; Evans and

Rosenfield 1985; Yates et a1. 1988). Thus, a significant

change in a migration count cannot be associated with

clearly defined breeding or wintering areas.

Furthermore, the extent to which migrants use the same

migration routes from year to year has not been

quantified. Thus a change in count from one area might

reflect a shift in flight path or a fluctuation in population

size. Additional study is needed to determine the value

of pooling data from severalsites (e.g., Titus et a1. 1990),

and to determine if data from spring counts are less

variable than those from falI counts. Analytical

considerationsinclude topics such as the implications of

autocorrelation among yearly counts, the use of

standardization (e.g., hawks per hour of observation),

and appropriate statistical tests. Counts of migrating

raptors have been used in migratory bird management

(e.g., U.S. Department of the Interior 1976, 1983, 1987)

and have the potential to provide even more useful data

as we learn more about raptor migration.

Acknowledgments

We thank S. S. Klugman for help in preparing this

manuscript, and W. S. Seegar, F. P. Ward, and the U.S.

Fish and Wildlife Service, Office of Migratory Bird

Management, for support of our studies of migration.

S. Droege and J. R. Sauer provided reviews.

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Allen, R. P., and R. T. Peterson. 1936. The hawk migrations

at Cape May Point, New Jersey. Auk 53:393-404.
Anonymous. 1971. Trends in populations of raptors in North

America special briefing summary. Congressional Record:

Committee on Merchant Marine and Fisheries, House of

Representatives 92nd Congress 1971:97-109.

Broun, M. 1935. The hawk migration during the fall of 1934,

along the Kittatinny Ridge in Pennsylvania. Auk

52:233-248.

Clark, W. S. 1978. Spring hawk movement at Sandy Hook, N.J.

1977. N.J. Audubon 4:43-47.

Clark, W. S. 1985. The migrating sharp-shinned hawk at Cape

May Point: banding and recovery results. Pages 137-148 in

M. Harwood, cd. Proceedings of the Hawk Migration

Conference, IV. Hawk Migration Association of North

America, Rochester, N.Y.

Dekker, D. 1984. Spring and fall migrations of peregrine falcons

in central Alberta, 1979-1983, with comparisons to

1%9-1978. Raptor Res. 18:92-97.

Devereux, J., T. Carpenter, and K. Durham. 1985. Spring

migration patterns of sharp-shinned hawks passing

WhitefISh Point, Michigan. J. Field OmithoI. 56:346-355.

Dunne, P., D. Keller, and R. Kochenberger. 1984. Hawk watch:

a guide for beginners. Cape May Bird Observatory and the

N.J. Audubon Society, Cape May, N.J. 80 pp.

Evans, D. L. 1975. Fall owl migration at Duluth, Minnesota.

Loon 47:57.

Evans, D. L., and R N. Rosenfield. 1985. Migration and

mortality of sharp-shinned hawks ringed at Duluth,

Minnesota, U.S.A. Pages 311-316 in I. Newton and R. D.

Chancellor, cds. Conservation studies on raptors. Int.

Counc. Bird Preserv. Tech. Publ. 5.

Fuller, M. R. 1979. Hawk count research. Newsl. Hawk

Migration Assoc. N. Am. 4:1-2.

Fuller, M. R, and C. S. Robbins. 1979. Report forms. News!.

Hawk Migration Assoc. N. Am. 4:2-3.

Hackman, C. D., and C. J. Henny. 1971. Hawk migration over

White Marsh, Maryland. Ches. Sci. 12:137-141.

Harwood, M. 1975a. Introduction. Pages 1-4 in M. Harwood,

cd. Proceedings of the North American Hawk Migration

Conference, 1974. Hawk Migration A<;sociation of North

America, Washington Depot, Conn.

Harwood, M. 1975b. Organization of the Hawk Migration

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M. Harwood, cd. Proceedings ofthe North American Hawk

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5-28 in M. Harwood, cd. Proceedings of the North

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Haugh, J. R 1986. Raptors in migration. Pages 35-48 in S. E.

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Hofslund, P. B. 1966. Hawk migration over the western tip of

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America, Rochester, N.Y.

Kerlinger, P. 1985. Daily rhythm of hawk migration, noonday

lulls, and counting bias: a review. Pages 259-265 in

AVIAJ'i POPULATION TRENDS 45

M. Harwood, cd. Proceedings of the Hawk Migration

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Kerlinger, P., V. B. Bingman, and K. P. Able. 1985.

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with tracking radar during autumn in central New York.

Can. J. Zool. 63:755-761.

Kerlinger, P., and S. A Gauthreaux, Jr. 1985. Seasonal timing,

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hawks during spring migration in south Texas: a radar and

visual study. Auk 102:735-743.

Kochenberger, R, and P.J. Dunne. 1985. The effects ofvarying

observer numbers on raptor count totals at Cape May Point,

NewJersey. Pages 281-293 in M. Harwood, cd. Proceedings

of the Hawk Migration Conference, IV. Hawk Migration

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Mueller, H. c., and D. D. Berger. 1%7. Wind drift, leading lines

and diurnal migration. Wilson Bull. 79:50-63.

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55:415-523.

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monitoring raptor population trends based on counts of

migrants. Pages 19-32 in B. U. Meyburg and R D.

Chancellor, eds. Raptors in the modern world. Proceedings

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Birds of Prey and Owls. Berlin, West Germany.

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assessment: proposed falconry regulations. Fish and

Wildlife Service, WaShington, D.C. 61 pp.

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F. p. tundrius from endangered to threatened. Federal

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birds of management concern in the U.S.: the 1987 list.

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61 pp.
46 BIOLOGICAL REPORT 90.(1)

Ward, F. P., and R. B. Berry. 1972. Autumn migrations of

peregrine falcons on Assateague Island, 1970--1971. J.

Wildt. Manage. 36:484-492.

Ward, F. P., K. Titus, W. S. Seegar, M. A. Yates, and M. R.

Fuller. 1988. Autumn migrations of peregrine falcons at

Assateague Island, MarylandNirginia, 1970--1984. Pages

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of peregrine falcons migrating through the eastern and

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AVIAN POPULATION TRENDS 47

The Common Birds Census in the United Kingdom

by

Raymond J. O'Connor

Department of Wildlife

College ofForest Resources

University ofMaine

Orono, Maine 04469

Introduction

The Common Birds Census (CBC) in Britain is based

on the use of a territory mapping method developed in

Sweden by Enemar (1959), subsequently modified by

Williamson and Homes (1%4) for use in Britain.

Spot-mapping censuses conducted on plots spread

throughout Britain are used to provide an annual index of

population trends in the species covered. The scheme was

started by the British Trust for Ornithology (BTO) in 1962

at the request of the United Kingdom's statutory Nature

Conservancy (now the Nature Conservancy Council

[NCC]), with the aim of monitoring bird population

numbers in Britain. The immediate stimulus for the

initiation ofthe scheme was concern over the growing use

of agricultural chemicals in Britain, where large-scale

mortalities associated with the use of persistent

organochlorine chemicals were frequently noted

throughout the 1950's (Cramp et al. 1962). The concept

of the CBC was to provide a bird population monitoring

scheme as a bio-indicator, providing an early warning of

future environmental problems. In this way the scheme

might allow the NCC or other agencies to start research

to find solutions before the problem(s) became severe. A

second factor of concern in farmland was the accelerating

destruction of hedgerows in Britain, which in the early

1960's were being removed for field enlargement at a rate

of up to 1.8 m/ha (about 1.5% of the total) each year

(O'Connor and Shrubb 1986).

In 1%4 the CBC was extended to encompass

woodland bird populations. In this hahitat the census

methods differ in detail from those appropriate to

farmland (Williamson 1964). Many of the species on

farmland are generalists of woodland origin and appear

in both indexes, but a number of species are largely

confined to the woodland habitat and are, therefore,

covered only by the woodland CBC index. Additionally,

several scarce species occurring in low numbers both in.

farmland and in woodland have hecn monitored through

a Scarce Species Index. This index is obtained by merging

the available data from the farmland and woodland plots

with data from a number of special sites that are neither

farmland nor woodland but which for various reasons

have had CBC census work undertaken on them. Such

sites include, for example, some bird observatories and

nature reserves. Thisindex issusceptible to changesin the

representation ofthe different habitats within the sample,

but does provide some crude measure of large-scale

population trend (Batten 1971).

A linear analogue of the CBC, the Waterways Bird

Survey (WBS), was started in 1964 as a means of

monitoring riparian bird populations along natural and

man-made watercourses. The linear nature of the

census plots in this scheme greatly simplifies the

problems to be addressed (review in Taylor 1983).

Design ofthe Survey

Census Plot Sample

As a national monitoring scheme, the CBC should·

have plots distributed across the country in a manner

representative of farmland and woodland habitats. In

practice, the scheme is limited by the distribution ofthe

BTO membership, which is concentrated in the

southeast of England. Historically, the CBC has had

about 300-350 plots censused in any given year, with this

total partly inflated by a significant number ofthe special

plots outside the main farmland and woodland habitats.

Up to a quarter of the observers leave the scheme each

year and are replaced by new observers, and since data

from a plot must be paired across years, not all plots can

be used for a given year's index. As a working target for

usable results, about 100 farmland and 100 woodland

plots are regarded as satisfactory. Current practice

requires that a species be recorded on a minimum of 30

plots each year if an index value is to be calculated for

it, and a national sample of 100 therefore implies that
48 BIOLOGICAL REPORT 90(1)

only species present on 30% or more of the plots can be

covered by a CBC index. In recent years, there has been

some difficulty in meeting this criterion for all species,

and some ofthe published indexes have fallen below this

limit. In 1986-87, the most recent period for which data

were available at the time of writing, some 78 farmland

and 80 woodland plots contributed to the index

calculations, and 9 of the 49 population changes

reported were based on fewer than 30 plots, the lowest

being based on 17 plots (Marchant and Whittington

1988). While there is no definitive statistical basis for the

cutoff at 30 plots, smaller samples are increasingly

vulnerable to the random walk effect described by

Geissler and Noon (1981). The statistical basis for the

historic success of the CBC thus seems to be in danger

of being eroded by the dwindling sample size.

CBC plots are self-selected by the observers, and

their geographical distribution is therefore not

stratified, but the CBC staff make some effort to ensure

that some plots are censused in most parts ofthe country

each year. In practice, this has been moderately

successful in maintaining regional coverage. Fuller et al.

(1985) found no evidence of any geographical shifts in

the distribution of farmland CBC plots from 1965 to

1981. They also reported an analysis of the extent to

which these plots mirrored land class and farming

practicesin Britain. They found that the CBC plots were

reasonably representative of agriculture in lowland

England, specifically south of a line about latitude

54°3O'N and east of longitude 3°3O'W. North and west

of these lines, however, certain land classes and farm

types were underrepresented. There has been no

serious attempt to evaluate how representative of

woodland the historic CBC plots have been.

Fieldwork at a Census Plot

The CBC requires that observers visit a defined

census plot 8-12 times through the breeding season. The

observers agree on the plot boundary with BTO staff

before starting. Plots on farmland must exceed 40 ha

(100 acres) to be minimally acceptable, with 60 ha (150

acres) preferred, but woodland plots can be as small as

10 ha (25 acres). (In 1986--87 mean areas of farmland

and woodland plots were 73.1 ha and 20.7 ha,

respectively.) The BTO provides the observers with

copies of a large-scale (1:2,500) map of the plot. On

these maps, observers record the locations of all birds

seen or heard, including all information relevant to

territorial or breeding behavior. The registrations are

plotted using standard symbols to indicate the different

types of evidence obtained, such as song and alarm calls;

behavior such as fighting, carrying food, or movements

within the plot; the location of nests found (although no

specific search for nests is required); the sex ofthe birds

seen; and family parties seen. Considerable emphasis is

put on the recording of contemporaneous registrations

of different birds because such registrations guarantee

that two territories are involved.

Standard coverage of a CBC plot is 10 complete

mapping visits during a breeding season that, in

southern Britain, runs from mid-March to late June.

This number is sufficient to detect a large proportion of

territories on the census plot under British conditions,

while a larger number yields rapidly diminishing returns

(Svensson 1979; O'Connor and Marchant 1981). In the

midlands the field season may be later, with first visits

undertaken in early April, and in northern Scotland

visits may extend into early July. Visits are spaced evenly

throughout the season, insofar as weather and the

availability of the observer's time permit, with weekly

visits regarded as ideal. Partial visits are allowed in three

circumstances: (1) where a full visit has to be abandoned

due to heavy rain, (2) to obtain special types of

registration (e.g., of crepuscular or nocturnal species),

and (3) where a large plot ofspecial interest to the BTO

has been designated for a group census in which each

group member censuses only part of the plot.

Morning visits are the norm, but evening visits may

be especially valuable in the early part of the season

when certain species (e.g., song thrush, Tllrdlls

philomelos), sing to greatest extent late in the day.

Observers are instructed not to census around dawn,

when bird detectability may change very rapidly, nor

during the afternoon lull in activity. Field time is

recorded and used in conjunction with the number and

type of visits to provide a rough check on consistency of

effort across years, because field time is a significant

correlate of census efficiency (O'Connor and Marchant

1981; O'Connor 1981c). On farmland, observers are

advised to spend about 1 h per 20 ha of plot and are

expressly asked to go into all major habitat patches

(chiefly hedgerows and small woodlands) and along the

perimeter of the plot. They are allowed to spend more

time in areas of high bird density than in areas of low

density. In woodland, a plot of 20 ha would require 3-4 h

of fieldwork, with the observers free to select their

routes within the plot but asked to come within 25 m of

every point therein. Two habitat-related differences are

of note. First, farmland generates relatively more sight

registrations and woodland relatively more sound

registrations. Second, woodland plots often need prior

gridding for accurate mapping, while the matrix offields

and hedges on farmland often provides a natural

reference net. The CBC Instructions also suggest

varying the start point for different visits, to minimize

potential for absolute bias, though always with emphasis

on across-year consistency.

Not all species lend themselves well to territorial

mapping, and the census is therefore supplemented for
such species by nest counts where nesting is conspicuous

(e.g., grey heron, Ardea cinerea) or colonial (e.g., sand

martin, Riparia riparia). Other species (e.g.,

woodpigeon, Columba palwnbus, swallow, Himndo

mstica, and starling, Stumus vulgaris) are more easily

censused by nest counts than by territory mapping, but

at a cost in time unacceptable to some observers. For

these the population estimate is taken from a

comprehensive nest count where possible and a

combined nest and territory total otherwise.

Since registrations have later to be assigned to

individual clusters, the problem of mapping at the edge

of the census plot can be significant (Marchant 1981;

Verner 1985). An arbitrary cutoff is unsatisfactory, for

observers may then tend to record registrations just

outside the boundary as being inside it. This applies also

where the plot is embedded in a larger area of the same

habitat. On farmland, observers are instructed not to use

imaginary boundaries running, for example, through the

middle of a field, but to use physical, identifiable

boundaries. At the same time they should not use

especially rich areas, such as a diverse shelter-belt, for

this purpose. With these guidelines they then record all

registrations near the boundary, but map them in their

true locations, whether inside or beyond the boundary.

This then permits the analysts to subsequently assign

these boundary territories in some appropriate manner,

currently by including them if more than one half of the

registrations lie inside the plot boundary and excluding

them otherwise. Even with this rule, however, a small

residual overestimate of density is possible, because

observer coverage is greater within the plot boundary

(Marchant 1981).

Additional Field Data

Observers are asked to submit plot habitat data to the

BTO. A full habitat map ofthe plot is requested for the

first year of a plot census, but in subsequent years only

habitat changes and year-specific information (such as

cropping plans for farmland) are required. In theory

each observer also completes a habitat questionnaire to

accompany such maps, but in practice these returns are

incomplete, to the point of limiting some analyses

otherwise feasible.

Farmland habitat details describe the landscape

elements of the plot - the location of hedgerows and

small woodlots, their species composition, the size and

shape (a function of management) of the hedges, and

the location, species, and size of standard (i.e.,

well-grown) treesin the hedges. Watercourses and small

ponds are also indicated, as is the basic topography of

the plot. Annual cropping plans show the crops used in

each field, sometimes with quite detailed information on

their management. Landscape elements are readily

AVIA.!Ii' POPULATION TRENDS 49

recognized by observers, and information is often very

well recorded in the farmland returns. For woodland

plots, however, the instructions' call for structure and

management information that is much harder to relate

to without ecological training, and data received are

rather poor. In practice these data have been discarded

by researchers, and most ofthe studies ofwoodland bird

habitat requirements have been based on data gathered

expressly for the purpose by BTO staff and others.

Analysis ofCensus Data

On completion of the fieldwork each season, the

information from the visit maps is collated by the

observer onto species maps, with the registrations

typically falling into (presumed territory) clusters.

Significant difficulties arise over the interpretation of

such data (Svensson 1974; Best 1975; Fuller and

Marchant 1985). The CBC minimizes these problems by

using trained staff and standard rules (contained in the

CBC manual Guiding Principles and similar to those

adopted by the International Bird Census Committee

[Anonymous 1969]) to produce definitive cluster maps.

The observers provide preliminary analyses that often

assist the professional analysts. In interpreting the

clusters as territories, emphasis is placed on using

singing males and on contemporaneous registrations,

with the intention of maximizing the agreement between

the total number of clusters and the total numher of

territories on the plot.

Agreement between the location and number of

clusters and the location and numbers of territories on

the census plot is affected by the efficiency of the field

worker and by bird behavior. Within a given type of

habitat, the behavior of each species is likely to be

relatively constant but to differ between species. Hence,

a given observer detectsthe different species at different

rates. However, individual observers may differ in their

personal census efficiency, so that they would obtain

different results were they to census the same census

plot. In practice it is impossible to normalize these

efficiencies to a common value for each species. The

CBC therefore describes population levels through the

construction of a CBC Index (Bailey 1967) whose

calculation effectively eliminates the observer census

efficiencies. This is achieved by pairing the plots across

years and computing a pooled estimate of the

percentage change between the 2 years. These

year-on-year relative changes are then chained together

on a base year value (for most species arbitrarily set to

100 in 1966) to generate an index proportional (but by

an unknown factor) over time to the true density of the

species. However, this index requires that each observer

remain constant in efficiency, even though the level of
50 BIOLOGICAL REPORT 90(1)

this efficiency is actually unknown and may differ from

one observer to another.

Completion of the analysis of each year's returns

typically requires the work of three to four analysts

from August through April. An interim

mimeographed report based on the early returns is

circulated to participants midway through this period,

and a short annual report is published in the Trust's

newsletter, BTO News.

Constraints on the Analysis ofthe

Survey

Limitations on the CBC scheme are of two types,

those imposed by assumptions intrinsic to the design of

the survey and those imposed by external constraints on

its staffing. Five major assumptions are made within the

CBC design: (1) census efficiency may differ between

observers without biasing the index; (2) individual

observers are constant in census efficiency over time; (3)

the average efficiency of the pool of observers does not

drift with time, despite the turnover in participants; (4)

on average the characteristics ofthe plots censused are

constant over time; and (5) variability of map·

interpretation can be eliminated with training.

Internal Constraints

Any census scheme may seek either to obtain

absolute estimates of population density, as pairs

breeding per unit area, or to obtain measures of

relative abundance. For the CBC the absolute census

efficiencies for each observer and species are

unknown. Hence, absolute densities cannot be

computed (though see below), and only relative

abundances can be established. Relative abundance

measures should ideally be related to density by a

linear function of the form

whereDj is the density ofbirds on the plotj,lj isthe relative

measure ofthe species' abundance on the jth plot, and aj

and bj are constants. Since these constants are, in general,

species-, plot-, and observer-specific, relative densities

can be compared neither across species nor across plots.

Changes in density are correctly indexed over time,

though, provided census efficiency remains constant from

year to year. This applies even if the values of the

coefficients a and b are unknown.

In the CBC scheme, data from many census plots are

collated to yield an overall index, and the resulting

confidence intervals are therefore realistic, though

based on certain assumptions. Since the percentage

change from year 1 to year 2 is independent of the

particular census efficiency aj of the jth observer,

constancy of observer efficiency is essential. Were the

same observers to continue censusing the same plots in

the long term, no further safeguards would be necessary.

In practice some 19-24% of the observers leave each

year, and replacement observers and plots must be

recruited to maintain the scheme. In practice, too, the

number of plots in the scheme in a given year may vary.

These operational features of the scheme impose the

additional requirements that the characteristics of

observers and the characteristics of their plots on

average do not change over time.

These assumptions have been supported by

examinations ofthe internalstatistical consistency ofthe

CBC data (Taylor 1%5; Mountford 1982) and by a field

experiment in which four observers who differed

significantly in individual census efficiency and in

duration of their census visits were found to remain

adequately consistent in these respects over the 2 years

tested (O'Connor 1981c; O'Connor and Marchant

1981). Although the duration of the sample consistency

has been studied less, it has been shown to be at least

5 years for several species (Mountford 1982, 1985). This

time-span will ensure significant turnover of observers

and suggests that requirements for average observer

efficiency are met and remain consistent. A major

limitation to such checking is that farmland

management in Britain changes rapidly in response to

technical and economic developments (O'Connor and

Shrubb 1986), and apparent breakdown in consistency

may reflect these changes.

Despite these checks, the average size of farmland

plots sampled has decreased markedly (about 32%)

between 1%1 and 1980 (Fuller et al. 1985). Because

small plots have a greater edge effect than larger plots

(Marchant 1981; Verner 1985), this downward drift in

average plot size could introduce some bias into the

CBC index, a possibility which has not been checked.

The composition ofthe CBC sample with respect to land

class, cropping regimes, and regional distribution did

not vary significantly over this period.

These features of the CBC allow some types of

analysis but preclude others. First, analysis ofsingle year

changes across species are valid. O'Connor (1981a)

showed that after the extraordinarily severe winter of

1%2-63 in which many resident birds died, the 1983--84

population increases of the species involved were well

correlated with their annual egg production. Second,

analyses of temporal trends are valid, and several such

studies have been undertaken. Because the CBC index

does not track the national population accurately, the

plots being nonrandomly distributed, such analyses

need to involve large-scale phenomena to be successful.

Winstanleyet al. (1975) documented a marked decline

in the number of whitethroats (Sylvia communis)
breeding in Britain. This was based on CBC data and

due to drought in the Sahelian zone of Africa, where

much of the population winters. O'Connor and Mead

(1984) have similarly documented the eightfold

recovery of stock dove, Columba oenas, following the

introduction of controls on the use of persistent

organochlorine compounds in Britain. Alternatively,

the spatial bias can be acknowledged and controlled by

suitable choice of regional samples, a procedure used

successfully by O'Connor and Shrubb (1985, 1986).

One limitation to using CBC index values is that the

values for all species were arbitrarily set to 100 in the

1966 base year. (Actually, certain species were too

scarce in 1966 to appear in an index in that year and have

been assigned other base years.) Hence, the index values

cannot be used to compare numbers across species

without first finding some way of estimating their

species-specific density multiplier in the base year,

though their relative changes can validly be compared.

To estimate absolute densities from CBC data, the

census efficiency coefficients (aj and b) ideally should

be known for each plot. However, if variation in these

coefficients across plots is small relative to the variation

in bird numbers between plots, absolute densities can be

obtained on the basis of an independent estimate of the

average value of a. Experimental evidence suggests that

the number of clusters found for each species using the

CBC method may not vary greatly between observers.

Among four observers, the lowest total of clusters

recorded (all species combined) was 70% of the largest

cluster (O'Connor 1981c), though the size of this effect

varies between species (O'Connor and Marchant 1981).

This can be compared with the more than two-fold

variation in numbers across species recorded in

Hickling (1983).

Cluster totals for a plot can only approximate

population size there. Spurious clusters may arise at

shared feeding sites; territories may be large relative to

the census plot; registrations may be typically diffuse,

especially with secretive species; double registrations of

the same bird on a single visit may occur; and multiple

sightings of birds in conflict or in parties may be difficult

to interpret (Fuller and Marchant 1985). Nevertheless,

in a field trial, Snow (1965) showed that for most species

a census based on eight visits gave good approximations

to the population actually present. Other studies based

on the international mapping method lend some

support to this conclusion (Diehl 1974; Enemar et al.

1979; Tomialojc 1980; Verner 1985), some suggesting

that good agreement between mapping and estimates

made with marked birds or with playback has been

obtained only through chance balancing of errors. In the

one overview of existing studies published to date,

territory mapping and independent assessment of the

number of breeding pairs present were correlated

AVIAN POPULATION TRENDS 51

across species from 0.69 to 0.97 (O'Connor and

Marchant 1981). The use of CBC clusters probably

ranks densities fairly well, but may not be absolutely

accurate as an indicator of absolute densities (Svensson

1980; Dawson and Verner, unpublished report). A

particular problem not yet adequately addressed for the

CBC is whether observers "saturate" (and therefore

underestimate) in the presence of high densities of

birds, as demonstrated by Bart and Schoultz (1984) in

the United States.

On the assumption that cluster densities from the

CBC scheme are adequately indicative ofreal densities,

variations in numbers between plots or classes of plots

can be investigated in relation to plot characteristics.

Osborne (1982) used this technique to demonstrate the

effects of Dutch elm disease on farmland bird

populations in Britain, and O'Connor (1981b) used it to

examine habitat correlates of farmland birds. Wilson

(1977), Williamson (1967), and O'Connor and Shrubb

(1986) likewise used the approach to compare regional

densities of birds. The census maps for individual plots

can also be overlaid on the corresponding habitat maps

to identify habitat correlates of bird distribution

(Williamson 1968,1970).

&temal Constraints

The CBC is extremely labor-intensive, both in the

field and in the office. For example, in 1982, some 360

participants spent about 13,000 h in the field and

generated about 14,000 species maps in about 6,000 h of

subsequent collation (O'Connor and Fuller 1984). At

the BTO the scheme each year requires the service of

four scientific and two clerical-secretarial staff. The

time of the participants is given voluntarily, but staff are

salaried under a contract from the Nature Conservancy

Council. Most of this staff time is devoted to the map

interpretation involved. With four analysts involved,

even moderate staff turnover results in one post being

under training at anyone time, and the resulting

cross-checking of analyses also slows down output ofthe

trainer. Yet this training is the key to the successful use

of the mapping method. O'Connor and Marchant

(1981) and O'Connor (1981c) showed that

inexperienced workers do not follow the written rules of

map interpretation closely enough to avoid significant

between-analyst variation. This was also the case in

studies by Svensson (1974) and Best (1975). However,

training successful\y eliminated this problem

(O'Connor and Marchant 1981).

The large staff commitment to this aspect ofthe CBC

severely limits the possihle output from the scheme,

given the existing budget. Automated cluster analysis

has been advocated (North 1977, 1980) to save

manpower, but the algorithms previously available have
52 BIOLOGICAL REPORT 90(1)

been unsatisfactory (Wragg 1982; O'Connor and Fuller

1984): their computation was poorly behaved and

involved a heavy computing load; they required

calibration by species and habitat against manual

interpretations; and they ignored much of the

qualitative information in the registrations. Map

digitization also proved as costly in time as manual

interpretation. Sheffer (1987) has since published

details of a new clustering algorithm which seems to

overcome the difficulties listed previously. Sheffer's

algorithm has been tested for 16 species and yielded

extremely good agreement with manual assessments of

the territories present, even without drawing on

qualitative information contained within the

registrations. If this technique proves applicable to the

CBC results, it will remove the need for highly trained

analysts to undertake map interpretation. It might also

remove the current need for observers to collate visit

maps to species maps, because visit maps could be

digitized directly and the registrations collated by

species using appropriate computer programs.

Scheffer's work may prove to be a major breakthrough

in reducing the manpower cost constraints on the use of

the CBC scheme data and opening up the possibility of

extensive analysis ofspatial patterns for which the BTO

data set is uniquely suitable.

Acknowledgments

This paper is based on work undertaken while I was

Director of the British Trust for Ornithology, and it was

funded under a contract from the United Kingdom

Nature Conservancy Council. My participation in the

Patuxent Wildlife Research Center workshop was funded

by the U.S. Fish and Wildlife Service. I am grateful to both

for their fmancial support. The comments of several

anonymous reviewers are also appreciated.

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standard for a mapping method in bird census work. Bird

Study 16:249-254.

Bailey, R. S. 1967. An index of bird population changes in

farmland. Bird Study 14:195-209.

Bart, J., and J. D. Schoultz. 1984. Reliability of singing bird

surveys: changes in observer efficiency with avian density.

Auk 101:307-318.

Batten, L. A 1971. An index of popUlation changes for some

relatively scarce species. Bird StUdy 18:13G-136.

Best, L. B. 1975. Interpretational errors in the "mapping

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Cramp, S., P. J. Conder, and J. S. A~h. 1962. The deaths of

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Diehl, B. 1974. Results of a breeding bird community census

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Fuller, R. J., and J. M. Marchant. 1985. Species-specific

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Fuller, R. J., J. M. Marchant, and R. A. Morgan. 1985. How

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Common Birds Census farmland plots? Bird Study

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Geissler, P. H., and B. R. Noon. 1981. Estimates of avian

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Estimating numbers ofterrestrial birds. Stud. Avian BioI. 6.

Hickling, R. A O. 1983. Enjoying ornithology. T. & AD.

Poyser, calton, U.K.

Marchant, J. H. 1981. Residual edge effects with the mapping

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J. M. Scott, eds. Estimating numbers of terrestrial birds.

Stud. Avian BioI. 6.

Marchant, J. II., and P. Whittington. 1988. 1986-87 CBC

index report. BTO News 157:7-10.

Mountford, M. D. 1982. Estimation of population fluctuations •

with application to the Common Birds Census. Appl. Stats.

31:135-143.

Mountford, M. D. 1985. An index of population change with

application to the C-ammon Birds Census. Pages 121-132

in B. J. T. Morgan and P. M. North, eds. Statistics in

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North, P. M. 1977. A novel clustering method for estimating

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North, P. M. 1979. A novel clustering method for estimating

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O'Connor, R. J. 1981a. Comparisons between migrant and

non-migrant birdsin Britain. Pages 167-195 in D.J. Aidley,

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O'Connor, R. J. 1981b. Habitat correlates of bird distribution

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Stud. Avian BioI. 6.

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O'Connor, R. J., and R. J. FUller, editors. 1984. A

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O'Connor, R. J., and J. H. Marchant. 1981. A field validation

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O'Connor, R. J., and M. Shrubb. 1985. Some effects of

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Taylor, K. 1983. A comparison of inter-observer and

inter-analyst agreement in the BTO Waterways Bird

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AVIA.1IJ POPULATION TRENDS 53

Taylor, S. M. 1965. The Common Birds Census-some

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Williamson, K., and R. C. Homes. 1964. Methods and

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247-302in R. F. Johnson, ed. Current ornithology, Vol. 2.

Plenum Press, New York.

Part II: Methods ofTrend Analysis

Papers in Part II of the workshop reflect some of the controversies that surround the estimation of population

trends from long-term surveys. Unfortunately, it is often unclear what constitutes a trend. Dagum and Dagum

(1988) divide time-series into four components: a trend, cycles, seasonal variations, and irregular fluctuations. They

define the trend as the component that "corresponds to sustained and systematic variations over a long period of

time," which is "associated with the structural causes of the phenomenon in question, for example, population

growth ...." The surveys considered in this workshop are all relatively short-term, and it is difficult to estimate a

trend that is independent of either short term fluctuations associated with weather or other cyclic components of

population change.

Also, ecologists are often interested in examining components of population change other than trend, and in

this case an estimate of trend does not provide an adequate description of regional population changes. An

underlying theme of several papers was the notion that trends are of little use in characterizing the dynamics of

populations, and that composite yearly indices for regions must also be estimated to provide insight into the other

components of the time-series. It is our opinion that the notion of estimating a long-term trend for a population is

relevant because a population cannot persist indefinitely with a consistently negative trend. The difficulties arise

in determining if a real trend exists in a short-term data set, or if the trend estimate is actually modeling another

component of the time-series. Regional annual indices of abundance provide a context for the trend estimates that

allows this determination.

Several approaches to estimating population trends and regional annual indices of abundance were discussed

in the workshop. In general, these approaches can be categorized as (1) methods that estimate regional yearly

indices of abundance and use the indicesto infer population trends, or (2) methodsthat estimate population trends

first, and then annual indices of abundance are estimated in the context of the population trends.

Unfortunately, for route surveys such as the North American Breeding Bird Survey, each route contains

attributes, such as years with missing counts and observer changes, that make simple yearly averages ofroute counts

biased estimates of yearly indices for a region (Geissler and Noon 1981). Consequently, most of the annual index

methods discussed in the workshop proposed (1) smoothing route data (using LOWESS methods, which

accommodate missing data but not observer differences) and then calculating regional yearly averages,

(2) estimating regional indices using linear models to adjust for missing yearly data and observer differences, or

(3) estimating regional yearly indices from variation remaining after variation associated with the regional trend

has been removed.

Literature Cited

Dagum, c., and E. B. Dagum. 1988.Trend. Pages 321-324 in S. Kotz and N. L.Johnson, eds. Encyclopedia ofstatistical sciences,

Vol. 9. John Wiley & Son, New York.

Geissler, P. H., and B. R. Noon. 1981. Estimates of avian population trends from the North American Breeding Bird Survey.

Pages 42-51 in C. J. Ralph and J. M. Scott, eds. Estimating numbers of terrestrial birds. Stud. Avian BioI. 6.
54 BIOLOGICAL REPORT 90(1)

Topics in Route-regression Analysis

by

Paul H. Geissler and John R. Sauer

u.s. Fish and Wildlife Service

Patuxent Wildlife Research Center

Laurel, Maryland 20708

ABSTRACT.-The route-regression method has been used in recent years to analyze

data from roadside surveys. With this method, a population tr~nd is estimated for each

route in a region, then regional trends are estimated as a weighted mean ofthe individual

route trends. This method can accurately incorporate data that is unbalanced by changes

in years surveyed and observer differences. We suggest that route-regression

methodology is most efficient in the estimation oflong-term (>5 year) trends, and tends

to provide conservative results for low-density species.

Introduction

The U.S. Fish and Wildlife Serviee (FWS) and the

Canadian Wildlife Service coordinate several large-scale

roadside surveys that are designed to monitor population

changes in breeding bird species. Two of these surveys,

the Woodcock Singing-ground Survey and the Mourning

Dove Call-count Survey, are the primary sources of

information regarding the population status of these

hunted populations, and are analyzed yearly as part ofthe

process of setting hunting regulations for these species.

The third survey, the North American Breeding Bird

Survey (BBS; Droege 1990), provides population

information for more than 400 avian species in the United

States and Canada.

All of these surveys were established in their present

forms during the I%O's. Since these surveys were

established, there has been ongoing research regarding

proper methods of analysis for the rather specialized

experimental designs of these surveys. Recent research

centers on finding a reasonable statistic for population

change over an interval of interest. In this paper, we

briefly outline the constraints on the analysis of these

roadside surveys, the current method of analyzing the

surveys, and some of the issues associated with the

present analytical method.

Constraints on the Analysis of

Roadside Surveys

Several of the papers in these proceedings discuss

the biases associated with roadside survey data (e.g.,

Droege 1990). Roadside surveys all share a design in

which the sampling unit is a route with several stops.

After the initial random selection, the same routes are

run each year. Unbalanced data results from

relocating routes, observer changes, and missing

counts. Each year, some routes are not run, and routes

are added and deleted from the surveys in all years.

Unless the analysis compensates for the lack of

balance, the counts can give the spurious appearance

of increasing or decreasing regional means (Geissler

and Noon 1981). By changing observers, variability is

introduced in the counts because individuals differ in

their hearing, knowIcdge of bird identification, and

ability to follow survey procedures. When a new

observer surveys a route, stop locations may change,

so not only does a different individual conduct the

route, but the actual area surveyed often changes

slightly. These observer differences can cause great

changes in count data from year to year for both

American woodcock (Scolopax mi/lor) and mourning

doves (Zenaida maerollra; D. D. Dolton, personal

communication; J. B. Bortner, personal

communication).

Route-regression Analyses

To circumvent the difficulties discussed previously,

the FWS supported the development of methods of

determining population trends from roadside survey

data. In recent years, the route-regression method has

been used to estimate the rate of population change

over an interval of interest. While this goal may not

always be appropriate, due to population cycles or

abrupt poplI1ation changes (e.g., James et al. 1990),

careful application of the methodology is useful for
AVIAN POPULATION TRENDS 55

For BBS data, we use physiographic regions within

States as our strata. The marginal mean (also called a

least squares mean) is the mean count in the midyear

adjusted for observer differences. The product

(Csr b:) is the adjusted count in the following year

according to the model (1). If the weight Wsr = lInsr ,

routes have equal weight and (2) reduces to

area of stratum s,

number of routes in stratum s,

marginal mean count for the route (Searle

et al. 1980),

estimated route trend (bo indexed by stratum

and route), and

weight discussed below.

most management applications. The route-regression

methodology was developed elsewhere (Geissler and

Noon 1981; Geissler 1984; Geissler and Link 1988); we

will summarize the method and discuss some of the

issues associated with its application.

The route-regression methodology is based on the

notion that "trend" can be estimated as the ratio:

(population in the midyear + 1 of the interval)/

(population in the midyear). If there are no missing

counts and if observer differences are negligible, the

ratio can be estimated directly from the regional total

counts. If there are missing counts, route changes, or

if observer differences are important, regional totals

cannot be used because each route will contribute

inconsistent data to the total. Instead, regional trends

can be estimated as weighted averages of the route

trends. A linear model is used to estimate the trend for

each route, adjusting for the differing abilities of

observers.

The route-regression methodology involves the

following steps.

1. Fit a multiplicative model to each route:

(1)

where

As

Its

Csr

Wsr =

LAs Cs.(y + 1)

b = -'s'-;;;;;;-----;::~_

LAs Cs.y s

(3)

where

C" count in year y,

bo slope term,

bi observer coefficient for observer i (i = 1, ... , It),

O~l' 1 if observer i ran route in year y, = 0 if not.

In practice, the model is fit using a linear regression on

the logarithm of (counts + 0.5), for example:

The estimate of In(bo) is back-transformed (Bradu

and Mundlac 1970) to find an estimate of bo on the

route. The back-transformation is approximately

exp{ln(bo) - 0.5 variance [In(bo)]} and compensates

for the skewness of lognormally distributed counts.

2. Estimate the regional trend as a weighted mean of

the route trends

(2)

where Cs . y is the mean adjusted count for the stratum

in year y, and y represents the midyear. If the weight

used is Wsr =variance[ln(Csry + 0.5)] / variance[ln(bsr)],

each route is weighted inversely by the relative variance

ofthe route trend estimate. This weight depends only on

known values (the years the route was run and the

identity of the observer) and not on the estimated

variance. Each stratum is effectively weighted by the

number of routes and number of years they were run as

well as by the stratum area. If the weights are

standardized so that 2: Wsr=1 ,strata are weightcd only r

by area and not by the amount of survey effort.

If the interval of interest is 2 years, then equation

(2) extrapolates beyond the last data point, which

overemphasizes population increases. For 2 years we

use the ratios of counts including only the routes that

were run by the same observer:

(4)

This equation differs from (2) in using actual counts

instead of adjusted counts in year y (the first year ofthe

interval) and y + 1.
56 BIOLOGICAL REPORT 90(1)

3. Estimate variances by bootstrapping the regional

slopes (5). This is accomplished by randomly selecting

a bootstrap sample of lis routes with replacement from

the lis routes in each stratum, and estimating Dfor that

bootstrap sample. We repeat this procedure 400 times

and estimate the trend and its standard error by the

mean and standard deviation of the bootstrap sample

estimates. The median can also be used to estimate the

center of a skewed distribution. Recent work suggests

that there is little difference between the mean and the

median, and consequently the more commonly used

mean may be preferable (Geissler and Link 1988).

Either a t or z value can be used to estimate confidence

intervals. If t value is used, the number of degrees of

freedom is the number of routes minus the number of

strata groups. If z values are used, we do not assess

statistical significance for States or Provinces with < 10

routes, or for larger regions with < 25 routes.

Advantages of Route-regression

Methods

Incorporates Unbalanced Nature ofRoutes

Route-regression methodology can use routes that

were not run for some of the years of interest. Routes

that are incomplete are weighted less heavily in the

regional averages, reflecting the lower value of these

data. In the singing-ground and call-count surveys,

routes on which no birds were counted for 2 (or 3 for the

call-count survey) years are considered to be "constant

zero routes" and are not run for the next 5 years. They

are then run, and if no birds are counted they are placed

back into constant zero status. Although this procedure

saves effort, it effectively creates a "not run" category

that has in the past been considered as zero counts.

Some controversy has existed regarding the proper

analysis of constant zero routes, and it has been

suggested that including them could produce a spurious

decreasing trend (S. L. Stokes, unpublished memo). The

route-regression method places these routes in their

proper context as low-density routes that have little

influence on regional population trends.

Proper Variance Estimation

The route-regression method obtains estimates for

the variances of regional estimates of trend from the

route trends. Earlier methods first estimated yearly

indices of abundance for the regions, then modeled the

trends from these indices. Variances for the population

trends were then a function of the variation among the

yearly indices, and the between-route variation was not

incorporated. Because we are estimating population

trends, the consistency oftrends among the routes is the

appropriate source of variation to consider in the

analysis, and the route-regression method correctly

incorporates this.

Use ofCovariables

The route-regression method provides a framework in

which sources of variation at the route level can be

identified and incorporated into the analysis. We

currentlyassume that observers perceive birds at different

levels, but that all observersrecord the same changes over

time. Therefore, a covariable can be included for

observers that scales the line at different levels for each

observer. In addition, if disturbance (e.g., number of

automobiles heard during the survey period) is measured

on a route, it can aL<;o be included in the analysis as a

covariable, with some additional assumptions.

Topics in Route-regression Analysis

Critiques ofthe Methodology

Since the route-regression method was developed,

there have been several reviews of the methodology,

most noticeably that ofD. W. Hayne ofNorth Carolina

State University. In response to the reviews, the

method has evolved to its current form, which is

presented here. One reoccurring criticism of the

methodology concerns back-transforming the slope

estimate to provide an unbiased estimate of D in

equation (1). The present Bradu and Mundlac (1970)

method is efficient if the estimate is unbiased, but may

not be the appropriate estimator if the slope estimates

are biased. D. S. Robson (personal communication)

suggested a direct, iterative method of solving

equation (1) that avoids both the back-transformation

and the necessity of adding a constant to the count

(which allows it to be log transformed). Unfortunately,

this method does not converge in a variety of common

cases, including the case in which no birds were

recorded on the first year a route was surveyed.

Model-based annual indices are not available for that

model, although residual indices could be used.

BiasesAssociated with Small N Year.5

A variety of simulations have been conducted using

route-regression methodology. One topic that has been

addressed is biases in the method. By simulating routes

with various underlying distributions of counts and

numbers of years run, one can assess how the

route-regression method performs under a variety of

reasonable situations that occur in nature. These

simulations are described by Geissler and Link (lQAA)'
however, we provide some guidelines for the use of the

method based on the simulation results.

1. Short-term ( < 5 year) trends may provide biased

results. In general, the trend method was designed to

provide estimates of long-term population trends.

2. The magnitude of positive or negative population

trends tends to be underestimated for birds that are

observed at low densities on survey routes.

3. Addition ofobserver eovariables effectively reduces

the number of years a route was run, as trends are

estimated within observers. Hence, observer changes on

a route effectively decrease the accuracy of the

estimates derived from the route.

Acknowledgments

We thank C. M. Bunck, S. Droege, R. M. Erwin, and

J. D. Nichols for comments on the manuscript.

Literature Cited

I3radu, D., and Y. Mundlac. 1970. Estimation in lognormal

lincar models. J. Am. Stat. Assoc. 65: 198-211.

AVIAN POPULATION TRENDS 57

Droege, S. 1990. The North American Breeding Bird Survey.

Pages 1--4 in J. R. Sauer and S. Droege, eds. Survey designs

and statistical methods for the estimation of avian

population trends. U.S. Fish WildI. Serv., BioI. Rep. 90(1).

166 pp.

Geissler, P. H. 1984. Estimation of animal population trends

and annual indices from a survey of call-counts or other

indications. Proc. Am. Stat. Assoc., Sect. Surv. Res.

Methods 1984:472--477.

Geissler, P. H., and W. A. Link. 1988. Bias of animal

population trend estimates. Pages 755-759 in Proceedings

of the 20th Symposium on Interface Computer Science

Statistics.

Geissler, P. H., and B. R. Noon. 1981. Estimates of avian

population trends from the North American Breeding Bird

Survey. Pages 42-51 in C. J. Ralph and J. M. Scott, eds.

Estimating numbers ofterrestrial birds. Stud. Avian. Bioi 6.

James, F. c., C. E. McCulloch, and L. E. Wolfe. 1990.

Methodological issues in the estimation of trends in bird

popUlations with an example: the pine warbler. Pages

84-97 in J. R. Sauer and S. Droege, eds. Survey designs and

statistical methods for the estimation of avian population

trends. U.S. Fish WildI. Serv., BioI. Rep. 90(1). 166 pp.

Searle, S. R., F. M. Speed, and G. A. Milliken. 1980.

Population marginal means in the linear model: an

alternative to least squares analysis. Am. Stat. 34:216-221.
58 BIOLOGICAL REPORT 90(1)

Estimation of Annual Indices from Roadside Surveys

by

John R. Sauer and Paul H. Geissler

u. S. Fish and Wildlife Service

Patllxent Wildlife Research Center

Lallrel, Maryland 20708

ABSTRACT.-Most of the surveys presently used to estimate population trends on a

large geographic scale depend upon repeated visits to a number of randomly selected

routes or monitoring points. As these surveys cannot be analyzed by modeling annual

mean densities among routes within a region, no natural annual index of population

density exists for the region. We discuss two possible methodologiesfor estimating annual

indices ofabundance. In the context ofthe route-regression methodology, in which trends

are estimated for each route and regional population trends are estimated as weighted

averages of route trends, it is possible to find average residual distances between the

predicted trends on each route and the actual data points. Adding these average residuals

to the regional predicted values provides a measure of averagc distance from the actual

data points to the predicted trends. A linear model approach can also be used to estimate

annual indices, in which a regional slope parameter can be fit to the data in combination

with annual effects. Bootstrapping can be used to provide some measure ofthe variability

of these annual effects. These methods provide similar results in an example using

Breeding Bird Survey data for scissor-tailed flycatcher (Tyrannlls forjicatlls) trends in

Arkansas and Oklahoma.

Introduction

Because of the extensive effort necessary to

accurately estimate the size of even a local population

of birds, almost all large-scale avian surveys (such as

the North American Breeding Bird Survey [BBS;

Robbins et al. 1986]) use a sampling scheme that

involves revisiting a permanent monitoring site (route)

at yearly intervals and surveying birds with some

consistent method. This approach provides precise

estimates of population change because comparisons

can be made within sites, removing the site-to-site

differences from the variance. The reduction in

variance thus obtained is analogous to reductions in

variances that occur when a paired t-test is used as an

alternative to a simple t-test. The annual procedures

used to derive counts from each route vary in accuracy

from territory mapping procedures designed to census

numbers of breeding males (Breeding Bird Census;

Johnson 1990) to a series of 3-min stops along a

predetermined route (BBS; Droege 1990). In all of the

surveys, however, the counts are not a census of an area

but only provide an index to the actual population

abundance (see Bart and Schoultz 1984 for a

discussion of the ramifications of using an index to

abundance). Often, observer differences and variation

in coverage (some routes are not run each year) bias

the annual average counts in a region. Because ofthese

constraints, important biases can arise from

population trends that are modeled directly from

annual average counts from roadside surveys (Geissler

and Noon 1981; Robbins et al. 1986). Modern trend

estimation methodologies have attempted to

circumvent biases associated with observer

differences and inequities of coverage (Geissler 1984;

Geissler and Sauer 1990).

The base-year method was formerly used by the

U.S. Fish and Wildlife Service to analyze roadside

survey data. In this method, changes from year to year

are estimated for comparable routes, and annual

indices of abundance are found by multiplying the

density for a base-year by the year-to-year changes.

Trends are then found using linear regression on the

annual indices. The trends are modeled directly from

the indices; hence, the trend line is an optimal fit to the

index values and the values provide a measure of the
fit of the line. This procedure can produce acceptable

indices of abundance under some circumstances;

however, if used over a series of many years it can

produce spurious results caused by propagation of

errors (Geissler and Noon 1981).

The route-regression method, which was developed

to circumvent some of the statistical problems of the

base-year method, does not provide the natural indices

of abundance that are provided by the base-year

method. Instead, it provides an estimate of population

change over any interval of interest (Geissler and Sauer

1990). Trends are modeled using linear regression

analysis for each route, incorporating observers as

covariables to account for differences in ability to

perceive birds. Route trends are in turn weighted to

form regional trend estimates. This weighting creates

difficulties in developing annual indices, as no natural

index of abundance exists for this weighting scheme.

To place this weighted estimate of population change

into a proper perspective and begin to examine possible

causes for population changes, it is necessary to examine

annual indices of abundance (e.g., James et al. 1990).

We present two alternative ways ofincorporating annual

information into the general framework of the

route-regression method. Both methods are discussed,

comparisons are made between the methods, and

examples of each are presented using BBS data for

scissor-tailed flycatchers (Tyralllllls forficatlls).

Residual Method

Description

The residual method was developed in an attempt to

provide estimates of population trends and annual

indexes of abundance that are complementary. In the

base-year method, population trends were modeled

secondarily from the annual indices produced in the

primary analysis. In the route-regression method the

primary product is an estimate of population trends, and

it seems reasonable to model the annual indices in the

context ofthe population trend. The method must allow

for variation in route and bird densities and in observer

abilities. Because statistical significance is not assessed

from these annual indices, the indices are primarily

useful as a portrayal ofyear-to-year variation around the

predicted trend line. Although the annual indices allow

visual examination of patterns, all prediction of change

points, intervals ofincrease or decline, and so forth must

be viewed in the context ofexploratory data analysis, and

verification of observed patterns must be done using the

route-regression method with appropriate adjustment

of significance levels.

Because the route-regression method is based on

linear regression methods, a natural annual index of

AVI~"I POPULATION TRENDS 59

abundance can be based on the. residual variation

between the predicted values from the regression and

the actual annual count data. The route-regression

methodology produces a regional slope estimate (5) by

first estimating individual route trend for any route (b,)

from the regression of annual counts (CI") on year (y)

and observer covariables (Oiy,):

C - bY bOIl" b 0"1'7 y,- Or lr'" lIr Eyr'

Taking logarithms,

1n(C:vr + 0.5) = In(bor)Y + (b1r)oIvr+ ... + In(b"r)

0llyr+ In(Eyr)'

where

r route index,

Cyr count in year y,

bor slope term,

bir observer coefficient for observer i,

Oivr 1 if observer i ran the route in year y, 0 if not,

and

Eyr = random error.

Ln(bor) is back-transformed (Bradu and Mundlak 1970)

to provide an estimate ofb, (the route trend) on a route.

Regional trends (b) are estimated as a weighted

mean of route slopes (Geissler and Sauer 1990). For

BBS data, we use State physiographic stratum units as

our primary regions of analysis. All population trends,

no matter what the geographic scale, are estimated from

combined ratio estimates of stratum trends.

To find the annual indices of abundance for a region,

re-run the regressions for each route in the region, fixing

the predicted regional trend as the slope parameter, and

storing the resulting residuals for each year. Calculate

the mean residual for each year. Find the predicted

regional trend line (on a log scale, see below). Finally,

add the mean residual to the predicted annual count

(from the trend line) and exponentiate the sum to

produce the annual indices. In the notation used

previously:

1.:.. First calculate a weighted regional estimate oftrend

(b) and an average density of birds on all routes over all

years (c).

2. For every route r in the region of interest:

a. "Fix" the regional slope, and calculate the

observer effects using:

-

C yr = 1n(C:l'r + 0.5) -In(b)*y = In(b1)01 + ... +

In(bll)oll + In(Eyr)'
60 BIOLO<;ICAL REPORT 90(1)

b. For each year in which the route was run,

calculate a residual:

A

, C' C' e yr = yr- yr'

where C'-'T is the predicted value from the

regression in (0).

3. Calculate the mean residuals for each year (e\,) over

all the routes in the region of interest.

4. Find the predicted trend line by setting the

predicted count in.!!Ie midyear equal to the regional

grand mean count (C), then multiply (or divide) by the

estimated regional trend to calculate indices for later (or

earlier) years:

- T.v=C, Tv+1=Tv*b,

and

- TI'_1 = Tv/b.

5. Calculate the annual indices by adding the mean

residual to the logarithm of the predicted counts and

exponentiating the sum:

Note that no weighting scheme is used in finding the

average residual, and no back-transformation other

than exponentiation is used in the derivation of the

indices. Further refinements of the methodology could

incorporate some back-transformation such as that of

Bradu and Mundlak (1970).

Advantages

Because the method was developed to provide

estimates of annual indices in the context of the

estimated population· trends, residual indices have

several advantages over alternative methods. The

analysis complements the trend analysis, and one does

not present separate analyses for trends and indices,

which can be inconsistent. They are conceptually

simple: you set up the predicted trend and use the

average residual variation to compute the annual data

points. If a route is not run in a year, no residual is

calculated; hence, only data for the year of interest are

incorporated into the estimates, and sample sizes of

routes run in a year are readily observable. Since the

analysis is done on the logarithmic scale and not

back-transformed until the annual indexes are

estimated, differences in scale among routes are not as

great a problem as they are with analyses using actual

densities (although they are still a component of the

analysis and can potentially be a difficulty).

Dimdvantages

Residual indices have the underlying assumption that

the trend estimate from the route-regression

methodology provides a valid estimate of change in the

population. The indices present a visual display of

whether, on average, the residuals ofroutes run in a year

are above or below the trend line given by the regional

trend estimate, and of the relative distance of the

residuals from the predicted trend line. Consequently,

if one has little confidence in the validity of the trend

estimate, it is unlikely that the annual indices derived

from the data will be of use. There are several cases in

which predicted trends (and therefore residual indices)

should be viewed with caution. We present two such

pathological cases.

1. Large (but nonsignificant) estimates of regional

trend. Point estimates ofrapid increases or declines that

contain a large amount of variability indicate little

consistency in population trends among routes, and may

indicate that many routes were not run· consistently

throughout the interval ofinterest. These large changes

tend to distort population estimates at extreme points in

the interval of interest.

2. Large variation in densities among routes. If large

variation in route densities occurs (as is often the case

with seabird colony surveys; see Engstrom et al. 1990),

or large differences in counts occur within routes

between years, the inclusion of a route can influence the

magnitude of a residual value. These large differences

in scale are difficult to incorporate into any analysis, and

should be avoided by either poststratifying the data set

and estimating trends separately for large colonies and

small colonies, or by treating the large colonies

individually. Unfortunately, there are some data sets

which are too unbalanced (in years in which routes were

run or in differences in density among routes) for any

trend-index method to provide useful results.

The residual method was developed with the intent of

providing annual indices that are primarily descriptive.

No statistical significance is assessed with regard to the

annual indices, but they provide a measure of how far (on

average) the data values were in a particular year from

that predicted by the regional trend.

Linear Model·Method

Description

Another approach to the estimation of annual

indexes of abundance involves fitting a linear model to

all the data for a region, and then using the estimated
model to calculate predicted counts for each route and

year, adjusting for missing counts and observer

differences. The model usually includes terms for each

observer and route to reflect differing observer abilities

and different habitat conditions along the routes. Thus

population changes are measured by comparing the

counts made by the same observer on the same route.

Year effects are included to model stratum-wide

population effects such as the effect of severe weather

on recruitment, and route trends are included to model

local habitat effects. Covariables, such as the effect of

noise or other disturbance, can also be included to make

the model more realistic. The usual model is:

C =bYdOrlv dOrn'fa) famE ry r 1···· n' 1 ... In ry

Taking logarithms,

AVIAN POPULATION TRENDS 61

Advantages

Linear model indices use an analysis of covariance to

fit observer and year effects to the survey data, and they

account for missing counts, differing observer abilities,

local habitat trends, large-scale population fluctuations,

and other factors. The model is used to estimate

predicted counts for each route and year whether the

route was run or not. This yields a complete set of counts

that have been adjusted for the differing observer

abilities. The mean annual counts reflect relative

densities of birds in the stratum. Weighting the relative

densities of the strata by the strata areas produces

population indices.

Thus the linear model method uses as realistic a

model as possible to produce counts that might have

been obtained under the ideal situation with complete

data and with the same observer running the route every

year. Once these predicted values for the ideal situation

are estimated, the analysis is straightforward, as the

mean counts are natural measures of relative density.

Disadvantages

The predicted count Cry on route r in year y can be

estimated as

(Searle et al. 1980) and then backtransformed (Bradu

and Mundlak 1970). It represents the predicted count

for the average observer on route r in yeary. The means

of the predicted counts weighted by strata areas As

provide annual indices of abundance:

where

br

di

Oril'

fy

01'

fry

slope coefficient for router,

observer coefficient for observer i,

1 if observer i ran route r in year y, 0 if not,

annual effects coefficient for year y,

1 if year y, = 0 if not, and

random error.

The linear model indices may not fit a trend line

estimated using route-regression methods as closely as

the residual indices, as the regional trend parameter in

the linear model is estimated differently from the

route-regression trend estimate. However, conducting

this separate analysis may be useful, as it can provide a

slightly different view of the trends in bird populations.

If the two methods provide greatly different results, it

would indicate that a more detailed analysis is

necessary. Unfortunately, the disadvantages associated

with the residual indices also apply to linear model

indices, as a rapidly changing or variable population will

always be difficult to model.

The improvements associated with the linear model

come at considerably increased computational cost. We

have difficulty fitting the linear model, even within

moderately large State physiographic stratum areas, as

large amounts of memory are necessary for inverting the

(XX) matrices. In addition, the linear model has to fit

many parameters. If observers change frequently or if

there are many missing counts, the parameters may not

be estimable, and it may be necessary to simplify the

model, possibly by not adjusting for observer differences.

Examples s

where WSTI' are weighting factors reflecting the amount

of information (inverse of the relative variance)

available for estimating (-STY' normalized to sum to 1.0.

We present two examples of the alternative index

methodologies from the scissor-tailed flycatcher data

set presented elsewhere in this volume. Arkansas and

Oklahoma were selected for analysis (Figure) for

1%7-87. Over the interval, the route-regression method
62 BIOLOGICAL REPORT 90(1)

F Sci s s 0 r - t 0 i ! e d Fly cot c h e I~ 01< Scissor-toiled Flycatcher

o

x

QJen

v

c

o

en

o

o

n

x

QJ o VN

C

o

\

\ I

\ --

I,

I,

, I \

f "J \ I I ,

o L------'-_-'-_--'-_L---'-_---'---_..L.-----"_-'-_---'-----' 0 L---'-_-'-_--'-_L---'-_---'---_-'------"_--'-_---'-----I

0 66 68 70 72 74 76 78 80 82 84 86 88 66 68 70 72 74 76 78 80 82 84 86 88

Year Year

Figure. Predicted population trends and annual indices for scissor-tailed flycatchers (Tyrannus forficatllS) for Arkansas and

Oklahoma from BBS data. Predicted trends from the route-regression method, residual indices, and linear model indices are

presented for 1967-87.

indicated significant declines in both States. Both

annual index methods showed similar overall patterns

for the interval, with population peaks reached in 1972,

followed by declines until about 1978, after which the

population generally increased in both States. The

year-to-year changes indicated by the methods did not

always match, with the most significant difference being

a slight decline indicated by the residual method

between 1984 and 1985, but a large increase suggested

by the linear model index between the years.

Discussion

The estimation of annual indices has proven to be

controversial. In current methods, no natural indices

exist for trend analyses with N > 2 years, as observers

change and routes are not consistently run. We have

presented two methods, both of which have certain

features that make them plausible indices. Because both

types of indices seem to be similar, the extra

computation cost of computing linear model indices

may not be necessary.

Literature Cited

Bart, J., and J. D. Schoultz. 1984. Reliability of singing bird

surveys: changes in observer efficiency with avian density.

Auk 101:307-318.

Bradu, D., and Y. Mundlak. 1970. Estimation in lognormal

linear models. J. Am. Stat. Assoc. 65: 198-211.

Droege, S. 1990. The North American Breeding Bird Survey.

Pages 1-4 in J. R. Sauer and S. Droege, eds. Survey

designs and statistical methods for the estimation of avian

population trends. U.S. Fish Wildl. Serv., BioI. Rep. 90(1).

166 pp.

Engstrom, R. T., G. S. Butcher, and J. D. Lowe. 1990.

Population trends in the least tern (Sterna antillanlln) from

Maine to Virginia: 1975-1986. Pages 130-138 in J. R.

Sauer and S. Droege, eds. Survey designs and statistical

methods for the estimation of avian populatin trends. U.S.

Fish WildI. Serv., BioI. Rep. 90(1).166 pp.

Geissler, P. H. 1984. Estimation of animal population trends

and annual indices from a survey of call-counts or other

indications. Proc. Am. Stat. Assoc., Sect. Surv. Res.

Methods 1984:472-477.

Geissler, P. H., and B. R. Noon. 1981. Estimates of avian

population trends from the North American Breeding

Bird Survey. Pages 42-51 in C. J. Ralph and J. M. Scott,

eds. Estimating numbers of terrestrial birds. Stud.

Avian. Bioi 6.

Geissler, P. H., and J. R. Sauer. 1990. Topics in routeregression analysis. Pages 54-57 in J. R. Sauer and S.

Droege, eds. Survey designs and statistical methods for the

estimation of avian population trends. U.S. Fish WildI.

Serv., BioI. Rep. 90(1).166 pp.

James, F. c., C. E. McCulloch, and L. E. Wolfe. 1990.

Methodological issues in the estimation of trends in bird

populations with an example: pine warbler. Pages 84-97 in

J. R. Sauer and S. Droege, eds. Survey designs and

statistical methods for the estimation of avian population

trends. U.S. Fish Wildl. Serv., BioI. Rep. 90(1).166 pp.

Johnson, D. W. 1990. Descriptions of surveys: breeding bird

censuses. Pages 33-36 in J. R. Sauer and S. Droege, eds.

Survey designs and statistical methods for the estimation of

avian popUlation trends. U.S. Fish WildI. Serv., BioI.

Rep. 90(1). 166 pp.

Robbins, C. S., D. Bystrak, and P. H. Geissler. 1986. The

breeding bird survey: its first fifteen years, 1965-1979. U.S.

Fish Wild!. Serv., Resour. Pub!. 157. 196 pp.

Searle, S. R., F. M. Speed, and G. A. Milliken. 1980.

Population marginal means in the linear model: an

alternative to least squares analysis. Am. Stat. 34:216-221.
AVIAN POPULATION TRENDS 63

Using Rerandomizing Tests in Route-regression Analysis of

Avian Population Trends

by

Brian T. Collins

Canadian Wildlife Service

Ottawa, Ontario

Canada K1A OH3

ABSTRACT.-This paper discusses several aspects of the route-regression method of

analysis of avian population trends. First, route-regression methods require that

observed counts, y, be transformed by In()' + c). A simulation study on the effect of

different values of c on the estimate of population trend is described. Choosing c =0.23

provides a more robust estimate of trend than c = 0.5 when the population count per

route is small. Second, weighting factors for each route can be chosen in several ways.

Some of the different choices selected by different authors are described. Third, the

standard method of assessing the significance of the estimated trend from a

route-regression analysis of an avian population survey is through a jackknife or

bootstrap test to assessthe consistency ofthe route trend estimates over the study region.

An alternative method of assessing significance is through a rerandomization test which

compares the trend to the magnitude of the within route variability. The situations in

which these two methods are appropriate are contrasted. Fourth, the results of the

route-regression method can be presented as slopes in the log scale but this is difficult to

interpret. Alternative methods of presentation, such as half-lives or yearly decline rates,

are shown. The results are illustrated with examples from the Southern Ontario

Waterfowl Survey.

Notation

The analysis presented here is based on the method

introduced by Geissler and Noon (1981), and the notation

is similar but simplified to eliminate the physiographic

strata within a Province or State. Let Yij denote the jth

observation on route i, and Xij denote the year this

observation was taken. The Y are transformed to

Zij = InU'ij + c), where c is a constant to be discussed

later. The reasons for selecting this transformation are

described in Geissler and Noon (1981) and Collins and

Wendt (1989).

A simple regression of the Zij against the Xij is

performed, providing an estimate (5i) of the trend over

time for route i. The individual route trends are

a!!Eregated to provide an overall estimate using a

weighted average

n n

b =LJ1ILJI' "w.b".! "w·

i=l i=l

where w denotes the weight given to route i and n

denotes the number of routes.

The weighting factor w is a product of three terms: an

area weight lli, a measure of the precision of the trend

estimate !2i, and a mea<;ure of the average population

index for the route!3i. These terms are described in more

detail below.
64 BIOLOGICAl, REPORT 90(1)

Selection of the Constant in the

Transfonnation

A simulation study was conducted to assess the

influence of the parameter c in the transformation

logO' + c) and to determine if a value of c existed which

yields estimates with a controlled maximum level ofbias.

It was assumed that the number of birds recorded on

a route was described by a negative binomial

distribution. Under this model the probability of

observingy individuals is

k(k + 1) ... (k + v-I) 1'( )-I'-k , . P 1 + P .

y.

where k and P are parameters of the distribution. The

mean and variance of this distribution are

E(v) = kP = II, and

V(v) = kp(l + P) =II + //i.:.

Thus for fIxed II, as k increases the variance declines.

As k goes to infInity the distribution approaches a

Poisson distribution.

In the simulation it was assumed that the mean

observed count in plot i and year j was

lIij = exr-(A + Bj).

Under this model a log transformation of the mean

would show a linear trend with time, an underlying

assumption in the route-regression technique. Inclusion

of the parameter c in the transformation, and the

inherent asymmetry of the negative binomial

distribution, biases the regression estimate ofB.

The effect of the added constant c is greatest when

the population level is low. Simulations were done for a

variety of populations levels given by initial mean counts

per plot of one, two, three, four, and fIve individuals.

The variance of the population is controlled by the

parameter k. To study the effects ofthe choice of c under

different populations, the simulations were done with k

equal to 2, 5, and 10. The variances of the initial

population level for these choices of k are shown in

Table 1. It can be seen that the variances vary widely

with k, especially for populations with large mean

counts. The varianee for a Poisson distribution

(k = 00) is also shown in Table 1. For initial mean

population levels of 1, 2, or 3, increasing k beyond 10

would not appreciably reduce the variance of the

distributions.

The simulation was done for trends which had

half-lives of 5, 10, and 20 years. These rates correspond

to annual changes of 12.9%, 6.7%, and 3.4%,

respectively. These changes are rapid but are within the

range of values seen in actual data presented later.

The effect of the additive constant was examined for

c equal to 0.1,0.2,0.3,...,1.0. These values were chosen

to include both the value 0.5 recommended by Geissler

and Noon (1981), and to have an upper limit of 1.0,

which was anticipated to cause a substantial bias in the

estimate of trend for populations with low counts.

The simulation was run for two cases: 10 routes

visited for 10 years and 50 routes visited for 10 years. The

simulation used the same set of random numbers for

each set of parameters within eaeh case but different

random numbers for the two cases. This was done to

reduce the influence of random variability in the

comparisons among parameters. The simulations were

run using a FORTRAN program on an IBM-AT. Each

simulation was run 1,000 times.

The results of the simulation are displayed in Fig. 1

for the 50-plot case, with the negative binomial

parameter k set to 2, the half-life equal to 50, and the

initial population densities of 1,2,3,4, and 5. The results

for the other simulations were similar and are not

displayed. The numbers at the end of each curve denote

the population size. It can be seen that the parameter c

influences the estimate of tfend substantially, especially

for small population sizes. As would be expected, the

slope of the lines declines as the population size

increases. The horizontal dashed lines indicate the

actual trend plus and minus 10%.

Table 1. Variallce ofillitial populatioll levels used ill the simulatiolls.

Initial

mean count Negative binomial parameter k

per plot 2 5 10 oc

1.0 1.5 1.2 1.1 1.0

2.0 4.0 2.8 2.4 2.0

3.0 7.5 4.8 3.9 3.0

4.0 12.0 7.2 5.6 4.0

5.0 17.5 10.0 7.5 5.0
AVIAN POPULATION TRENDS 65

Fig. I. Mean estimates of trend based on 1,000 simulations of

10 years of data from 50 routes with half-life of 20 years.

Negative binomial parameter k = 2, for initial population

densities of 1, 2, 3, 4, and 5.

TRANSFORMATION FACTOR C

equal to 0.5 could be conjectured to not introduce

substantial bias in the estimated trend.

The simulations have been done only for declining

trends. However, due to the symmetry ofthe solution to

the regression equation, the results can be seen to also

apply to an increasing trend if the years were numbered

in reverse order.

It is obvious that introducing too large a value for c

would tend to minimize the estimated trend over time

and produce estimates which are biased toward zero;

however, using too small a value for c can bias the trend

estimate away from zero (Fig. 1). Consider what

happens when there is a declining trend. It is

substantially more likely that positive counts at the

beginning of the time-period will be followed by one or

more zero counts near the end of the time-period than

that the reverse will occur. The estimate ofslope for such

a data set will be negative. As the value for c is reduced

the transformed zeros will move farther away from the

transformed positive values and the slope estimate will

become more negative. Eventually, as c is reduced, the

preponderance of outcomes of this type will introduce

a substantial bias in estimated slope.

,

0.9 J0.7

,

0.5

,

0.3

-.01 l

-.02 2

3

4

5

-.03

-.04 ,

0.1

o

z

w

0:::

IZ

L5

L

l.L

o

W

L

Ief)

W

The values of c, where each curve of the type shown

in Fig. 1 equaled the true value plus or minus 10%, were

calculated using linear interpolation. Thus for each

curve a range of values of c which introduces a bias of

less than 10% was calculated. These ranges are shown

in Fig. 2. The vertical dashed line on the graph is at

c = 0.23, which was the value selected by Collins and

Wendt (1989) to control the bias.

No simulations have been done for populations

greater than five individuals per route because the

influence of c decreases with increasing population size.

For populations of five birds per route, setting c to 0.5

would introduce a bias of less than 10% for many

situations. As the population size increased the

influence of c would decline even further, and setting c

Weighting ofIndividual Routes

The weighting factor Wi used for each route IS

composed of three terms as described earlier. The

inclusion of the three terms in the weighting of the

individual route regressions is common in many surveys,

but the exact form of the terms can vary.

The area weighth is a reflection of the area of the

survey which the route represents. In the analysis of the

Canadian segment of the Breeding Bird Survey (BBS)

by Collins and Wendt (1988) the areas were calculated

on a degree block basis, because the degree block was

the primary sampling unit used to define the survey. The

area ofland and the number ofroutes run in each degree

block were calculated, and each route in the degree

HALF-LIFE 5 HALF-LIFE 10 HALF-LIFE = 20

Fig. 2. Range ofc for which the mean estimate

of trend was within 10% of the true value

based on 1,000 simulations of 10 years of

data from 50 routes with half-lives of 5, 10,

and 20 years. Negative binomial

parameter k = 2, 5, and 10, for initial

population densities of 1, 2, 3, 4, and 5.

r-:

-,-

-l=i

1 -:-..

j ii' , . , 0.1 0.3 0.5 0.7

,

, : -+--

-Ir,.,

0.1 0.3 0.5 0.7

TRANSFORMATION

FACTOR C

r-:i--+

-kT-

:-:-

::,

1! : 2 i-- 1

3-'

~I~ ~-:-

11 ' 2-:

3 -+-

~I-: 4t --+-1--

1! : 2'-+

3. -:-

I I

0.1 0.3 0.5 0.7
66 BIOLOGICAL REPORT 90(1)

block was assigned a weight equal to the area divided by

the number of routes. This analysis covered the period

1966-83. Because the population trends might not be

described by a linear trend over the entire period of the

survey, the analysis was repeated for five selected

subperiods: (1) the most recent 5 years, (2) the period

before the most recent 5 years, (3) the most recent 10

years, (4) the period before the most recent 10 years,

and (5) the entire survey frame. The number of routes

run varies among the subperiods analyzed, and hence

the area weight had to be recalculated for each

subperiod. In some instances there were no available

routes within a degree block, and the block was not

included in the analysis. This means that comparison of

trends among subperiods can be confounded with

differences in area of coverage. Other analyses have

resolved this problem by pooling contiguous degree

blocks to ensure complete coverage of the survey region

(Erskine 1978), but the selection of the blocks to be

amalgamated was subjective. This problcm of

intermittent coverage of degree blocks by the BBS

causes particular difficulty in defining the northern limit

to the range covered.

Robbins et al. (1986), in their analysis of the BBS,

used physiographic regions within a State or Province to

define sampling units, rather than degree blocks. The

physiographic regions tend to be substantially larger

than degree blocks, particularly within Canada, and

hence may always have a suitable route within each

subperiod. This would then avoid the difficulty in

comparing subperiods with different areas of coverage.

This procedure, however, ignores the distribution ofthe

routes within the physiographic regions. The routes are

often more clustered close to major urban areas and less

concentrated in the remote areas. Giving all routes

within a physiographic region the same weight could

produce an estimate that is weighted towards the areas

in which routes are run.

The precision of the individual route estimates of

trend (b) varies among routes because the number of

observations on each route varies. Under the usual

assumptions made in regression analysis, the variance of

the estimate of trend for an individual route is given by

?

OJ

V (0) = -1/-1----:---

L ~rij-Xi) 2

j=!

where 111 is the number of observations on route i, 0; is

the variance of an individual observation, and Xi is the

average vear of observation for route i.

Th~ ~arianee term 0; is unknown and may vary

among routes, but it could be estimated from the

difference between the observed and predicted counts.

However, the number of observations on a route is too

small to provide rc1iable estimates of variance. Hence 0; is assumed constant across routes and can be

dropped from the slope precision term in the weighting

factor. The slope precision term is set equal to the

denominator in the above variance term. This is a

different weighting factor than that proposed by

Geissler and Noon (1981) who proposed the term

tn,.

hi= [(m-l)L(xij- Xi)2JO.S

j=!

This was the weighting factor used by Robbins et al.

(1986), but in another report, Geissler (1984) stated

that the weighting should be proportional to the

reciprocal of the variance, as is described in the

previous paragraph.

The third term in the route weight is a measure of

the population level for the route. The recommended

estimator of this term also has been changing over

time. The original form (Geissler and Noon 1981) of

this term was

This is the geometric mean count for the observations

on route i, except for the additive constant c, which is

introduced to avoid multiplying by o. This procedure

ignoresthe number of years and the distribution of years

in which the observations were taken. Thus for a route

in which the population is changing, the weight given to

the route varies depending on the years in which the

observations were taken. For example, Fig. 3a shows two

routes in which the population is decreasing. Route 1

was observed in years 1 and 2, while route 2 was

observed in years 4 and 5. Under the scheme above,

route 1 would receive a higher population weight than

route 2. In Collins and Wendt (1989) the route

population weights were based on the predicted

population level in the midyear of the period under

study. The predicted transformed count in the midyear,

ZiOl would be
AVI~'II POPULATION TRENDS 67

I

3 i I

4 5

Route

observations are taken on a route. Some BBS routes

were run for a few years and then dropped orwere only

initiated near the end of the time frame under study.

The predicted values for the dependent variable

extrapolated beyond the range of the observed

independent variable have a large variance. The

conversion ofthese values by using them as exponents

can result in assigning unreasonably high weights to

some routes. This problem can be reduced somewhat

by Windsorizing the weighting factor. In Collins and

Wendt (1989) the route population weights were

Windsorized at 100, and in the Southern Ontario

Waterfowl survey they were Windsorized at 50.

Geissler (1984) also has refined the route

population term through a technique developed by

Bradu and Mundlak (1970). This procedure is used

to minimize the bias in a regression on a

logarithmic scale.

Testing for Significance

Fig. 3. Hypothetical trend~ illustrating effect of various

population weighting schemes: (A) effect of routes being

observed in different years, and (B) effect of different route

trends.

i

3

YEAR

i

4 t

5

Route 2

The usual method of testing significance in the

route-regression method is through a jackknife or

bootstrap estimate of variance. This method

assesses the significance of the overall trend

through the consistency of the individual route

estimates of trend. Whenever the trend is similar in

direction and magnitude for the set of routes, the

B)

0)

A)

C)

IZ

o

u

o

UJ

:::;:

cr

e

U1

z

g

- r--,-----r.--r------, • Y~R Y~R

Fig. 4. Hypothetical trends illustrating how bootstrap and

rerandomi7.3tion tests would as.~ess significances differently.

The midyear was selected because it did not bias the

results towards an increasing or decreasing trend.

Consequences of misapplicaton of weights are best

illustrated by extreme cases. For example, Fig. 3b shows

two hypothetical routes with perfect linear trends and

the same average population level, but with trends in

opposite directions. If the predicted count in year 1 was

used, then route 1 would be given a higher weight and

the overall trend would be downward, while if the final

year were used, route 2 would be given a higher weight

and the overall trend would be downward. In this

example, selecting the initial year (giving higher weight

to a route which exhibits a decline) biases the combined

estimate of trend towards predicting declining

populations and vice versa.

One problem with using predicted population levels at

the midyear of the study to assign weights is that it can

involve extrapolating beyond the range of years that

which can be converted to a predicted count as
68 BIOLOGICAL REPORT 90(1)

jackknife procedure will estimate the variance to be

small and will lead to a conclusion of a significant

trend. An alternative method to assess significance is

through a rerandomization test. In this procedure the

observations are randomly reordered separately

within each route, and the test statistic is recalculated.

The proportion of times in which the randomized

trend statistic exceeds the observed one is an estimate

of the probability the observed trend could have

resulted when no underlying trend is present.

Figure 4 illustrates some hypothetical data for

which the two procedures would provide different

answers. Figure 4a indicates an ideal data set in which

there is a consistent trend within a route over time and

across routes. Figure 4b shows a situation in which the

trend is consistent across routes, but there is a

substantial amount of within-route variability. In

Fig. 4c, the trend is consistent over time within a route,

but varies among routes, and in Fig. 4d the trend varies

among routes, and there is substantial within-route

variability. The regression lines for individual routes

are identical for Figs. 4a and 4b and, similarly, the

individual regressions are identical for Figs. 4c and 4d.

Both procedures would find that the data in Fig. 4a

indicate an appreciable trend, and that the data in

Fig. 4d indicate a much less consistent trend. The

jackknife procedure would assign equal significance to

the trends in Figs. 4a and 4b despite the larger

variability of the observations in Fig. 4b about the line.

This is because it only evaluates the consistency of the

trend. The rerandomization test would conclude that

the trend shown in Fig. 4b was less significant than the

one in Fig. 4a because it evaluates the variability of the

data about the trend line. The jackknife procedure

would find the trend in Fig. 4c less significant than the

trend in Fig. 4a; however, the rerandomization

procedure would find both to be ofsimilar significance

despite the among-plot variability.

The examples in Figs. 4b and 4d exhibit a substantial

year effect, since the deviations about the trend line

are similar for different routes in a given year. This

would occur in situations where overall weather

effects, such as severe winters or late springs,

dominated the bird counts. In this situation both the

jackknife and the route rerandomization would have a

type I error rate above the nominal rate for the test. A

correct rerandomization test in such a situation would

involve randomly reordering yearly observations in

parallel for all routes. Such a test would probably have

a substantially reduced power to detect trends, and

careful consideration would be required on how to

handle missing observations.

Presentation of Results

The output of the route-regression analysis is an

estimate of trend in the log scale. These values are not

easy to grasp, nor is it easy to understand their

biological significance. An alternative method of

presentation would be to calculate the time required

for the population to change by a given amount, such

as a half-life or doubling-life, or to calculate the

amount of change which would take place in a fixed

period of time. This calculation is illustrated in

Table 2, which shows results from the Southern

Ontario Waterfowl Breeding Survey. For example, the

estimated slope is 0.0327 for mallard (Alias

platyrhyllcllOs), which corresponds to a doubling-life

of21 years, a 58% increase over the 14-year period of

the survey, a 3.3% increase per year, and a 39%

increase in 10 years.

The estimated half-life and doubling-life should

only be viewed as a means of interpreting the slope.

This parameter is unstable when estimating trends

which are close to zero. For example, a slope of 0.01

has a doubling-life of 31.6 years, while a slope of -0.01

Table 2. Estimated trends in sOllthem Gntario waterfowl, 1971-85.

Estimate of Doubling Percentage change

slope in or half- Over pcricx.l Per Over 10

Species log scale life ofsurvey year years

Canada goose 0.1543 d 5 + 767 16.7 376

Wood duck 0.0440 d 15 + 85 4.5 55

Mallard 0.0327 d 21 + 58 3.3 39

Common merganser -0.0021 hgna -3 -0.2 -3

Ring-necked duck -0.0030 hgn -4 -0.3 -3

Blue-winged teal -0.0085 h 81 -11 -0.8 --8

American black duck -0,(1672 h 10 -{il -{i.5 -49

Green-winged teal -0.0725 h 10 -{i4 -7.0 -52

Common gOldeneye -0.1253 h 5 --83 -11.7 -71

a hgn = half-life greater than 99.
AVIAl" POPlJLATIOl" TREl"OS 69

has a half-life of 31.6 years. The presence or absence

of a single bird might be enough to change the slope

between these two values, which would be considered

unremarkable if the slope were analyzed, but would

produce a substantial change in the half-life

calculation, which might be interpreted as biologically

significant by an unsophisticated reader. Biologists

also may find reported half-lives or doubling-lives that

are longer than the period of the study to be difficult

to interpret.

The percentage change over the period of the study

is a useful description of the magnitude of the trend,

but cannot be compared between studies which cover

different periods. This can be avoided by calculating

the percentage change for a fixed period. The

percentage change per year is a more interpretable

variable, but the objective of the analysis is to estimate

long-term trends. Hence reporting the percentage

change over 10 years may be more appropriate.

In Table 2 the species are sorted by the estimated

slope, which enables the reader to assess the change

for each species with respect to the others and to

evaluate the number of declines compared with the

number ofincreases. This is possible because only nine

waterfowl species are analyzed, and locating a species

of interest remains easy. For the BBS this might not be

practical because of the large number of species and

the greater taxonomic diversity.

Another possible presentation of the data is to plot

individual routes on a map, with increases and decreases

indicated by different symbols. Estimates of trend for

each route were averaged for 20-min blocks and are

shown in Figs. 5 and 6 for American black ducks (Anus

rnbripes) and mallard. It can be seen that the areas where

black ducks were located is mostly restricted to eastern

Ontario, while mallards were located over a widespread

area. For the black duck the decline in numbers

1985

1971

+

5.0 < X

2.0 < X~ 5.0

1.0 < X~ 2.0

0.5 < X~ 1.0 + - X~ 0.5 . - never observed

5.0 < X

2.0 < X~ 5.0

1.0 < X~ 2.0

0.5 < X~ 1.0 + - X~ 0.5

. - never observed

. .

. .. ..

.+

++.

...

. Ill· . + . +

·1Il+++0

++++'++O+IIl++

'O+IIl++O+

. + III -l.-_,,'-:;;~~

A- 0.04 < b ,,- 0.01 < b:> 0.04 o - -0.01 < b;l; 0.01 v - -0.04 < bi> -0.01

• - b:> -0.04 · - not observed

..- 0.04 < b ,,- 0.01 < b;l; 0.04 o - -0.01 < b:> 0.01 v - -0.04 < b;l; -0.01

• - blii -0.04 · - not observed

.. .. . .

. 0

.. v

. ... • A

Fig. 5. Estimated trends in number of American black duckS

(Anas mbripes) recorded in the Southern Ontario

Waterfowl Breeding Survey, 1971-85.

Fig. 6. Estimated trends in number of mallard (Ana~

platyrhynchos) recorded in the Southern Ontario Waterfowl

Breeding Survey, 1971-85.

Fig. 7. Predicted numbers of American black ducks (Anas

mbripes) on plots in the Southern Ontario Waterfowl

Breeding Survey in 1971 and 1985.
70 BIOLOGICAL REPORT 90(1)

1985

< X

2.0 < X;:a; 5.0

1.0 < X;:a; 2.0

0.5 < X;:a; 1.0

+ - X;:a; 0.5

. - never observed

1971

< X

2.0 < X;:a; 5.0

1.0 < X;:a; 2.0

0.5 < X;:a; 1.0

+ - X;:a; 0.5

. - never observed

. . + .

.. + • .

........

Fig. 8. Predicted numbers of mallard (Anas Plat)'rlr)'nclros) on plots in the Southern Ontario Waterfowl Breeding Survey in 1971

and 1985.

throughout its range is evident despite the sporadic

estimates of increases, but the change for the mallard is

more difficult to assess because the areas ofincrease and

decrease are not segregated. This may be due to the

among-route variability ofthe estimate oftrend and could

be remedied through averaging over larger areas or

through potential mapping.

Figures 5 and 6 do not indicate the magnitude of the

population at each plotting position, which may cause

difficulty in interpretation. Figures 7 and 8 show the

predicted eount per plot at the initial and final years

of the survey for the same two species. The reduction

in predicted black duck numbers throughout the range

can be seen by comparing the two maps in Fig. 7, and

hence the occasional increases in black duck numbers,

which were seen in Fig. 5 no longer appear as

discrepancies to this trend.

The maps shown in Fig. 8 show the general increase

in mallard numbers across the study area and suggest

that there may be a movement ofthe mallard away from

the northern shore of Lake Ontario. Thus mapping of

the data can reveal aspects of changes in the population

which are not revealed by trend analysis.

Literature Cited

Bradu, D., and Y. Mundlak. 1970. Estimation in lognormal

linear models. J. Am. Stat. Assoc. 65:198-211.

Collins, B. T., and J. S. Wendt. 1989. The Breeding Bird Survey

in canada 1966-83: Analysis of trends in breeding birds

populations. Can. WildL Serv., Tech. Rep. Ser. 75.

Erskine, A. J. 1978. The first ten years of the cooperative

Breeding Bird Survey in Canada. Can. WildL Serv., Rep. Ser.

42.

Geissler, P.B. 1984. Estimation of animal population trends

and annual indices from a survey of call counts or other

indications. Proc. Am. Statist. Assoc., Sect. Surv. Res.

Methods 1984:472-477.

Geissler, P. B., and B. R. Noon. 1981. Estimates of avian

population trends from the North American breeding bird

survey. Pages 42-51 in C. J. Ralph and J. M. Scott, eds.

Estimating numhcrs of terrestrial birds. Stud. Avian BioL 6.

Robbins, C. S., D. Bystrak, and P. II. Geissler. 1986. The

Breeding Bird Survey: Its first fifteen years, 1965-1979.

U.S. Fish WildL Serv., Resour. Pub\. 157. 196 pp.
AVIAN POPULATION TRENDS 71

Estimating (Relative) Species Abundance from

Route Counts of the Breeding Bird Survey

by

Lincoln E. Moses and Daniel Rabinowitz

Statistics Department

Stanford University

Stanford, Califomia 94305

ABSTRACT.-We propose and illustrate a method of estimating relative abundance of

a species from Breeding Bird Survey data, with missing route counts, by iteratively fitting

an additive row and column model to the logarithms of route counts. We point to the

possible value of additionally fitting the first characteristic vectors in a singular value

decomposition of the matrix of residuals from the row and column additive fit. Finally,

we offer some reflections on whether to smooth (across time) the annual relative

abundance results.

Introduction

For 21 successive years a set ofroutes, originally chosen

as a probabilitysample ofthe UnitedStates, have furnic;hed

route counts of many bird species (Robbins et al. 1986).

These counts have been obtained by a well-standardized

observing process. Because of the probability-sample

design it is possible to combine the route counts with

known weights belonging to the design, and thus to

construct a national abundance estimate for each species.

Estimates for physiographic Provinces, or for other

regions, such as States, can be similarly devised using the

weights that belong to the routes in such a region.

Statistical problems arise in constructing the estimates.

First, not every route is run every year. This complicates

the estimation. Second, it may seem appropriate to smooth

the annual abundance ligures. How shall this be decided?

How might it best be done?

This report treats these questions, and in the course of

so doing considers also the possible desirability of

estimating not the abundance of species (a number, like

13,500 or 1,060,000) but rather the relative abundance of

the species, like 60% or 110% of usual.

One way of treating the missing data problem is to

impute values for missing data, and then proceed with the

analysis appropriate to complete data.

Imputation ofMissing Values, and

Estimation ofAbundance

We begin by introducing notation, to help the

reader (and the writers). We will always talk in terms

of only one species, so it needs no representation in

our argument, or in the expressions we shall use. So,

for some one species, let the route count from route

number i, in year t, be denoted:

ric (i = 1, ...,R; t = 1, ...,T).

We have altogether R routes (this is a small number

if the species is the Kirtland's warbler [Dendroica

kirtlandii] and a large one if it is the great blue heron

[Ardea herodias)), and we have T years of annual

Breeding Bird Surveys (BBS).

The estimated species abundance in year t we write as

(1)

where the weights are given by the sample design,

the ric by the year's route counts. But some route

counts are missing. Our approach will be to impute
72 BIOLOGICAl, REPORT 90(1)

2500 ,-------------------------,

(estimate) those missing route counts, and then get

the estimate in (1), where some of the, have been

. d 1 Impute.

Imputation might be done by fitting a row and

column model to the R x T data array. Such a model

might be

(2)

2000

1500

v 1000

(a) data

•

•. • • • 500 ." • • . . . ., . . ..iMl¥,,·· • 0 ••• I

0 20 40 60 80

10

• 8 (b) square root

6 •

v • 4

• •

d '* '* "* '* **. '* ****'1:**

~,J;;..:.U··~:.;.· . • '* :r * '*** -** 0 ··

5

9

1.6

8

1.5

4

7

.

·

.·

1.4

•

I

·· .

•

5 6

1.3

2

log (Y + 1/6)

1.2

(c)

1.1

2

1

(d) ('f + 1/6)

.; '* * '* ** '* ..... '* ****., 1**

'* *.

"

";' 1M'. '* /f*: ,;**

. ."'1'. .

1

5 I

!,

4

I 3 I

v

,I

1

o l

0

0.06

0.05

0.04

v QD3

0.02 •

0.01 e

0.0

1.0

This model closely resembles one already in the

literature (Mountford 1982).

We fitted the model by a technique known as

"mean polish" (Mosteller and Tukey 1977:181-183),

which proceeds iteratively. At the start zeroes are

entered for missing values. A complete cycle first fits

row averages, then computes the residuals, to which

column averages are then fitted, and the new residuals

are ready for beginning the next cycle. We obtained

eonvergence in about six cycles. The method makes

much smaller demands on computer memory than do

conventional least-squares estimates (and associated

imputed values for the missing data). It dates from the

early days of the modern eomputer (Healy and

Westmacott 1956). The method is evidently also

applicable where the rows call for different weights

(row averages can be calculated using those weights);

In this expression p denotes a power like 1 (the

original data) or 1/2 (the square root) or 0 (by a

limiting argument, the logarithm).

Figure 1 shows plots of variance against mean for

the data when the transformations

1 V2 V\O • 'it, 'it, ('it + V6) ,and In('it + V6) were apphed to

scissor-tailed flycatcher (Ty,aJlJllls foificallls) data.

Each point corresponds to one route, with the route's

mean plotted horizontally and the variance of its 21

years' values plotted on the ordinate.

The constant 1/6 was added to each route count to

avoid complications with rOllte counts of zero. We

believe that had we used 1/2 or 1 rather than 1/6 we

would have arrived at nearly indistinguishable results;

the choice of 1/6 is based on a recommendation in the

recent literature (Mosteller and Tukey 1977).

We liked the weakness of the mean-variance

association seen for the logarithmic transformation,

and we liked it for other reasons which will appear.

So we favored the model

m

lThere are instances when this imputation approach coincides exactly

with a method of "fitting the only data present." For example. in the

usual analysis of variance situation the "missing value formulas"

have just this character.

Fig. 1. Mean vs. variance for various transformations of the

route counts: (n) raw counts; (b) square rcx)t of counts; (c)

natural log of counts (increased by 1/6); (d) route counts

increased by 1/6 and then raised 10 the power 0.1. (One

point for each route.)
(lJ

U

C

o

u

C

:::J

r>

<::

(lJ

>

Natural-I

log ~ercent

0.4 ~lll g

i

i

I

i-135

I

I'

I

0.2 2

i

1

110

I

0.0 LIDO

I

I

r91

i

-0.2l 82

i

1

74

-0.4 Lp I

1 2 3 4

AVIA." POPllLATIO:" TRENDS 73

\\

\.

~. /~~~I

• I

I i , I

i

I

\--0 I

i

I

I

I

I

1* J 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Year

Fig. 2. Relative ahundanee of scissor-tailed flycatcher (Tyrannlls forficams; routes) with annual average exceeding 2.0, as

estimated in two ways: Stars denote values filled without reference to covariances; line shows estimates using sample

covariances.

the method is closely related to the EM algorithm

(Dempster et al. 1977).

The fitted value of In(ric + V6) for any missing route

could then be back-transformed to an imputed value

(somewhat biased), permittingA(t) in expression (1) to

be computed.

Now, analysis of variance routines are designed for

situations where the error terms have homogeneous

variance and zero correlations. Though we have

helped with the former desideratum, by transforming

to the logarithm, we would check the correlation

among the residuals. What we found for the

scissor-tailed flycatcher data were clearly significant

correlations on routes between counts for nearby

years. So we fitted the logarithmically transformed

observations again, this time estimating the 21 by 21

covariance matrix between the residuals (from the

row and column additive fit) for times t and t', and

then used that estimated covariance matrix in fitting

the parameters ai and {3t. This fitting was

accomplished by applying a form of the EM algorithm

(Dempster et al. 1977). The results were almost

unchanged! Figure 2 shows the values oft; plotted for

years 1 thr~ugh 21 computed in the two ways; the stars

show the {3's from the EM algorithm; the vertices of

the broken line show the ~s from the direct analysis

of variance program. Notice how close the two time

series are; observe also that simpler estimates look

like a slightly smoothed version of the EM result. We

judge the two series to be unimportantly different,

and are content to use the simpler estimation

procedure, at \cast for the scissor-tailed flycatcher

data.

Some further light is cast on this i~ue by Fig. 3, which

presents again the simpler-fitted {3/s; this time each

appears as a star.lhe broken lines show the highest and

lowest values of {3c found in applying the simpler fitting

procedure to 500 bootstrap samples from the 236
74 BIOLOGICAL REPORT 90(1)

Natural t logperce:1t

O. 8 -";"'22-;~~--------------------------------------,

201

(J)

u

c

o

u

C

:=1

.D

<:I:

(J)

> i

•

I

5 10 11 12 13 14 15 16 17 18 19 20 21

-0. 8 I -'---_-'----_'_---l-_---L_-'--- ~_____'_ ____'__ _'___~_~_'_~_ ___'___~_~

1 2

YeCir

Fig. 3. Bootstrap variation of estimated relative abundance. Stars depict estimates using routes with average annual count

exceeding 2.0: Upper line connects maxima of 500 bootstrap samples; lowerline connects the minima. (Estimates did not use

covariance structure.)

routes.2 These oU~.F lines give an idea of the variability

of the estimates f3t

• In the light of that variability, we

continue to regard as unimportant the differences from

the two methods of estimation, one of which acts as if

the inter-year correlations were zero, while the other

takes them into account.

It is known that ordinary least-squares estimates of

a correct additive model suffer inefficiency, but not

bias, in the presence of correlations. So, with

abundant data as here, we need not be surprised to

see close agreement betwecn two unbiascd estimators

ihe bootstrap analysis was performed by drawing with replacement.

a sample of 165 complete route records for the 165 routes retained

as having annual averages exceeding 2.0. and then analyzing the data

of the bootstrap sample by the same program as applied to the main

analysis.

off3t. With fewer data, diffcrences might be large, and

we might prefer the EM algorithm.

Before going further we explain an important

feature of the fitting procedure that we employed. Of

the 236 routes with any positive counts for the

scissor-tailed Oycatcher, there were 71 that had very

spotty data, in the sense that in the 21-year period the

total counts observed did not exceed 42 (Table).

These routes were omitted in the caleulations that

underlie Figs. 1,2, and 3, and their accompanying text.

The possible effect of omitting this part ofthe data set

is probed in Fig. 4, which compares the estimates just

referred to, with estimates in which all those' omitted

routes were included, but in the following way. For

each of the nine States, the small routes were

combined into one fictitious super-route; this

increased R from Hi5 to 174. The eye is struck by the

smallness of the differences between estimates that

completely omit the 71 small routes and the estimates
AVIA''; POPULATION TRENDS 75

Table. Distribution ofscissor-tailedflycatcher (fyrannus forlicatus) routes by State alld route abundance. Routes with 42

orfewer species sighting~ in the 21-year period are counted as small; those with 43 or more are large. Observe that all

counts on allroutes appearin the tabulation ofthe right-most column, where one super-route aggregates all counts of

the smallroutes.

Number of routes

State

Arkansas

Iowa

Kansas

Kentucky

Louisiana

Missouri

New Mexico

Oklahoma

Texas

Total

Observed Used in analysis

Large Small Primary Including

43 birds or more 42 birds or less analyses super-route

6 16 6 7

0 1 0 1

14 23 14 15

0 1 0 1

2 9 2 3

3 11 3 4

1 2 1 2

35 1 35 36

104 7 104 105

165 71 165 174

N"'"eel I 1og?ercent

0.4 I 9

I

1

135

I'

O. 2 1\0r-l\~2

1

Q) I U ~ll a c I 0

D

I

c • :::l

D

<:r:. 0.0 1

100 • Q) I > I

I .....,

0 L91

0)

ex:

-0.2 ~82

1 2 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Year

Fig. 4. Estimated relative abundance of scissor-tailed flycatcher. Estimates based only on routes with more than 2.0 annual

average counts are denoted by *. Estimates including for each State one super-route that combines all small routes in the

State are denoted by the line.
76 BIOLOGICAL REPORT 90(1)

physiographic Province), then a new consideration

appears. The method in which we Iirst estimate the

absolute abundance allows us to calculate for each

Province,p (p = 1,..., P), its abundance in year t as

that inelude them as fictitious State super-routes.

Exploration with other species (and perhaps with

other thresholds than 42) would further illuminate the

suitability of this mode of dealing with routes offering

meager data.

The distribution by State ofsmall routes is displayed in

the Table. Ap(t) =I Wirir

(I')

(6)

whence, exponentiating both sides, we get

Estimation of Relative Abundance

Yil =In (ril + lie,) =(Xi + (31 + Cil (I PI =0). (3)

This leads to estimates a; and;;; and indeed to Iitted

values for the Yip which we call.\~

where the routes being summed over (with their

weights) are the routes in Province p; the subseript (P)

on the summation sign denotes that. Now these Province

abundances add up toA (t), since each route is in one of

the Provinces and A(t) uses them all. That is the

consistency relation that this method possesses, and it

enjoys this property exactly for complete data (i.e., no

missing 'il)' But, when some route counts must be

imputed, some discrepancy-perhaps not important

unless there is much incompleteness- arises between I Ap(t) and A(t), depending on whether the

p

imputation is done separately for each Province or for

all the data at one time.

When we go direcJ}y to relative abundancc

estimation through the f31' then separate Iitting to the

Provinces is natural, and the estimates, f3~(P)' that result

need not be equal, and the value of B(t) for the whole

country is some kind of weighted average of the ~(J'),

not easily described.

Thus we have two ways to estimate relative

abundance for component Provinces: (1) from the

whole-country model's estimates deduce absolute

abundance and correct to relative abundance Provinee

by Province; or (2) directly estimate relative abundance

Province by Province. They are not equivalent, and it is

not at all obvious what their relative merits are. The issue

can be seen as two ways of handling any interactions of

year with the routes belonging to different Provinces. To

Iix ideas, suppose there are two Provinces, U and V, and

that relative abundance in U was high in early years,

while in V it was high in later years. Except for the

missing data Iitting, the absolute abundance approach

in effect obtains the overall estimates by using a certain

weighted combination (resulting from the Wi of the

sample design) of the two Provinces' estimates fP) and

fF). If the weights are in some way inappropriate then

the Provinces' abundances are consistently, but

inappropriately, combined. On the other hand, the

direct relative abundance approach would estimate the

two Provinces' relative abundance parametersffl) and

fF) separately in the two Provinces, which would be

desirable, but how to combine the estimates into a

national figure does not have an obviously best solution.

(Many are possible. Perhaps they could be weighted in

(4)

(5)

In the previous section we showed how to estimate

A(t), the (absolute) species abundance in year t, by

replacing the missing route counts with values from a

logarithmic row and column model. If, as may be the

case, total abundance is most interesting merely as a

device to assess relative changes in abundance, then

plotting the logarithm ofA (t) against t will render equal

percentage changes as visually equal; thus 64, 80, 100,

and 125, each 25% greater than its predecessor, would

be equally spaced vertically in such a graph.

Ifrelative abundance is itself the focus ofinterest, we

need not do so much calculation, for the estimates i3;

already developed in the two-way fitting can serve

directly. Recall expression (3), which we repeat:

So, except for the addition of 1/6 to every route count,

we lind the Iitted values of each route count (whether

originally missing or not) represented in equation (5) as

the product of a quantityAi (the ith route's estimated

overall abundance) and another quantity , the year's

abundance factor. The original normalization Dl = 0

implied that 2)1; =0, and therefore that the product of

all the e~ must be eO = 1.0. So the product of ~'s is 1.0;

some are larger (in abundant years) and some are

smaller. The product (and their geometric mean) is 1.0.

Thus we have a second, and more direct, solution to

the estimation of relative abundance. Figures 2 through

4 display the Iitted ;J; (on the natural log scale) and the

Iitted (on the percent scale).

If we wished to estimate relative abundance for a

particular portion of the whole country (e.g., a
proportion to" wi, and" wi; that is, the sums of .LtCl) .Lt(2)

route weights in Provinces (1) and (2).) Notice that the

second approach better re\1eals the different annual

patterns of Provinces U and V.

AVI~'1 POPI'LATION TRENDS 77

indebted to J. Tukey for the ideas that follow. We

learned them in a conversation with him whilc birding.

Return to the estimation modc1 at (3),

Consider now augmenting it to the following more

complex form

Before going further we stop to understand just how we

would interpret this modc1 if (1) it were true and (2) we

knew the values of the new parameters Yl'Y2"",YR and

61,62,.•. ,6T. The y's are rather straightforward to

interpret. First, their squares average to 1.0, and so we

will expect to regard 3 or -3 as a definitely large value

and values between -1 and + 1 as definitely not large.

Second, two routes with the same value of Yi tend to

depart from the typical yearly pattern specified by (31 to

the same degree, along the ("most relevant")

time-distortion pattern, which is given by the set 6J,

02,..., OT. Third, two routes with equal and opposite Yi

depart equally, but oppositc1y, from the (3T's, and again

with reference to the time-distortion pallern given by

the set 6J,..., 0T. Of course a route i with Yi = °

is one

whose departure from the (3T's, though not necessarily

zero, is uncorrc1ated with the modal time-distortion

pattern. The set of6's can be thought of as a pattern over

the years. Perhaps it is high in the middle years and low

in the early and later years, or maybe it is periodic, and

so forth. In any case, our filling (to be described later)

will choose the pattern of6'sthat obeys (7) and that most

reduces the residual sum of squares left after the row

and column additive fit of the a's and (3's. So we may

regard the set of 6's as the most relevant way in which

routes tend to vary - eithcr with that way or against

it - from the overall average time pattern (31) (32,·'" (3T.

When we have the estimates 01, 02,..., OT, we might

hope to relate them to EI Nino years, shifts in the jet

stream, and so forth. But having estimates Yl'Y2"",YR

may be more immediately fruitful. We might take a map

and (using color coding) mark each route according to

its yvalue location on the net- oo , -3, -1.5, 0, + 1.5, +3,

00. Perhapsthe large positive valuesfall mainly on routes

at high elevations, or in the south; perhaps there is a

trend with seasonal rainfall, or date of last killing frost,

and so forth.

It may well be that exploration of the use of the model

(7) would justify the effort.

The computation proceeds in this way. First, fit the

additive row and column model as previously described

and compute Y, the R by T matrix of residuals. Then, to

Additional Comments and Discussion

1. We have said nothing about smoothing. The

abundance estimates already have been purged of

much randomness, bywhat amounts to averaging data

across many routes. The bootstrap data in Fig. 3 admit

the possibility that some smooth curve (indeed many

possibilities offer themselves) could be passed within

the uncertainty zone. But would such a smooth curve

be closer to reality? This question really means "In

fact, do bird populations change smoothly from year

to year, or is there a lot of abrupt change at times?" If

the latter proposition is true, then the value of

smoothing becomes unclear.

The desirability of smoothing a time series seems

to be greater when there are very many data points,

and when the data must be regarded as quite "noisy."

Suppose we observe y(t), and it combines two

components, a signal g(t) and noise (e), thus

yet) = get) + e(l).

If e(t) has large variation, we can hopc to see the signal

get) better by smoothingy(t) - in effect averaging the e(t)

in some way to stabilize and reduce thcir influence. We

many welcome this, even knowing that we have also

averaged - and distorted - g(t). But what if e(t) has

small excursion? Indeed, what ifwe knewg(t)? Might we

still wish to smooth it? Maybe. Maybe we strongly prefer

simpler paths in time, as easier to contemplate. Maybe

we think of true but unusual changes in the value ofyet)

as transient and accidental, and wish to downplay them.

The considerations are reminiscent of when to prefer a

diagram or a painting to a photograph. We ourselves

incline to the view that with only 21 time points, and

those equally spaced, the eye can pretty well supply any

needed smoothing or important trend identification. Of

course, showing both the points and the smooth can be

a good solution. But we have nothing to contribute here

about preferred ways to smooth across time.

2. The relative abundance method calls for an

annual refitting across all years and routes. This is a

drawback. Shortcuts that call for less frequent

refitting are not hard to develop, but we do not offer

our results here, as they bring a lot of technical detail

to what is really a subordinate prohlem.

3. The BBS data are quite extensive and may

afford opportunity for revealing patterns of

substantive interest, much beyond more estimation of

relative abundance (or absolute abundance). We are

)'il = ai + f31+ Y,OI + eil

('Lf31 = 0; LYi2 = R).

(7)
78 BIOLOGICAL REPORT 90(1)

the T by T matrix A = Y'Y, apply an eigenvalue

program. The largest eigenvalue Al and its associated

eigenvector 01 are needed. The desired vector (01

02,o..,OT) is in fact the eigenvector 01' As shown in the

Appendix, the R-vector Y = (Yl'Y2,o,o,YR), which may be

very long if there are many routes, can be computed

from Y, OJ, and Al with little ado, as

Literature Cited

Dempster, A. P., W. Laird, and D. Rubin. 1977. Maximum

likelihood from incomplete data via the EM algorithm. J.

R. Stat. Soc. (D) 39:1-38.

Gabriel, K R. 1978. Least squares approximation of matrices

by additive and multiplicative models. J. R. Stat. Soc. (D)

40:186-1%.

Geissler, P. H. 1984. E~timation otanimal population trends

and annual indices from a survey of call-counts or other

indications. Proc. Am. Stat. Assoc., Sec. Surv. Res. Methods

1984:472-477.

Healy, M., and M. Westmacott. 1956. Missing values in

experiments analyzed on automatic computers. App!. Stat.

5:203-206.

Mosteller, F., and J. W. Tukey. 1977. Data analysis and

regression: a second course in statistics. Addison Wesley,

Reading, Ma~. Page 95.

Mountford, M. D. 1982. Estimation of population fluctuations

with applications to the common bird census. App!. Stat.

31:135-143. Equation (1).

Robbins, C. S., D. Dystrak, and P. H. Geissler. 1986. The

breeding bird survey: its first fifteen years 1965-1979. U.S.

Fish Wild!. Serv., Resour. Pub!. 175. 196 pp.
AVI~" POPULATION TRENDS 79

Appendix

Let Ywith R rows and T columns, R > T be of rank

K. Define Y'Y = A and IT' = B. The singular value

decomposition ofA (Gabriel 1978) ensures that:

where (1) A are the K non-zero eigenvalues of A (and

also the K non-zero eigenvalues ofB) and we order them

so that A1 ..1. 2 ... AK; (2) ° is the ith eigenvector

(corresponding to Ai) ofA, of length T, and the Oi are

orthonormal, that is (0;, OJ) = O;j; and (3) b; is the ith

eigenvector of B, of length R, and the b's are also

orthonormal. The decomposition in (1') exhibits Yas

the sum (weighted by YAj) of K degenerate R x T

matrices, each the product of an R-component column

vector, post-multiplied by T-component row vector.

We find the largest eigenvalue Al and its associated

eigenvector 01 by subjecting the TxT matrix A to an

eigenvalue program. To avoid next computing and

working with the larger R x R matrix B (perhaps 200 x

200 or 1,000 x 1,(00), we postmultiply both sides of (1 ')

by 01. obtaining

From this expression we see that from 0t we can

compute b I. The T-vector°1 is the desired time pattern;

that is, 01 = (01. O2,,,,, 0T) and b1VR = (Yt'Y2"",YR) is

the desired set of row coefficients, normalized to satisfy

LYT = R.

(2 ')

R

= LYAf bAt = YAf bt ·

i=1

R

Yat = LYAf b;a; at

i=t

K (1 ')

y= "'VT:b·a'· L.. I I I

i=1
80 BIOLOGICAL REPORT 90(1)

Smoothed Scatterplot Analysis of Long-term Breeding Bird

Census Data

by

Stephan R. Taub

Biology Department

George Mason University

Fairfax, Virginia 22030

ABSTRACT-Scattcrplot smoothing using the LOWESS algorithm is proposed as a

technique for summarizing population census data accumulated over a span of years.

Scatterplot smoothing is compared with least-squares linear regression lines. A<; a

descriptive method, it is superior to regression techniques beeause it docs not excessively

obscure detail and does not necessitate a priori assumptions about underlying processes.

Unlike most procedures used to analyze time series, it docs not require strictly periodic

data sets. This procedure is used as the basis for ongoing studies oftrends in populations

of birds at several sites in the eastern and midwestern United States.

Introduction

Johnston (1990) identified 15 long-term data sets of

breeding bird censuses taken at mature deciduous forest

sites in the eastern and midwestern United States. Each

data set consists of the density of pairs of land bird<; for

each year a census was taken at a given site. Despite some

possible limitations, the sets are potentially useful for

comparative analyses ofbreedingbird population changes.

The data sets identified by Johnston display two

properties which make their description and analysis

problematic: (1) some sets are aperiodic, that is, census

years are not evenly spaced over the duration of the study,

and (2) in some sets, possible trends in the number of pairs

over the years are obscured by a substantial amount of

interyear fluctuation or "noise." Any procedures ll<;ed to

describe or analyze these data must deal with these

properties. In this paper I present a method for the

description of these unbalanced and variable data sets

which will increase the likelihood of discovering trends or

significant changes over time.

Methods

Standardizalion

To eliminate differences in scale, the reported numbers

ofbreeding pairsfor each year in each ofthe censuseswere

standardized to the number of pairs per 100 acres.

Grouping

The standardized number of pairs for each species

listed in the data sets was totaled over all species and

subtotaled into two mutually exclusive subcategories,

neotropical migrant species, and short-distance migrant

and resident species. Thus, for each data set, three series

of standardized counts, total, neotropical, and resident,

were produeed.

Smoothing

Standard time series models are not applicable to these

data sets because oftheir aperiodicity. For this reason, and

to avoid imposing an a priori model on the data, scatterplot

smoothing was used to examine them. Such smoothing

techniques can be used to visualize trends otherwise

obscured in data sets characterized by substantial,

apparently random, fluctuations (noise) from one

observation to the next.

The smoothing method selected for the breeding bird

eensus data was Cleveland's robust loeally weighted

regression algorithm (LOWESS; Clcvcland 1979), as

implemented in SYSTAT, version 3.0 (Wilkinson 1987).

This method produces curvesthat are relatively insensitive

to the values of outlying data points. It allows, by

adjustment ofthe value of a parameter,!, user-control over

the degree ofsmoothing of the data - that is, the extent to

which each smoothed point is influenced by adjacent
points. A good description of this method is given in

Chambers et al. (1983).

The procedure consists of the following steps:

1. For each point in the original data set, a line is fitted

to it along with a fixed number of adjacent points

contained in a strip on either side.

2. Neighborhood weights are assigned to all points in

the strip, the selected point having the greatest weight.

The weight function is symmetrical and decreases to

zero with distance from the selected point.

3. A line is fit to the points within the strip, using

weighted regression. The smoothed point lies on the

fitted line at the same abscissa as the selected point.

Scatte!plots

The standardized counts and smoothed values for

each year of each census were entered into a SAS

(version 5.16, SAS Institute, Cary, North Carolina) data

set and plotted using SAS/GRAPH installed on a VAX

8800 minicomputer. The illustrations included in this

report are taken directly from SAS/GRAPH output.

LinearRegression

Least-squares linear regression techniques applied to

the standardizedvaIues were used to obtain straight lines.

AVI~" POPULATIO~ TRENDS 81

Results

Standardized Data

The data set from the Rock Creek Park, D.C., site

was used, as an example, to construct the plot of counts

of total, neotropical, and resident pairs shown in Fig. 1.

This plot reveals two features characteristic of many of

the data sets: (1) aperiodicity, and (2) substantial

fluctuation from one year to the next (noise), possibly

obscuring underlying trends.

Smoothed Data

For the same site, smoothed neotropical, resident,

and total counts are plotted in Figs. 2 and 3. In addition

to illustrating scatterplot smoothing, these figures

demonstrate the effect of the value off (0.25 and 0.50)

on the degree of smoothing obtained with LOWESS.

Linear Regression

Straight lines fitted to the standardized counts for the

Rock Creek data arc shown in Fig. 4.

Discussion

Several authors recently have recognized the

importance of long-term population counts of birds

to detect population trends. A variety of methods

T '

T T T T

N

P;:::

R

T T Fig. 1. Standardized counts of neotropical

(N), resident (R), and total (1) pairs at

Rock Creek Park, D.C. 1948-86.

51] 91]
82 BIOLOGICAL REPORT 90(1)

Fig. 2. Smoothed counts (f=.25) of

neotropical (dotted line), resident

(dashed line), and total pairs (solid

line), Rock Creek Park, D.C., 1948-86.

P

A

I

R

S

200

100

40 50 60 70 80 90

-- ------.. " '. ..~

~-

230

220

210

200

190

180

170

160

150

P 140

A 130

I 120

R 110

S 100

90

80

70

60

50

40

30 L,.-~~~---,--,-~~~~,,--,~~~~,,-~~~~~~~~--T

Fig. 3. Smoothed counts (f=.50) of

neotropical (dotted line), resident

(dashed line), and total pairs (solid

line), Rock Creek Park, D.C., 1948--86.

40 50 60

YEAR

70

300

80 90

Fig. 4. Least-squaresstraight lines fitted to

the standardized neotropica! (dotted

line), resident (dashed line), and total

counts (solid line).

P

A

I

R

S

200

100

40 50 60

YEAR

70 80 90
have been used to describe and interpret the resulting

data. Wi1cove (1988), for example, described

population changes at 10 Great Smoky Mountain sites

by comparing different censuses taken 34-35 years

apart. Robbins et al. (1986) used linear regression of

transformed annual roadside counts taken over a

period of 15 years to investigate trends in bird

populations distributed throughout the United States.

Hall (1984a, 1984b), for long-term census data of

Appalachian spruce forest bird populations, simply

tabulated his counts or presented linear regression

lines of the untransformed data. Holmes et al. (1986)

also used simple linear regression to describe bird

population changes. Their study spanned 16

consecutive years at the Hubbard Brook

Experimental Forest in New Hampshire.

These reports, however, reveal some of the pitfalls

inherent in deciding whether significant population

changes (i.e., trends) have occurred over long timeperiods. The Breeding Bird Survey data (Robbins et al.

1986), while extensive, are fit to log-linear regression

models to discover trends. Although fitting data to a

model is necessary for some statistical testing

techniques, the required assumptions (e.g., linearity of

the transformed counts) are not necessarily realistic and

may obscure details such as whether any conjectured

increase or decrease in population size began during or

before the study period. Simp1c linear regression, such

as applied by Hall, also requires the assumption that the

rate of change in a population is constant over time.

Wi1cove's study does not depend on graphical

analysis and docs not attempt to fit data to an a priori

model. Instead, it compares different counts obtained

34-35 years apart. Wilcove used a nonparametric

technique (chi-squared goodness of fit tests) to

conclude that, at his sites, no significant population

changes had occurred in neotropical migrants.

Although this technique does not require that

assumptions be made about any trends underlying the

data, it is not as powerful as techniques available for the

analysis of linear models and only considers two points

in time at each site. It is, therefore, not as likely to reveal

underlying trends.

The present paper does not address the issue oftesting

hypotheses about trends. Rather, it focuses on the problem

of adequate description of the census data. By smoothing

the breeding bird census data, one can easily visualize

whether there have been population changes over time.

AVIA.'I; POPULATlO:" TRE:"DS 83

The advantage of this presentation over simple linear

regression is evident from comparison of Fig. 2 or 3 with

Fig. 4. While linear regression obscures considerable

detail, scatterplot smoothing appears to abstract

underlying changes over time without excessive loss of

detail.

The effect of the parameter f on the degree of

smoothing is evident from visual comparison of Figs. 2

and 3. The greater the value off, the greater the degree

of smoothing and loss of detail. Unlike regression,

however,scatterplot smoothing allows user-control over

the degree of loss of detail.

An obvious advantage of least squares regression is

that it permits testing of the regression coefficient, a

measure of the rate of change over time, for statistical

significance. If necessary for the purposes of statistical

hypotheses testing, appropriate models can be fit to

smoothed data sets. Should a smoothed scatterplot

suggest, for example, a biphasic pattern (e.g., no

changes during the earlier years at a site, but a

population decline later), a piecewise linear regression

model could be explored. Current efforts in this

continuing study are exploring such models where

appropriate, both for species grouped as described

earlier and for individual species.

Literature Cited

Chambers, J. M., W. S. Cleveland, I3. Kleiner, and P. Tukey.

1983. Graphical methods for data analysis. Duxbury Press,

Boston, Mass. 395 pp.

Cleveland, W. S. 1979. Robust locally weighted regression and

smoothing scatterplots. J. Am. Statist. Assoc. 74:829-836.

Hall, G. A. 1984a. PopUlation decline of neotropical migrants

in an Appalachian forest. Am. Birds 37: 14-18.

Hall, G. A. 1984b. A long-term population study in an

Appalachian spruce forest. Wilson Bull. 96:228-240.

Holmes, R. 1'., 1'. W. Sherry, and F. W. Sturges. 1986. Bird

community dynamics in a temperate deciduous forest:

long-term trends at Hubbard Brook. Eco!. Monogr.

56:201-220.

Johnston, D. W. 1990. Descriptions of surveys: breeding bird

censuses. Pages 33-36 in J. R. Sauer and S. Droege, eds.

Survey designs and statistical methods for the estimation of

avian population trends. U.S. Fish Wildl. Serv., BioI. Rep.

90(1).166 pp.

Robbins, C. S., D. Bystrak, and P. H. Geissler. 1986. The

breeding bird survey: its first fifteen years, 1965-1979. U.S.

Fish WildI. Serv., Resour. Puh!. 157. 196 pp.

Wilcove, D. S. 1988. Changes in the avifauna of the Great

Smoky Mountains: 1947-1983. Wilson Bull. 100:256-271.

Wilkinson, L. 1987. SYSTAT: the system for statistics.

SYSTAT Inc., Evanston, III.
84 BIOLOGICAl, REPORT 90(1)

Methodological Issues in the Estimation ofTrends in Bird

Populations with an Example: the Pine Warbler

by

Franccs C. James

Department ofBiological Science

Florida State University

Tallahassee, Florida 32306

Charles E. McCulloch

Biometrics Unit

Department ofPlant Breeding and Biometry

Comell University

Ithaca, New York 14853

and

Lorctta E. Wolfe

Wolfe Associates

1706 Semalachee Drive

Tallahassee, Florida 32301

ABSTRAC"I:-The North American Breeding Bird Survey (BBS) is designed to provide

estimates of the distribution of birds in North America and trends in their abundance. In

the recent IS-year summary of BBS data by the U.S. Fish and Wildlife Service (Robbins

et al. 1986), the data were analyzed by a linear, parametric method developed by Geissler

and Noon (1981) and modified by Geissler (1984). This method, which we call linear

route regression (LRR), gives the slope of an index to linear trends in overall population

size on a log scale. In this paper we compare the LRR method of analysis with a method

developed by Mountford for the analysis of censuses made by the spot-map method of

censusing in England. The Mountford method measures yearly fluctuations in

unsmoothed data, but not trends.

We also present here a new method, which we call nonlinear nonparametric route

regression (NNRR). Nonparametric route regression is similar to LRR in the way it

aggregates data from the route level to obtain estimates of trends for larger geographic

regions. The major difference is that NNRR does not force the data into straight lines

on a log scale. It uses LOWESS, a locally weighted scatterplot smoother to allow flexibility

in the degree of smoothing of the data. Because data are fit directly on a route-by-route

basis, NNRR docs not require special weighting by abundance, the use of logarithms, or

corrections for zero counts, and routes representing equal areas are weighted equally.

Using NNRR, we can directly fit a measure of abundance: mean stops per route on which

the species of interest was observed. We also explore the value of additional smoothing

of the data.

We illustrate the new method with an analysis of BBS data for the pine warbler

(Dene/mica pinus) in the central southern and southeastern region of the United States.

In this example we show that much information that would have been obscured by LRR

can be displayed with NNRR. By defining a trend as log slope in LRR, one can obtain a

single p-value for its significance, but, in this process, much other information about

trends is lost.
Introduction

The North American Breeding Bird Survey (BBS) is

the most ambitious attempt to monitor populations of

animalsin nature that has ever been conducted. Robbins

et a\. (1986) and Droege (1990) discuss some important

difficulties that arise in the analysis of BBS data. Here

we review the subjects of data quality, missing data,

aggregation of data by routes into larger areas,

weighting of data, and model choice. We compare the

route regression method of Geissler and Noon (1981)

and Geissler (1984), which was used in the IS-year

summary ofthc BBS (Robbins et a\. 1986) and which we

refer to as linear route regression (LRR), with a method

developed by Mountford (1982, 1985) for analysis of

data from the Common Birds Census in England. Then

we propose a new method, nonlinear nonparametric

route regression (NNRR). The new method is based on

first smoothing data for each route by locally weighted

regression (LOWESS; Cleveland 1979, 1981; Chambers

et a\. 1983) and then aggregating the resulting values by

physiographic strata within States. To illustrate the

method, we analyze data for the pine warbler

(Dcndroica pinlls) in the central-southern and.

southeastern regions ofthe United States for the 22-year

period 1966-87. An outline of the recommended

procedure is given in the Appendix.

Statistical Issues

Data Quality

Without high quality data, no statistical method will

give reliable analyses. Because the BBS depends on

volunteers, whose work can only be minimally

supervised, data quality is an important consideration

(Robbins et a\. 1986; Droege 1990). "If a measurement

procedure leads to biased estimates, taking a large

sample doesn't help. It just repeats the basic mistake on

a larger scale" (Freedman et a\. 1978:303). Thus, care

must be taken to improve and monitor the data quality,

even if data quantity must be sacrificed.

Missing Data

A difficult aspect of the devclopment of statistical

methods for the BBS is that many routes have not been run

every year. If there were an association between

abundance and the pattern of missing data, then ignoring

the missing data could lead to erroneous conclusions. For

example, if there were more missing data in later years of

a survey from the routes of higher abundance, then an

increasing population trend would be underestimated or

estimated to be negative when it was not. Consider the

example given in Table 1. There is no population trend at

AVIA;'II POPULATION TRENDS 85

any location. But, because there are more missing data at

the sites of higher abundance, thcre seems to be a decline

when only the averages are considered. A simple average

ofthe routes with data present is misleadingbccause it puts

more weight on the routes with smaller abundances in the

later years.

It is possible to use the data that are present to

estimate the pattern for years for which data are missing.

Both the route regression method and the Mountford

method do so by fitting a model that incorporates route

effects. In Table 1, ifthe missing data arc first estimated

within route and are then averaged, the results correctly

show no trend.

Aggregation

Premature aggregation of routes on a geographic

level will hide trends at the route level. It was premature

aggregation of data across routes that led to the

misleading decline in the averages in Table 1. The

temporal aggregation of data also affects what trend is

being estimated. Trends may be positive for one group

of years and negative for another. An appropriate

method of analysis should be sensitive to different

trends for different sets of years.

Weighting

If the analysis involves aggregation of routes, the

question of weighting needs to be addressed. The

choices of whether to weight by area represented,

abundance, and statistical precision, are difficult.

If the routes represent the areas in which they are

located, as is the case with BBS data, then weighting by

the area represented by each route is reasonable. On the

other hand, with the Colonial Bird Survey, the data do

not represent abundance in a surrounding area, and

weighting by abundance would not be reasonable.

If trends in an overal1 population are desired,

weighting by abundance should be performed. If a large

population on one route is declining and a smal1

population on another route is increasing, the overall

trend is a decline. This situation is il1ustrated in Table 2,

in which the route with the large population shows a

halving in population size each year and the route with

the smal1 population shows a doubling. Route averages

are automatical1y weighted by abundance, so they show

the overal1 decline, but the overal1 average proportional

change is unweighted, and it shows a misleading

increase.

There are situations in which abundance weighting

should not be used. For example, if the proportion of

routes that are declining is the information sought,

abundance weighting would not be appropriate.
86 BIOLOGICAL REPORT 90(1)

Table 1. An association between missing data and abundance can falsely indicate a population decline when only the

averages are considered.

Year

Route 2 3

1 32 32

2 20 20 20

3 14 14 14

4 2 2 2

Average 17 17 12

4

14

2

8

5

2

2

Weighting by statistical precision should always be

used, but the development of a proper method needs

further study. The LRR method weights by components

of the within-route estimated variance, but this is not a

good idea. Observations within a route among years

must be correlated, and the amount of dependence

within routes should be considered in the development

of a weighting scheme.

Choice ofa Model

The model that is fit to the data determines how the

trends are estimated, and it also determines the

smoothness of the estimates. If the model requires that

the trend be a smooth function, then the trend will be

smooth. Ifthere is flexibility in the model, the trend may

be irregular. The efficiency of the estimates will be a

function of the type of model used. A fully efficient

method should be sensitive to the correlation structure

of the data.

Comparison of the Route Regression

and Mountford Models

In this section, we compare the LRR method of

Geissler (1985) and the method of Mountford (1982,

1985) with respect to missing data, aggregation, and

weighting.

Both methods have suitable ways for handling

missing data. With LRR, which analyzes the data on a

log scale, a separate regression line is fit for each route,

so each line has its own slope and intercept. The

estimated slopes are then combined by means of a

weighted average. This method avoids the problem of

having no weight for years in which data are missing

(Table 1). In a somewhat similar fashion, Mountford's

method has site effects that correct for missing data.

With LRR, the results of route-by-route analyses are

aggregated to larger geographic levels. The entire set of

years is used to find a single estimate of a trend. Of

course, a subset of the years could be selected to search

for trends over shorter periods. The Mountford method

does not use either geographic or temporal aggregation.

Separate effects arc estimated for each year. In fact, the

Mountford method measures yearly fluctuations, not

trends. Geissler (1985) and Sauer and Geissler (1990)

have suggested a supplement to LRR in which yearly

fluctuations are estimated by analysis of residuals.

Linear route regression uses weights equal to the

product of the area, the abundance, and a component

of the inverse of the variance of the slope estimate (the

diagonal clement of (X'X)-t, where X represents the

model matrix for the within-route regression). As

mentioned above, we think that weighting by the inverse

of the variance of the slope estimate, or components of

it, is not optimal. There is substantial route-to-route

variation in the BBS data, so it would not be wise to allow

a route with a good within-route fit to greatly

overshadow the other routes. A better weighting scheme

would weight by a combination of within-route and

between-route variabilities. The Mountford method

Table 2. Example demonstrating that an overall decline in a population can be adequate~v illustrated by averages but that

it would be masked if ZlIlweightcd estimates ofthe trend were calculated.

Year lrend (annual

Routc 2 3 4 proportional change)

1 192 96 48 24 0.50

2 2 4 8 16 2.00

Averagc 97 50 28 20 1.25
AVIA.N POPULATION TRE:'oiDS 87

for LRR, and

and the Mountford model is given by

10g(Cyr + .5) = log(ar) +y *log(br) + error, (1)

accommodation of the correlations in the data and the

caleulation ofstandard errors.

How might these methods be improved? They both

handle missing data suitably, and the LRR addresses

aggregation well by building from a route-level analysis

to larger geographic regions. We suggest improvements

in the other two areas: weighting and model to he fit. If

the numerical data are fit directly on a route-hy-route

basis, then no ahundance weighting need be done.

Fitting the data directly also avoids the complications

involved with logarithms and the corrections needed for

zero counts. If the major component of variation is

route-to-route variation, then routes representing equal

areas should be weighted approximately equally, and

the within-route variation should essentially be ignored.

Though still not optimal, this is the weighting we have

chosen. With respect to the model to be fit, we suggest

one that is intermediate between the ones used with

LRR and with Mountford's method. Mountford's

method does not use any of the information from year

to year to try to remove extraneous variation that is not

part of a trend. On the other hand, the LRR produces

only a single estimate for the entire period considered

and hence docs not help to discover trends over shorter

periods. We suggest the usc of a method that allows the

estimates to be smoothed somewhat hut not forced to

be straight lines on the log scale.

Because each route is conducted only once each year,

some of the between-year variation is undouhtedly

attributable to factors other than long-term trends in the

bird population. Trends should be more evident ifsome

of this extraneous variation is removed by smoothing.

We use LOWESS (locally weighted scatterplot

smoother; Cleveland 1979,1981; Chamhers et al. 1983)

for two reasons. First, it allows us to judge trends without

having to select in advance the years for which the trend

is to be caleulated. For example, a trend that hegan in

the middle of the IS-year period of the Rohhins et al.

(1986) analysis would be more sensitively detected by

our method than by a linear trend analysis. Second, it is

a conditional smoother; that is, it entails no assumption

that the values on the x-axis (years) are equally spaced.

The degree of smoothing with LOWESS is optional.

Iff, the smoothing parameter, isset to 1.0, the smoothing

is complete, and the result is nearly a straight line. Iffis

set to 0.5, half of the data are scanncd to calculate each

fitted value, and very general patterns can he expressed.

In Fig. 1, a single route (No. 7007) from the BBS

scissor-tailed flycatcher (Tyralllllls forjicatus) data is fit

with LOWESS. Small values off(near zero) allow many

changes in the fitted line; large values off (ncar 1) lead

to nearly linear fitted lines. Table 3 gives hypothetical

data and shows how variances and confidence intervals

can be calculated for LOWESS lines using

route-to-route variahility. The data consist of six routes

(4)

(2)

log(C\"r) = log(ar ) + y *log(br ) (3)

From this equation, we see that the Mountford analysis

uses an additive error on the original scale, whereas the

LRR analysis uses an additive error on the log scale. If

we consider only the mean values and ignore the

constant of .5 that is added to correct for zero counts,

then the models can be written as

does not use any type of weighting, but it could be

adapted to use weighting schemes.

In many ways, the models fit by LRR and the

Mountford analysis are similar. The LRR model is a

linear regression through time on a log scale. The

Mountford model is also multiplicative. More precisely,

ifwe let ~\"r denote the count for year y and route r, then

the LRR model, ignoring observer effects, is given by

for the Mountford analysis. From these equations, we

can see that both methods fit additive models on the log

scale, with separate constants for each route. They are

different, however, in that LRR fits separate slopes for

each route but assumes a constant trend across years,

whereas the Mountford method fits separate effects for

each year but assumes that they are common to all

routes. The LRR analysis summarizes the information

by estimating the linear trend on the log scale for the

entire period considered in the analysis. It produces as

smooth a trend estimate as is possible. The Mountford

method is at the other extreme, fitting each year with a

separate effect, allowing the ultimate in flexibility. It will

tend to yield irregular estimates of a trend through time.

The LRR and Mountford analyses handle variances

and covariancesin the data in extremelydifferent manners.

The LRR method ignores correlation within a route when

fitting separate slope estimates. It uses between-route

variability to estimate standard errors of the trend

estimates. Because the trends are estimated directly,

confidence intervals on the mean ahundance cannot be

caleulated. For this reason, the diagrams in Rohhins et al.

(1986) that give confidence intervals are for the trend

estimates only. They should not he interpreted as

confidence bands for the mean ahundance line as a

function of time. The Mountford method estimates the

correlations in the yearly ratio estimates to obtain slightly

more efficient estimators. Either method issuitable for the
88 BIOLOGICAL REPORT 90(1)

Lowcss Smoothed, F = .25

in one stratum-within-State and seven routes in the

other stratum-within-State. The first stratumwithin-State is assumed to be 100 units in area, and the

other is assumed to be 50 units. Because these

calculations are simply weighted averages, there is no

need for bootstrapping.

Example of the Proposed Method:

the Pine Warbler

To explore the usefulness ofthe proposed method for

the examination of trends in a particular bird

population, we present an analysis of data for the pine

warbler, a common breeding bird of pine woods from

southern Canada to the Gulf of Mexico. We consider

only the nine-State area of the central-southern and

southeastern United States (Fig. 2, Table 4).

Choice ofan Abundance Criterion: Stops per

Route

Data Quality, Missing Data, Aggregation

The BBS data for each route in each year have been

rated by the staff of the Fish and Wildlife Service on the

basis ofweather and the reliability ofobservers. We have

used only type 1 data, the type they judged to be most

reliable. For the 22-year period 1966-87, there were

399 routes in the region for which there is some type 1

data. However, many routes were not run in all 22 years,

and the data for some routes were judged to be less

reliable for certain years. For the 399 routes on 22 years,

about 54% of the total possiblc records are available.

Our criterion for inclusion of a route with missing data

was that at least 10 years be represented and that there

be at least 1 year of data in each of four periods: the first

6 years, each of the next 5-year periods, and the last 6

years. The 201 routes that met these criteria were

deemed sufficiently complete to be included in our

analysis (Fig. 2). Note that the selection of routes was

not affected by the presence of pine warblers but only

by the reliability of the survey and the coverage of the

period of interest.

Because of insufficient data, we were not able to

include Louisiana or South Carolina in our analysis. In

each of these States, there were long series of years in

which no surveys were conducted. Also, we excluded

stratum-State units for which there were fewer than

three routes (stratum 1 in Florida, strata 3 and 13 in

Georgia, and stratum 4 in North Carolina).

We have not addressed the issue of estimation of

missing values. (In one case, estimation ofthe State line

for Georgia for 1969 and 1970, we used an average to get

a missing value.) By setting a criterion for the estimation

of missing values, one could use more of the data.

Whether or not this procedure would change the result

would have to be determined.

A

30

24 ...

'" 18

5

:'S:

..

"

12

Year

Fig. 1. Population trend for the scissor-tailed flycatcher

(Tyrannus forficanls) on one route (No. 7007) showing

successively more smoothing inA, n, and Cusing LOWESS.

There are two logical choices for a criterion of

abundance for Breeding Bird Survey data: the total

number of individuals of the species recorded on the 50

stops of one survey and the number of stops out of the
AVIA." POPULATION TRENDS 89

Table 3. Calculation ofmean and variance in LOWESS estimated values by route illto stratllm-within-State units and

aggregation ofthese values to get average State values and their variances, without smoothing above the route level.

LOWESS values by year (stops per route)

Stratum-within-State Route 2 3 4 5

Unit 1, Area =100 1 0 0 0 1 1

2 1 3 4 5 6

3 2 4 6 7 7

4 1 1 1 1 1

5 2 3 4 2 2

6 4 5 4 4 4

Stratum-within-State, 1, mean 1.67 2.67 3.17 3.33 3.50

Estimated variance of mean (s2/n ) 0.31 0.58 0.83 0.98 1.12

Unit 2, Area =50 1 1 3 4 5 1

2 2 4 4 4 3

3 5 5 4 4 3

4 6 6 7 6 6

5 1 2 0 1 2

(, 1 1 1 1 1

7 3 3 5 3 3

Stratum-within-State, 2, mean 2.71 3.43 3.57 3.43 2.71

Estimated variance of mean (s2/n ) 0.71 0.49 0.94 0.60 0.48

Means, variances, andconlidence intervals

Weighted average =Lw;Li =(IOOL] + 50L2)/150

~(~ ) Variance = LW;s; = ( 100/150) -( S i /nl) + (50/150) - s2 / n2

State weighted average

Variance of weighted average

For approximate confidence intervals, use ± 2 ¥VaT:.

2.02

0.21

0.92

2.92

0.30

1.10

3.30

0.46

1.36

2.37

0.49

1.40

3.24

0.54

2.08

total of 50 at which the species was recorded. Robbins

et al. (1986) used the former; Cox (1987) used the latter.

Of course the variable of most interest is the number of

birds present, but there are several reasons why stops

per route might be a more reliable indicator of

abundance than the actual count of individual birds.

First, it is less difficult for an observer to determine

whether or not a species is present at a particular stop

than it is to determine how many individuals of each

species arc present. Second, there is probably a greater

difference between observers in judging the number of

individual birds than there is in determination of

whether the species is present (Bart and Schoultz 1984;

S. Droege, personal communication). Also, analysis by

stops per route would be simpler than analysis by

individuals. For these reasons, a high positive

correlation between these two criteria would be

justification for using stops per route on which the

species was recorded as a measure of abundance.

We examined data for 18 routes in the Lower Coastal

Plain (stratum 3) in northern Florida. Data for all 18

routes were pooled, and average stops per route on

whieh pine warblers were recorded was plotted along

with the average number ofindividual pine warblers that

were seen or heard per route (Fig. 3). The two variables

are highly correlated. When theirstandard deviations by

year are compared, the variation around these average

values is less for stops per route. All these factors

supported our decision to use stops per route as our

abundance criterio!,1.

Model to Fit: the Choice ofa Smoother

A plot ofthe original data values and the smoothed

LOWESS line with f = 0.5 for three routes in the

Upper Coastal Plain stratum (stratum 4) in southern

Arkansas illustrates the value ofsome smoothing. The

trends are clear in the original data, but they are

emphasized in the LOWESS lines (Fig. 4a).

Smoothed lines for all nine routes in this stratum in

Arkansas allow visual comparison of substantially

more data, and a seeond smoothing of averages of all

of these values expresses the general trend for the

stratum-within-State unit (Fig. 4b). A comparison of
90 BIOLOGICAL REPORT 90(1)

~/

Fi!:. 2. The central-southern and southeastern region of the United States, showing the locations of 201 Breeding Bird Survey

routes for which there are reliable data for a reasonable span of the period 1966---87 (see text). The physiographic strata

(Robbins et al. 1(86) are shown by dOlled lines and are identified in Table 4.

Tenn. ,

I

• 4

.

• ./://

/ . /

,.,//

I

I

I

Fla.

•

• •

••

• •

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,

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• ,. • • /

• ./

• I

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I • • I

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• .',

La.

3

25

20

15

10

5

0

65 70

SlOps

75

Indiyiduals

80 85 90

Fi!:. 3. The average number of SlOpS per

50-stop route on which the pine warbler

was recorded, and its standard deviation,

plotted along with the average total

numocr of individual pine warblers

(Dendroica pinus) seen or heard and its

standard deviation. The data are mean

values for 18 routes in stratum 3 (Lower

Coastal Plain) in Florida.

Year
AVIA.'" POPULATION TRENDS 91

25-,-----------------------,

Smoothed

Stratum

Line

85 90

Fig. 4. (A) The abllndance ofpine warblers on three of the

nine rallies in stranun .; (Upper Coastal Plain) in

sOllthern Arkansas. Trends smoothed by LOWESS

(f = .5) are given by hem)' lines for eaeh route.

(B) LOWESS-smoothed lines for the nine routes in this

stratum-within-State unit and a solid line for

resmoothed values for the general trend in this unit.

(C) The same solid line as in 4B plotted with the original

unsmoothed values for the nine routes.

,

"\

\

,I \

I \

c

5

20

25-.-----------------------,

10

15

90

A

85

Smoothed

Stratum-within-State

Line

70 75 80

B

O~~~~..........--........,~~~,.....,..~~~--r--~----.-j

65

20

Q)

-=0 15 0:::

...

Q)

Q.

CIl

Q. 10 0

Cii

5

25

20

Q)

-=0 15 0:::

...

Q)

Q.

CIl

Q. 10 0

Cii

Year

this last line with the original values shows that the

generally increasing S-shaped trend is very difficult to

see in the morass of the originallincs (Fig. 4c). Note,

also, that the low values for the summer of 1978, which

followed two extremely cold winters, have bcen

obscured by the doubly smoothed

stratum-within-State line. This example demonstrates

the importance of selecting the degree of smoothing

on the basis of the generality of the particular

question of interest. If the question is to find the

effects of particular events on bird populations,

smoothing may be unwise. Exprcssion of the data at

more than one level of smoothing can be very helpful,

but even mild smoothing can shift the time of the

apparent occurrence of a change in the size of a

population.

Aggregation and Smoothing

Because it is important to minimize bias attributable

to differences among observers and to other site effects,

we analyzed records for each route separately. Then, as

in the LRR method, we averaged route values into units

that are first physiographic strata within States. We

obtained State or stratum values by averaging

stratum-within-State values and weighting by

proportional areas. A stratum that has a strong increase

in pine warblers will not greatly affect a State trend if

that stratum occupies only a small area within the State.

See for example stratum 4 in Florida (Table 4; Fig. 5d).

We constructed State and regional trend lines using

weighting proportional to area (Table 4) as suggested

by Geissler and Noon (1981), but we have not weighted

the data by precision as they suggest. Finally, we

obtained regional values by averaging State or stratum

values and again weighting by proportional area. With

this aggregative method, trends at various spatial scales

can be compared graphically. In each State there are

three to six stratum-within-State units (Table 4; Fig. 2).

Trend lines are affected by both the level of

smoothing and whether smoothing is applied at various

levels of aggregation. If data are smoothed at the route

level and then aggregated by calculation of means to get

trends for stratum-within-State units and State trends,

each weighted by proportional area, the trends are

somewhat less clear than they are ifthe smoothed values
92 BIOLOGICAL REPORT 90(1)

Table 4. 77le proportion oflhe area ofeach physiographic slTalum in each Siale, Ihe proportion ofeach Slate in the central

sOl/them region, and Ihe nl/mber ofrOlltes with reasonably complete and reliable data by State.

AL AR FL GA LA MS NC SC TN Total

Area (km2/1 ,(00) 81.0 83.8 87.2 116.2 72.5 76.3 78.7 47.8 66.7 710.2

Proportion of region 0.114 0.118 0.123 0.164 0.102 0.107 0.111 0.067 0.094 1.00

No. of complete routes 37 28 32 18 12 14 11 10 39 201

No. ofstrata 5 3 4 5 4 3 4 3 6 37

Proportions ofstrata within Statesa

0.172

0.365 0.360

1 Subtropical 0.130

2 Floridian 0.454

3 Lower Coastal Plain 0.028 0.327 0.113 0.112 0.037

4 Upper Coastal Plain 0.560 0.238 0.088 0.378 0.340 0.787

5 Mississippi Alluvial Plain 0.289 0.304 0.177

6 East Texas Prairies 0.245

11 Southern Piedmont 0.078 0.239

13 Ridge and Valley 0.269 0.239

14 IIighland Rim 0.066

19 Ozark Ouachita Plateau 0.473

21 Cumberland Plateau

23 Blue Ridge Mountains 0.030

" States are numhered as in Robbins et al. 1986. l"ig 1.

0.264

0.199

0.267

0.372 0.212

0.024

0.183

0.409

0.111

0.061

by route are smoothed again at the higher levels. For

Arkansas, Florida, and North Carolina, the successively

smoothed lines are given in Figs. 5b, d, and f, and the

lines based on only initial smoothing by routes and then

aggregation by weighted means are given in Figs. Sa, c,

and e. In each case the State line is the solid line.

A trend line for the region can be constructed from

values for States (Fig. 5), if they are weighted by the

proportion of the region occupied by each State. Again,

the sensitivity of the trend to variation will depend on

the amount of smoothing. If smoothing is applied only

to the route data and means are used to aggregate

successively larger areas, the regional trend (Fig. 6a) can

show the effects of individual years. Note, for instance,

the decline in 1981 in Alabama, Georgia, and North

Carolina, an effect that is particularly evident in the

Southern Piedmont (stratum 11) of North Carolina. To

discover whether this is a coincidence or a real decline

would take further work. Such phenomena are not

apparent when the data are smoothed at each stage of

the aggregation (Fig. 6b), but such a plot gives a nearly

linear regional trend against a background of less

smoothed State lines. The generality of the trend is

accentuated at larger spatial scales. The analysis is

based on data that seemed hopelessly heterogeneous at

the beginning but has been reorganized and simplified.

All States show increasing populations of pine warblers.

It would be possible to place standard error bars on the

NNRR curves to show the variability associated with

individual year estimates. However, the NNRR method

is not designed to give estimates of standard errors for

a single, overall trend measurement, such as the linear

trend on the log scale. The strength of the NNRR

method is its ability to indicate shorter term trends that

might be obscured by an overall measure.

In Fig. 6d, it is clear that in the region the pine warbler

has been especially abundant in the Lower Coastal Plain

(stratum 3) throughout the 22-year period. Note that the

physiographic region in which the population has shown

the greatest increase is the Southern Piedmont (stratum

11, mainly the Carolinas and Georgia). These increases

plus increases in the Upper Coastal Plain (stratum 4)

account for most of the large increase in the population

as a wholc (Fig. 6d). In Alabama, Arkansas, Florida, and

Mississippi, there were slight declines in the late 1970's

and early 1980's.

A widespread decline in the number of pine warblcrs

occurred after the severe winters of 1976-77 and

1977-78 (see Fig. 3 for northern Florida, Fig. 4c for

southwestern Arkansas, Fig. 6a for the entire region).

Another apparent decline occurred in 1981 in North

Carolina, Georgia, and Alabama (Fig. 6a), especially in

the Southern Piedmont stratum (Figs. 6c, d, and Fig. 7).

This decline is associated with a drought that began in

May 1980 and continued through an exceptionally cold

winter and into the very hot summer of 1981. Were

normal numbers of pine warblers present but just not

singing on these very hot, dry June days? The numbers

returned to normal values in 1982, and general increases

have continued through the 1980's. Increases in the
AVIAN POPULATION TRENDS 93

20.,--------------------,

Arkansas o

10

15

20.,--------------------,

A

70 75 80 85 90

Year

- ._-- -- -"---'-" • '_'" __ • • .-" 2

..... " .... /

/

/

/

...,.-- /

"ttt.r.:.::: < _................... 3

"'.,...... Slale Line ':lIo.

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65

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North Carolina

70 75 80 85 90

o+----........-..,...........---..........................-.-...,...........~--r~.........-.,.......j

65

20r------------------,

Arkansas E

20.,------------------,

15

85 90

Slate Line

80

\

75

11

,

. ,...... ,,'," ••••••••••- -. 3

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70 75 80 85 90

State Line .' ••- .\-•••- -_.- -.... •••••••••• 19

-....-..,

oc

5

B

10

20 .,.--------------------,

c

20 -,--------------------,

F

15

11

State Line

------"

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, / ,,'

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5

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State Line ..........................................._......_. o· - .

5

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en

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70 75 80 85 90

O+--.-~-.-~~~,......~~..___~-.,......,.-~_.__l

70 75 80 85 90 65

o+--.-~-r-~-~,......~~..___~~.--r-~--..--j

65

Year Year

Fig. 5. Stratum-within-State pine warbler (Dendroica pinus) trends~dashed lines; number indicates stranun) and State trends

(solid lines) for Arkansas, Florida, and North Carolina. Lines in A, C, and E were smoothed only at the route level. Lines in

B, D, and Fwere smoothed at the route, stratum-within-State, and State levels.
'e

12 I j \ I

tl:l

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---- ...... .......

95

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Stratum 21

Stratum 14

Strata 11

SIratum3

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StratumS

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Year

75

---.----------------------_.

.-.-.-.-.-._._._._._.-...-.-.-

70 75 80 85 90 95

Stratum 5

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70

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........ ...•..··L:··: _ _ ..' '. ..•..;;;:}.,' <fl ::..~.~_ ', ,~ , ,1"'-..\ " Stratum 2

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Mississippi

Tennessee

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Alabama

Arkansas

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Florida

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...

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C.

til

C.

o

00

c

CII

<I>

::;

<I>

"Eo

.~

...

<I>

C.

til

C.

o

00

c

CII

<I>

::;

Fig, 6. State, stratum, and pine warbler (Dendroica pinl/s) regional trends: (A) State lines smoothed only at the route level. (B) Smoothing applied at the route

stratum-within-State, State, and regional levels. (C) Stratum lines smoothed only at the route level. (D) Smoothing applied at the route, stratum-within-State, stratum,

and regional levels.
abundance of pine warblers are exceptionally large in

the Lower Coastal Plain and in the Southern Piedmont

where timber management is heaviest. In the period of

this analysis the overall acreage in managed pine in the

southeastern States has declined, but much of what

remains has matured into suitable habitat for the pine

warbler. The highest density of breeding pairs recorded

on a Breeding Bird Census in the southeastern coastal

States was 30 pairs per 40 ha in managed loblolly pine

(Pinus taeda) and shortleaf pine (P. echinata) pole

timber (Hamel et al. 1982).

The pine warbler is a member of the genus

Dendroica. Its congeners are mostly neotropical

migrants. The genus is of particular concern because

census data taken independently of the BBS suggested

that populations of the neotropical migrants are

declining (Hall 1984). Published analyses of BBS data

have not shown declines for this group (Robbins et al.

1986; Cox 1987). Biologists and conservationists should

give serious attention to why there is a disparity between

the results of different types of data. We selected the

pine warbler for study with the idea that future

comparisons with its congeners should be instructive.

Summary

The analysis of BBS data requires serious attention

to matters such as data quality, missing data,

aggregation of data by routes, and weighting of data. In

this paper we compared the linear route regression

(LRR) method of Geissler with the method of

Mountford. We proposed a new method called

nonlinear nonparametric route regression (NNRR),

which does not weight routes by components of the

inverse of their variances and which does not require

that trends be linear on a log scale, as does linear route

regression. The new method was illustrated with data

AVIAN POPULATION TRENDS 95

for the pine warbler. The advantage of NNRR is that

there is no prejudgement ofwhat a trend must look like,

other than the selection of the smoothing parameter.

With LRR, one can obtain a single P-value for the

significance of a trend, defined as log slope, but much

information is obscured.

Literature Cited

Bart, J., and J. Schoultz. 1984. Reliability of singing bird

surveys: changes in observer efficiency with avian density.

Auk 101:307-318.

Chambers, J. M., W. S. Cleveland, W. S. Kleiner, and P. A.

Tukey. 1983. Graphical methodsfor data analysis. Duxbury

Press, Boston, Mass. 395 pp.

Cleveland, W. S. 1979. Rohust locally weighted regression and

smoothing scatterplots. J. Am. Statist. Assoc. 74:829-836.

Cleveland, W. S. 1981. LOWESS: A program for smoothing

scatterplots hy rohust locally weighted regression. Am.

Stat. 35:54.

Cox,J. 1987. The Breeding Bird Survey in Florida: 1969-1983.

Fla. Field Nat. 15:29-44.

Droege, S. 1990. The North American Breeding Bird Survey.

Pages 1-4 in J. R. Sauer and S. Droege, eds. Survey designs

and statistical methods for the estimation of avian

population trends. U.S. Fish Wildl. Serv., BioI. Rep. 90(1).

166 pp.

Freedman, D., R. Pisani, and R. Purves. 1978. Statistics. W.

W. Norton and Co., New York. 589 pp.

Geissler, P. II. 1985. Estimation of animal population trends

and annual indices from a survcy of call-counts and other

indicators. Proc. Am. Stat. Assoc., Sect. Surv. Res.

Methods 1984:472-477.

Geissler, P. II., and 13. R. Noon. 1981. Estimates of avian

population trends from the North American Breeding Bird

Survey. Pages 42-51 in C. J. Ralph and J. M. Scott, eds.

Estimating the numbers of terrestrial birds. Stud. Avian

BioI. 6.

Hall, G. A. 1984. PopUlation decline of neotropieal migrants

in an Appalachian forest. Am. Birds 38:14-18.

6,...------------------------,

By STRATUM, each step smoothed

By STATE, only roules smoothed

By STATE, each slep smoothed

By STRATUM, only routes smoolhed

5 Cl.l

:;

0

Q::;

... 4 Cl.l

C.

til

C.

0

V5 3

c

Cl.l

2

I

65 70 75 80

Year

85 90 95

Fig. 7. Trend in the population of the pinc

warbler (Dendroica pinus) in thc

central-southern United States for 22

years, 1966-87, estimated hy either Statc

or stratum with two levels ofsmoothing.
96 BIOLOGICAL REPORT 90(1)

Hamel, P. fl., H. E. l.eGrant, Jr., M. R. Lennartz, and S. A.

Gauthreaux, Jr. 1982. Bird-habitat relationships in

southeastern forest lands. U.S. f'or. Serv., Gen. Tech.

Rep. SE-22.

Mountford, M. D. 1982. E~timation of population

fluctuations wit h application to the Common I3ird Census.

App!. Stat. 31:135-143.

Mountford, M. D. 1985. An index of population change with

application to the Common Bird Census. Pages 121-132

in D. Brillinger, S. f'ienberg, J. Gani, J. Hartigan, and K.

Krickenberg, eds. Statistics in Ornithology.

Springer-Verlag, New York.

Robbins, C. S., D. Bystrak, and P. II. Geissler. 1986. The

Breeding Bird Survey: its first fifteen years, 1%5-1979.

U.S. Fish Wild!. Serv., Resour. Pub!. 157. 1% pp.

Sauer, J. R., and P. H. Geissler. 1990. Estimation of annual

indices from roadside surveys. Pages 58--62 in J. R. Sauer

and S. Droege, eds. Survey designs and statistical methods

for the estimation of avian population trends. U.S. Fish

Wildl. Serv., BioI. Rep. 90(1). 166 pp.
Appendix: Data Smoothing Procedure

Analyses were done on an Apple Macintosh computer

with the following software:

Systat v3.1 (Systat, Inc., Evanston, Ill.) for WWESS

smoothing and some data selection;

StatView 512+ (BrainPower Software, Calabasas,

Calif.) for data importing;

Cricket Graph (Cricket Software, Malvern, Pa.) for

plotting; and

Absoft FORTRAN (Absoft Corp., Auburn Hills,

Mich.) for writing utility programs.

1. Determine which routes are eligible for inclusion

in the analysis. Criteria are:

a. Type 1 data for at least 10 years out of the

22-year period;

b. Type 1 data for at least 1 year in each 5-6-year

period. The periods are:

1966-71 (6 years),

1972-76 (5 years),

1977-81 (5 years),

1982-87 (6 years).

2. At this time, Systat does not do LOWESS

smoothing on a "BY" variable (as in "by route"),

so a separate file must be made for each route.

LOWESS is run on eaeh file. The column with the

smoothed data is then merged back into the

original route file to have access to the column of

year values. These merged route files are then

converted to text format.

3. A utility program (AVGIT) is run on the

smoothed route lines to get stratum-within-State

averages. The program asks how many and which

route files are to be input. A new file is created

that contains averages of all smoothed route

values of 1966, then for 1967,..., 1987. The program

is run for each stratum-within-State in the region.

4. The stratum-within-State files (nowwith one value

for each year) are read into Systat. If additional

smoothing is desired, LOWESS is run on each file.

They are saved in text format.

5. A utility program (MULTIT) reads all the

smoothed stratum-within-State files in a

particular State. The program then asks for the

proportion each stratum is ofthe entire State area.

A weighted average for each year is then

calculated for the State. The program is run for

each State in the region.

6. The State files are read into Systat. If additional

smoothing is desired, LOWESS is run on eaeh

one. These are the final smoothed State lines.

These are saved in text format.

7. A utility program (SMULTIT) reads all the

smoothed State files and asks for the proportion

each is of the entire region. A weighted average

for each year is calculated for the region.

8. If additional smoothing is desired, the region line

is read into Systat and LOWESS is run on it. This

is the final smoothed region line.

9. Similarly, steps 5 through 8 can be used to

aggregate by strata instead of by States.
98 BIOLOGICAL REPORT 90(1)

Using Checklist Records to Reveal Trends in Bird

Populations

by

Stanley A. Templc and John R. Cary

Department of Wildlife Ecology

University ofWisconsin

Madison, Wisconsin 53706

ABSTRACT.-Yemly population indices from the Wisconsin Checklist Project were

compared to indices from the North American Breeding Bird Survey, the ChristmasBird

Count, and migration counts from the Cedar Grove Ornithological Station. Checklist

results were generally concordant with results of other surveys, and they provide an

efficient method of monitoring population trends.

Introduction

When there arc doubts about the validity of trends in

bird population indices, one of the more convincing

reassurances that the trends are real is a demonstration of

concordance among several independent indices. Thus,

parallel trends in several separate populations that have

been studied independently or parallel trends derived

from studying the same population in different ways

become important corroborating lines of evidence. In this

paper, we seek such corroboration by looking for

concordance among population indices dcrived from four

independent surveymethods. Through these comparisons,

we hope to validate the use of qualitative checklist records

for detecting population trends and to provide an

additional independent source of data on trends in

Wisconsin bird populations.

Methods

Our approach is to summarize yearly population

indices (1982-87) derived from four sources: the

Wisconsin Checklist Project (WCP), the Breeding Bird

Surveys (BBS), the Christmas Bird Counts (CBC), and

migration counts from the Cedar Grove Ornithological

Station (CGOS). We then compare the population trends

suggested by these indices, looking specifically for patterns

of concordance that indicate that the WCP is tracking the

same changes in abundance as the other population

monitoring schemes. Because the WCP data span the

entire year, it is possiblc to compare them with the seasonal

data from BBS, CBC, and CGOS.

Breeding Bird Surveys

We tabulated the average number of individuals

detected per BBS route each year from 1983 through 1987

for 143 speeies of birds that were recorded in all 5 years

and that were also included in the WCP. We also noted the

names ofthe BBS cooperatorswho conducted counts each

year and the date on which they made their counts. When

analyzing the WCP data, we subsequently ignored the

WCP record<; that these cooperators had submitted during

the week of their BBS count. This insured that data from

the BBS and WCP were independent with respect to

observers.

Christma5 Bird Counts

For all of the CBC's in Wisconsin from 1982 through

1986 (Hilsenhoff 1983, 1984, 1985, 1986, 1987), we

tabulated the total number ofindividuals of each of53 bird

species detected per 1,000 party-hours each year. We also

noted the dates on which CBC's in each county had taken

place so that in subsequent analyses we could ignore WCP

records from that county and week. This insured that data

from WCP were independent of the CBC data.

Cedar Grove Ornithological Station

Each fall, from late August through late November,

observerstally the number ofmigrating raptors that pass

by the Cedar Grove Ornithological Station on the shore

of Lake Michigan (MuelIer and Berger 1967). We

tabulated the total number of individual peregrine
AVIAN POPULATION TRENDS 99

falcons and goshawks seen each year from 1983 to 1987

(G. Allez, personal communication). None of the

observers at CGOS were participants in the WCP, so

data were independent.

Results

Relation Between WCP and CBC Indices

WISCONSIN

53 SPECIES x 5 YEARS (1982-86)

Comparative Method'l

Relation Between WCP and BBS Indices

Relation Between WCP and CGOS Indices

We calculated WCP and BBS indices for 143 bird

species in each of 5 years, 1983-87. In Fig. 2 we plot the

resulting 715 WCP indices (143 species x 5 years)

against their corresponding log-transformed BBS

indices. The strong, overall, positive relation bctween

the two sets ofindices is apparent. In Table 2 we present

the significant correlation coefficients (r 0.805,

P < 0.05) between the five pairs of yearly indices for the

143 species. Of these 10 significant correlation

coefficients, 9 are positive, a higher proportion than

might be expected by chance. The single negativc

correlation is for a nocturnal owl that is not wcll indcxed

by diurnal census methods. In many of the species for

which correlation coefficients were low (i.e., < 0.5) and

nonsignificant, there was little or no variation in either

the BBS or WCP indices over the 5-year period.

In all, we calculated 265 indices of annual winter bird

abundance in Wisconsin (i,e., 53 species x 5 years), both

for the CBC and WCP. The general relation between the

WCP and CBCindicesisshown in Fig. 1 inwhich 265WCP

indices are plotted against 265 corresponding

log-transformed CBC indices. Examination of the

resulting ~tter plot revealstwo general clusters of points,

one shoWIng a steeper slope than the other, but both

showing positive correlations. The cluster of points

surrounding the shallow slope represents birds that are

locally abundant but not widely distributed around

Wisconsin in winter. Many wintering gulls and other water

birds, for example, are very abundant along the Lake

Michigan shore. Their CBC numbers are high, but theyare

reported at very low frequencies, if at all, by birders away

from the lakeshore in other parts of the State. Hence, the

overallWCP reporting frequencies for the entire State are

low even though the CBC tallies are high.

In Table 1, we present the significant correlation

coefficients(r 0.805, P < 0.05) between WCP and CBC

indices for the 53 species. There were 21 significant

correlations among the 53 species, and 20 of these were

positive, a much higher proportion than expected by

chance. For many of the species with low correlation

coefficients (i.e., <0.5), one or both of the indices varied

little over the 5 years.

1E4 1E5 1E6

~' :- .. .. : ": . raa: :..

..'

'.

100 1000

Wzsconsin Checklist Project

For each year (1982 to 1987) we tabulated reporting

frequencies (Temple and Cary 1987a) for various bird

species over time periods and geographic area.. that were

appropriate for comparisons. We compared the mean

number of individuals of each species per BBS route with

the WCP reporting frequencies for each species during

June in each year. We compared the number ofindividuals

per 1,000 CBC party-hours with the WCP reporting

frequencies for each species during December and

January 1982-83 through 1987-88. We compared the

annual tallies of raptors seen at CGOS with the WCP

reporting frequencies for these species during

September-November each year for all counties

bordering Lake Michigan.

To detect concordance between the indices from the

WCP and those of the BBS, CBC, and CGOS, we

calculated corrclation coefficients (r) for appropriate

pairs of indices. We took a positive correlation to

indicate concordance and a negative correlation to

indicate lack of concordance. Correlation coefficients

close to zero (i.e., 0.1) were somewhat ambiguous and

occurred primarily when one or both ofthe indices being

compared had not varied over the years being examined.

In certain cases we also calculated regression

coefficients that indicated the trends of the various

population indices over the 5 years of the study.

100

g

>- 80 u

z

W

::J 60 0

w

a::

u. 40 "

z

i=

a:: 20 0

CL

w

a:: 0

-20

10

CHRISTMAS BIRD COUNT

Fig. 1. The relation between WCP reporting frequencies and

birds per 1,000 CDC party-hours for 53 species over 5years

(1982-86) in Wisconsin.

We calculated WCP and CGOS indices for two

species of migratory raptors, the northern goshawk

(Accipiter genti/is) and peregrine falcon (Falco
100 BIOLOGICAL REPORT 90(1)

Table 1. Significant correlations between WC? and CBC indi.:es over 5 years.

Species

Red-tailed hawk

American kestrel

Ring-nec,;ed pheasant

Herring r,ull

Eastern screech owl

Red-headed woodpecker

Hairy woodpecker

Pileated woodpecker

Black-capped chickadee

Brown creeper

Golden-crowned kinglet

American robin

Northern shrike

European starling

American tree sparrow

Snow bunting

Purple finch

Common redpoll

Pine siskin

American goldfinch

Evening grosbeak

143 SPECIES x 5 YEARS (1982-86)

WISCONSIN

C--arrelation coefficient (r)

0.837

0.879

0.895

0.818

-0.815

0.919

0.859

0.833

0.989

0.839

0.893

0.995

0.946

0.950

0.882

0.839

0.839

0.966

0.967

0.839

0.992

100

E

>- 80t u

z

W

:::J "j 0

w

cr

'"- 40 '-' z

i=

cr 20 0

0- I w

0+ cr

ll. I

-20 I

1E-2 1E-1

BREEDING BIRD SURVEY

10 100

Fig. 2. The relation between WCP reporting frequencies and

the average number of birds per nBS route for 143 species

over 5 years (1982--86) in Wisconsin.

Tahle 2. Significant co"elations between WC? and BBS indices over 5years.

Species

Green-backed heron

Canada goose

Cooper's hawk

Rock dove

Barred owl

Yellow-bellied sapsucker

Brown creeper

Eastern bluebird

Vesper sparrow

Western meadowlark

Correlation coefficient (r)

0.805

0.868

0.807

0.938

-0.822

0.811

0.893

0.957

0.838

0.833
AVIA:" POPIJLATlO:" TRE:"OS 101

GOSHAWK

150

450

300

Fig. 3. The relation between WCP reporting

frequencies (solid bars) and the CGOS counts

(open bars) of migrating northern goshawks

(Accipiter gentilis) for the years 1982-86.

750

f600 3

o

()

w

>

o

a:

Cl

a:

«

Cl

w

()

1.5

2.0

2.5

1.0

3.0

>-

()

z

W

::J

o

w

a:

u.

Cl

fa:

o

0-

w

a:

1982-83 1983-84 1984-80; 1985-30

peregrillllS), during 198~7. These two species were

selccted for comparisons because they were known to

have undergone major changes in abundance during the

period, the goshawk declining in the aftermath of a

major crash in snowshoe hare populations in 1981 and

the peregrine falcon increasing as populations

recovered from the DDT era. In Fig. 3, the close

agreement between the WCP indices and CGOS indices

for goshawks is obvious. The correlation between the

two was high and significant (r 0.992, P < 0.05). In

Fig. 4, the annual WCP and CGOS indices for peregrine

falcons are compared, and again close concordance is

found. The correlation between the two indices was high

and significant (r = 0.964, P < 0.05).

Discussion

Having demonstrated general concordance between

WCP indices and three other independent indices of bird

abundance, we feel confident about using WCP indices to

monitor trends in Wisconsin bird populations. We are

particularly encouraged to discover that WCP indices are

sensitive enough to detect year-to-year changes that are

known a priori to have taken place in certain bird

populations. Two examples bear further discussion.

First, in the ease of migrating goshawks and

peregrine falcons, WCP indicesresponded sensitively to

known changes in the magnitude of the annual fall

migratory movements. These two cases are particularly

noteworthy because both of these raptor species are

relatively rare. The WCP reporting frequencies for the

peregrine falcon, for example, varied from 0.2% to 1.1%

between 1983 and 1987, yet even with these low

reporting frequencies the checklist data still reflected

known trends and paralleled migration counts.

A second example comes from Temple and Cary

(1987b) who used WCP data to examine year-to-year

variations in the phenologies of migrants. They found

that the phenologies of different species showed slight

but predictable differences that WCP indices were

sensitive enough to detect. Spring arrival dates of

short-distance migrants varied considerably between

years and were positively correlated with spring

temperatures, but spring arrival dates of long-distance

migrants did not. In this case a hypothesis was tested

with WCP data on year-to-year variations in bird

populations, and WCP indices were able to detect yearly

differences of only a few days in arrival dates.

Because the WCP data span only 6 years, it is perhaps

premature to discuss using these data to detect

"long-term" population trends (i.e., year-to-year

changes in abundance that have a consistent upward or

downward tendency over many years). Nonetheless, it is

possible, again, to look for concordance between trends

detected using WCP data and trends detected using

other independent data sets (Temple and Temple 1987).

From the 1982 to 1987 BBS and CBC data from

Wisconsin we identified those species that showed

PEREGRINE FALCON

Fig. 4. The relation between WCP reporting

frequencies (solid bars) and the CGOS counts

(open bars) of migrating peregrine falcons

(Falco peregrinlls) for the years 1982-86.

1.4

o 1.1

z

w

::J

o 0.8 w

a:

u.

Cl 0.5 z

fa:

o 0.2 0-

W

a:

90

fz 75 ::J

0

()

60 w

>

0

a:

Cl

45 a:

«

Cl

30 w

()

1982-83 1983-84 1984-85 1985-86
102 BIOLOGICAL REPORT 90(1)

Canada Goose

WOP ":281.9800 + 3.5500 YEAR

LOG10 (885) -26.0043 + 0.3069 YEAR *

100 2

80

E Ul

60 CD CI CD UJ

a: 0 0 lJ.. 40 ...

tll 0 0 Ul -1 ..J :z 20 • • • 0

•

0 • -2

82 83 84 85 86

Canada Goose

WOP -170.1000 + 2.2400 YEAR

LOG10 (C8C) -4.7243 + 0.1090 YEAR

100 6

0

80 0 5

E 0 to! Fi~. s. Comparisons oftrend lines determined

60 0 4 CD by linear regression of WCP data and CI to!

UJ 0 a: 0 either log-transformed DBS or CDC data. lJ.. 40 3 ...

tll 0 0 Ul • ..J X 20 • 2 0

• • • •

0

82 83 84 85 86

Eastern Bluebird

WOP -274.7600 + 3.6300 YEAR * LOG10 (8Bs) -10.3528 + 0.1248 YEAR *

100 2

80

E Ul

60 ..0 CD CI e CD UJ 0- 0 0 0 a: 0

lJ.. 40 ... • ... tll 0 0

Ul • • -1 ..J x 20 0

•

0 -2

82 83 84 85 86

YEAR
AVI~ POPULATION TRENDS 103

Ring-necked Pheasant

WOP 163.9200 1.8200 YEAR It

LOG10 (CBC) 10.9502 0.1015 YEAR

100 6

80 5

E t.J

60 4 III 0 t.J

UJ

ex: 0 lL 40 3 ...

l.!l 0 0 0 0 l1l e 0 ...J X 20 0 2

• .. • • 0

• •

0

82 83 84 85 86

Ring-necked Pheasant

WOP 181.7400 1.9200 YEAR

LOG10 (B8S) 14.8883 0.1741 YEAR It

100 2

80

E 0- l1l

60 a III 0 0 -9 III Fig. 5. Continued. UJ 0 0 ex: 0 lL 40 ...

l.!l 0 0 l1l • -1 ...J x 20 • • • • 0

•

0 -2

82 83 84 85 86

Common Redpoll

WOP -253.8200 + 3.1000 YEAR It

LOG10 (C8C) -25.2674 + 0.3370 YEAR

100 6

80 5

E t.J

60 4 III 0 t.J UJ

ex: 0 lL 40 3 ...

l.!l 0 0 l1l ...J X 20 2 0

• • ... .. • 0 •

82 83 84 85 86

YEAR
104 BIOLOGICAL REI'ORT 90(1)

significant 5-year increases or decreases (based on

slopes of regression lines being significantly different

from zero). In Fig. 5, we compare slopes of regression

trend lines caleulated using log-transformed BBS or

CBC data and corresponding WCP data. Again, a

general pattern of concordance is evident in a variety of

species, both rare and common.

A final point about using the results of the WCP to

monitor trends in bird populations is cost and ease of

data collection and management. Bird watchers are

obviously willing to participate in the WCP in greater

proportions than they arc the BBS, perhaps because it

caters to their normal birding activities and

record-keeping habits (Temple and Cary 1987a).

Because of the popularity of the WCP, temporal and

geographic coverage has been consistently good. The

ease with which data are recorded on optically

scannable forms and then transferred to

computer-compatible formats is a definite advantage.

We have, so far, been able to manage the entire WCP

data set (which exceeds 2.92 MB) on desktop

microcomputers. These efficiencies have kept costs

extremely low in view of the volume of valuable data

being handled. Not counting labor (all of which is

volunteered), the WCP has annual operating costs of

about $1,800 (mostly for forms, optical scanning,

postage, and office supplies).

In view of the apparent success of using WCP data to

monitor population trends, this program is probably one

ofthe most cost-effective monitoring programs in North

America. Furthermore, unlike the BBS, CBC, and other

programs that are temporally restricted, the WCP

coverage spans the entire year.

Acknowledgments

We are grateful for the cooperation of 431 members

of the Wisconsin Society for Ornithology who

participated in the WCP from 1982 to 1986. A.J. Temple

kept track of them and the 29,892 checklists they

submitted for analysis. S. D. Robbins provided us with

copies of BBS data for Wisconsin. G. Allez provided us

with unpublished records of migrating raptors counted

at the eGOs. The WCP received financial support from

the A. W. Schorger Fund of the Department ofWildlife

Ecology, University of Wisconsin - Madison.

Literature Cited

Hilsenhoff, W. 1983. The 1982 Wisconsin Christmas Bird

Counts. Passenger Pigeon 45: 1-16.

IIilsenhoff, W. 1984. The 1983 Wisconsin Christmas Bird

Counts. Passenger Pigeon 46:1-16.

IIilsenhoff, W. 1985. The 1984 Wisconsin Christmas Bird

Counts. Passenger Pigeon 47: 1-16.

IIilsenhoff, W. 1986. The 1985 Wiseonsin Christmas Bird

Counts. Passenger Pigeon 48:2-16.

IIilsenhoff, W. 1987. The 1986 Wisconsin Christmas Bird

Counts. Passenger Pigeon 49: 1-16.

Mueller, E., and D. Berger. 1%7. Wind drift, leading lines, and

diurnal migration. Wilson Bull. 79:50-63.

Temple, S., and J. Cary. 1987a. Wisconsin birds: a seasonal and

geographical guide. University of Wisconsin Press,

Madison. 36-+ pp.

Temple, S., and J. Cary. 1987b. Climatic effects on year-to-year

variations in migration phenology: a WSO research project.

Passenger Pigeon 49:70---75.

Temple, S., and A. Temple. 1987. Detecting year-to-year

changes in bird populations with checklist records: a WSO

research project. Passenger Pigeon 48: 158-162.
AVIAN POPULATION TRE1'i'DS 105

Detecting Trends in Hawk Migration Count Data

by

Kimberly Titusl

U.S. Fish and Wildlife Service

Virginia Cooperative Fish and Wildlife Research Unit

Department ofFisheries and Wildlife Sciences

Virginia Polytechnic Institute and State University

Blacksburg, Virginia 24061

Mark R. Fuller

U.S. Fish and Wildlife Service

PatlL-..:elll Wildlife Research Center

Laurel, MOlyland 20708

and

Dan Jacobs

Maryland Sea Grant College Program

University of Maryland

College Park, Maryland 20742

ABSTRACT.-Counts of migrating raptors tend to he extremelyvariable among hoth

sites and species. We usc linear regression, route-regression, and a nonparametric

method to estimate population trends from counts ofnorthern harriers (Circus cyaneus),

peregrine falcons (Falco peregrinlls), and broad-winged hawks (Buteo platyptems). All

methods show similar results for northern harrier and peregrine falcon trends, but only

the route regression method indicated a decline in broad-winged hawk trends. Because

of the small number of migration count sites, statistical power is an important

consideration in the analysis of trends in migrating raptors.

Introduction

Monitoring raptor populations over a large area is

difficult because raptors nest in low dcnsities compared

to other birds, and they are usually secretive and often

nest in remote areas (Fuller and Mosher 1987).

Furthermore, funds are not available to survey nesting

populations of most species. For migratory raptors, one

possible method for monitoring population change is to

1Present address: Alaska Department of Fish and Game. Division of

Wildlife Conservation. P.O. Box 20. Douglas. Alaska 99824.

count individuals passing fixed points along their

migration paths. The data acquired are known as hawk

counts. During migration many species migrate along

leading lines, which are environmental and

physiographic features (e.g., mountain ridges,

coastlines, peninsulas) that result in a concentration of

birds (Murray 1964; Mueller and Berger 1967; Alerstam

1978; Richardson 1978). In some instances, hawks have

been countcd annually for many years at noteworthy

concentration areas (Allen and Pcterson 1936; Brett

and Nagy 1973; Fuller and Titus 1990).

Most early analyses of hawk counts used empirical

methods and often employed the "running average"
106 BIOLOGICAl. REPORT 90(1)

technique of plotting data to evaluate trends in counts

(Spofford 1969; Hackman and Henny 1971; Nagy 1977;

Dunne and Sutton 1986). The methods used often

lacked rigor in their assessment of countsthat have been

shown to have high variation among years (Svensson

1978; Titus et aI.1989a). Robbins (1975) used regression

methods to evaluate trends in counts of hawks, but his

suggestions have not received wide attention, and his

examples were mostly with unstandardized data.

Recently, Ward et al. (1988) and Mueller et al. (1988)

used more rigorous evaluations to detect trends in

counts over time by standardizing their 9ata before

statistical analysis.

The use of hawk migration counts for population

monitoring has been criticized, in part, because of the

high among-year variation in counts and the inability to

tease apart the components ofvariability in counts made

at a location (Ulfstrand 1958; Kerlinger 1985; Smith

1985). Much of the among-year variation in counts is

presumed to be caused by weather (Richardson 1978;

HU5selI 1985) rather than population changes.

Consequently, Hussell (1981, 1985) used multiple

regression techniques to evaluate changes in migration

counts after accounting for the effects of weather. This

approach is useful when only short-term (e.g., 5 years)

and complete weather data are available.

We have approached the problem of detecting trends

without accounting for weather effects because these

data are not always available, and even if they are the

data entry and programming required to conduct these

analyses inhibits their timely application. Other

recommendations have been made to deal with the

problems associated with highly variable count data,

such as conducting multiple counts (Harris 1986). We

use simple linear regression, a nonparametric method,

and route-regression (Geissler and Noon 1981; Geissler

1984) to evaluate trends in hawk count data. These

statistical methods provide a basis for objective

assessments of trends in counts, but they have not all

been previously applied to hawk count data. Therefore,

our objectives are to (1) describe some attributes of

standardized hawk count data; (2) provide an

explanation of a nonparametric trend analysis method;

(3) empirically compare the results of different trend

analysis techniques; and (4) recommend appropriate

techniques for certain applications. To compare

methods, we highlight results using data from three

species of migrant hawks (northern harrier, [Circus

cyaneus], peregrine falcon, [Falco peregrinlls),

broad-winged hawk, [Buteo platyptemsJ). These three

species typify the variety of hawk count data for which

there is interest in detecting trends.

Methods

Study Sites

We demonstrate analyses for detection of trends in

hawk count data with examples from six noteworthy

eastern United States hawk lookouts (Table 1) where

data are recorded on standard forms provided by the

Hawk Migration Association of North America

(HMANA; Roberts 1985). Count data have been

acquired from some of these sites for many years (e.g.,

> 50 years at Hawk Mountain), while at other sites

standardized count data were available only from 1980

Table 1. Names, locations, referencesfor site descriptions, and numberofyears ofhawk count data that were analyzed at

each ofsix eastem hawk lookollts.

Years included Total

Name and location in analysis years References

Hawk Ridge, north shore Lake

Superior, Duluth, Minnesota 1972-87 16 Hofslund 1954, 1966

Whitefish Point, northeast tip of

Upper Peninsula, Michigan 1980-87 8 Magee 1922; Devereux et al. 1985

Derby Hill, southern shore of Lake

Ontario, ncar Mexico, New York 1979-87 9 Haugh and Cade 1966; Haugh 1972

Hawk Mountain, eastern Pennsylvania 1972-87 16 Broun 1935, 1949

Sandy Hook, Atlantic coast,

central New Jersey 1979-87 9 Eynon 1941; Clark 1978

C:1pe May, Atlantic coast,

southern New Jersey 1976-87 12 Allen and Peterson 1936
to 1987 (Fuller and Titus 1990). We arbitrarily chose not

to use data before 1972 even they were was available

(Table 1).

Hawk Species

We reviewed data from counts of 14 species of

Falconiformes, and we performed detailed analyses on

counts of northern harriers, peregrine falcons, and

broad-winged hawks. Counts for some species are

comparatively low (e.g., peregrine falcon, bald eagle

[Haliaeetlls leucocephallls]), others are moderate and

vary little among sites and years (e.g., northern harrier,

American kestrel [Falco sparverills]), while others are

high (broad-winged hawk).

We analyzed the total season-long standardized

count for a species at a site. The resulting count was

standardized in that all days (except those with

inclement weather) of the entire migration period were

surveyed, and data were reported on the HMANA

forms. Other methods of standardization could have

been chosen whereby the season-long count would be

adjusted. Examples of other adjustments include the

count per number of observation days, count per

number of observation hours, and count standardized

by seasonal or daily averages (Alerstam 1978; Titus and

Mosher 1982; Husselll985; Ward et al. 1988). Our use

of the total season-long count was due in part to some

missing information on observation hours and

observation days.

Statistical Analyses

AVIk'" POPULATION TRENDS 107

Simple Linear Regression

Testing the significance ofthe slope of a regression line

is one method for analyzing trends (Neter et al. 1985;

Holmes and Sherry 1988). Thistechnique can be useful for

analyzing trends at a single location over time. Warkentin

and James (1988) used the significance ofSpearman rank

correlation coefficients to evaluate trends, but such

methods do not specifically test for trends.

Nonparametric Rank Trend Analysis

Hawk counts often fluctuate considerably among years

at a given location. A nonparametric method might be

better suited for the detection of a trend than regression

methods because it is difficult to account for the

year-ta-year variability, much of which is presumed to be

caused by factors other than population change. In

addition, the ac;sumption of homogeneity of variances

might not be met with hawk count data. Lehmann (1975)

developed a nonparametric statistic to test for trends over

time. Thiswa<; devcloped as a one-tailed tcst and can detect

the presence of a positive trend (e.g., increasing hawk

counts with time). We modified this test to be two-tailed in

order to test for the presence of either positive or negative

trend. An explanation ofthe test statistic follows.

For a given location, hawk counts are arranged in

ascending order and assigned ranks, with ties receiving

the mean ofthe corresponding ranks (midranks; PROC

RANK TIES = MEAN, SAS 1985). The null hypothesis

is that the counts are random and do not change with

time. In other words, the assignment of the ranks is

random. Alternatively, if the counts tend to increase or

decrease with time, the ranks would do likewise. The test

statistic then is

where R; is the rank of the ith yearly count and i goes

from one to the number of years (N). Small values of D

might indicate the existence of a positive trend, and

large values of D might indicate the existence of a

negative trend.

A null distribution for D can be calculated to determine

the significance of a positive or negative trend. This

calculation becomes arduous because as N increases the

possible number of distinct arrangements of the ranks

increases by N! Lehmann (1975) showed that the

distribution can be approximated by the normal

distribution when a continuity factor of 1 is added to D.

The expected value of D is given by

Simple linear regression, nonparametric

rank-correlation trend analysis, and route-regression

trend analysis were used to test for trendsin hawk counts

among years. We use the term trend to mean that a

statistical test reveals a statistically significant change in

the counts over the period. We assume this statistical

significance is an indicator of some change in the

number of raptors. Ultimately, wildlife managers will

want to assume that changes in counts are an indication

of changes in raptor numbers rather than a result of

observer effort, counting methods, or weather.

A consideration for our data is the ability to detect

significant trends with only 8 years of counts from a site.

Another consideration is serial correlation of the

counts, because both simple linear regression and

nonparametric rank-trend analyses assume that counts

are independent among years. However, tests of serial

correlation (time-series) are not applicable to our small

sample of years (Neter et al. 1985).

D=""(R-i)2 L... I .

(1)
108 BIOLOGICAL REPORT 90(1)

(3)

Using these three equations, a z-score can be calculated

as

where lli is the number of observations with the ith rank,

and the variance is given by

Results and Discussion

species, and only six sample sites. The

route-regression technique cannot be used for

analyzing trends at a single site. No observer

covariates were used in our analyses. We weighted our

analyses by the magnitude of the counts (James et al.

1990). The route-regression analyses were conducted

by J. R. Sauer.

(4) <l> =[D - E(D)] /Var(D{2 .

A negative <t> (i.e., the calculated D is greater than

expected) indicates the possibility of a positive trend.

Likewise, a positive <t> (i.e., the calculated D is less than

expected) indicates the possibility of a negative trend.

The null hypothesis of no trend is then tested using the

two-tailed probability.

We used the two-tailed test to detect significant

positive or negative trends over time at each site.

Lehmann (1975) also expanded the test statistic to test

for a positive trend among groups (e.g., sites). For each

site, the statistics shown in equations 1-3 are calculated

and summed. The z-score calculated is similar to

equation 4 and is given by

(5)

Again, we modified the test and used the two-tailed

probability in order to test for positive or negative

trends. Results from this method can be compared with

linear and route-regression techniques.

Route-regression Trend Analysis

We used route-regression trend analysis to test for

long-term trends in counts of migratory birds

(Geissler and Noon 1981; Geissler 1984). This

method was developed for use with surveys such as

the Breeding Bird Survey (BBS), where individual

survey routes were sampled over a number of years

(e.g., Robbins et al. 1986). This method is generally

applied when a large number (> 20) of routes (sites)

are surveyed for at least 5 years. In contrast to BBS

data, our data included much larger counts for most

Variability in Counts

.Counts varied widely among species and sites. For

example, mean counts of golden eagles (Aquila

chrysaetos) were 9.1/year at Whitefish Point, while

counts of sharp-shinned hawks (Accipiter striatus) at

Cape May averaged 39,632/year. Mean counts of

peregrine falcons were usually less than 100 per site

per year (excepting Cape May), those of the northern

harrier were less than 1,000 per site per year, and

broad-winged hawk counts were sometimes of the

order 104 (Table 2; Figs. 1, 2, and 3). Note that the

within-site and among-year variation in counts was

not higher for the broad-winged hawk than for other

species. Although this species has high variability in

absolute counts among years, its coefficient of

variation was similar to that of species with lower

annual counts. Coefficients ofvariation averaged 40%

for all 14 species at six sites. This variability seems to

be inherent in migration count data, and might limit

detection of trends over short periods (Svensson

1978). This among-year variation in counts generally

is attributed to the effects of weather (Richardson

1978; Kerlinger et al. 1985; Kerlinger and Gauthreaux

1985); therefore, increased standardization of the

specific protocol for counting hawks in the field might

only reduce the variance by a small amount.

Linear Regression

Linear regression is a useful technique for testing

trends at one location, and we found significant trends

in 9 of 18 analyses (Table 3). Because the number of

years varied among sites, the F-value required to obtain

Table 2. Meall allnualCOli/its ofnorthem hamers(CircIlS cyanells), broad-wingedhawks (Bllteo platyptems), andperegrine

falcons (Falco peregrinus) at six: eastem hawk lookouts. Coefficients of van'ation given in parenthesis.

Count site

Species Hawk Ridge Whitefish Point Derby Hill Hawk Mtn. Sandy Hook cape May

Northern harrier 360.6 (46) 266.8 (46) 729.7 (24) 276.1 (31 ) 316.6 (41) 1,815.4 (46)

Broad-winged hawk 29,343.3 (44) 4,816.6 (61) 27,573.3 (35) 10,526.6 (55) 160.2 (59) 4,126.1 (111 )

J'eregrine falcon 12.1 (55) 12.8 (59) 2.2 (44) 11.8 (61) 5.3 (53) 316.4 (64
NORTHERN HARRIER

significant trends also varied. For example, with 8 years

of data at Whitefish Point, Fcril.05; 1.6 df = 5.99, whereas

with 16 years of· counts at Hawk Ridge,

F crit .05; 1,14 df = 4.60. Therefore, we might, on average,

3500

3,000

2500

f- 2000 :z:

::>

0

u 1,500

LOCATION

_ HAWK MTN

__ HAWK RIDGE

_______ CAPE MAY

. __ DERBY HILL

WHITEFISH PT

_._ SANDY HOOK

AVIA,'\i POPULATION TRENDS 109

expect more statistically significant trends at sites with

more years of data, if biologically real trends occurred

with equal frequency among sites.

Hawk count data are subject to similar concerns of

other time-series data. Namely, hawk count data are

highly variable (Figs. 1 and 2a), trends might not be

linear, and a single regression line might mask details of

biological interest. Statistical and nonstatistical

methods are available to explore these concerns;

however, regression can still be one of the more useful

methods for testing a trend. For counts from a single site

with less than 10-15 years, data smoothing (Velleman

and Hoaglin 1981; Chambers et al. 1983) and more

rigorous testing methods (Edwards and Coull 1987)

might not be valid alternatives.

1,000 i

5001

i

0 1

72 74 76 78 80 82 84 86

YEAR

Fig. 1. Annual counts of northern harriers (CirclIs cyanells) at

six eastern hawk lookouts. 111e number of years of annual

counts varies by location.

BROAD-WINGED HAWK

LOCATION

_ HAWK MTN

__ HAWK RIDGE

CAPE MAY

___ DERBY HILL

WHITEFISH PT

___ SANOY HOOK

72 74 76 78 80 82 84 86

Nonparametric Rank Trend Analysis

We found significant trends in 8 of 18 analyses, all of

which were the same as the regression method. Only in

the tests of the northern harrier counts from Whitefish

Point were the results different between methods. The

P value from the regression analysis was 0.093, and from

the nonparametric method it was 0.108, so even in this

instance the levels of significance were nearly equal.

The non parametric method has the appeal that

homogeneity of variances over time and a linear pattern

need not be assumed. High among-year variability in

counts is not of consequence for this nonparametric

method, only the order of ranks. Empirically, the

statistical power obtained with the nonparametric

technique matched that oflinear regression, making the

technique appealing to those who have data suited to the

method. A disadvantage of the nonparametric trend

analysis is that there is no slope estimate, so we cannot

estimate an annual change, which is routine with

regression approaches.

100,000

10000

f-

:z: 6 1,000

U

100

10

\..../.

/

/ /"\. /' ...../

/ "\./

i

72 74 76 78 60 82 84 86

YEAR

LOCATION

_ HAWK MTN

__ HAWK RIDGE

.•••••. CAPE MAY

___ DERBY HILL

WHITEFISH PT

_._ SANDY HOOK

1.000

100

f-

:z:

::>

o

u

10

PEREGRINE FALCON

/'

\ / \

\ /

\ /

\ /

V

LOCATION

_ HAWK MTN

__ HAWK RIDGE

••• . CAPE MAY

___ DERBY HILL

WHITEFISH PT

_._ SANOY HOOK

Fig. 2. Annual counts of broad-winged hawks (BilleD

platyptems) at six eastern hawk lookouts. Counts are plotted

both as an arithmetic (A) and a logarithmic scale (B).

72 74 76 78 eo 82 84 86

YEAR

Fig. 3. Annual counts of peregrine falcons (Falco peregr;nlls)

at six eastern hawk lookouts plotted on a logarithmic scale.
110 BIOLOGICAL REPORT 90(1)

Table 3. Comparison oflinearregression and nonparametric rank trend analysis techniques based 011 analysis of single

sites.

Peregrine falcon

Species

Northern harrier Broad-winged hawk

Location Trend" Significanceb Trend Significance

Hawk Ridge

linear regression NS NS

nonparametric method NS NS

Whitefish Point

linear regression Pos. * NS

nonparametric method NS NS

Derby Hill

linear regression NS NS

nonparametric method NS NS

Hawk Mountain

linear regression Pos. *** NS

nonparametric method Pas. *** NS

Sandy Hook

linear regression NS Pos. **

nonparametric method NS Pas. **

Cape May

linear regression Pos. ** NS

nonparametric method Pos. ** NS

, Trends indicated are: Pas. = positive: Neg. = negative: blank = no significant trend.

Significance' = P < 0.10: •• = P < 0.05: ••• = P < 0.01: NS = not significant.

Trend

Pas.

Pos.

Pas.

Pas.

Pas.

Pos.

Pas.

Pas.

Pos.

Pas.

Significance

*

*

***

**

NS

NS

***

**

**

***

***

***

Route-regression Trend Analysis

Broad-winged hawk counts decreased, and northern

harrier and peregrine falcon counts increased

significantly when the route-regression trend analysis

was applied (Table 4). For the northern harrier counts,

three of six univariate regression analyses indicated a

positive trend, and the weighted, pooling capabilities of

the route-regression method detected an overall

positive trend. The analysis weighted most on the high

and increasing counts at Cape May (Fig. 1). Despite the

large bootstrapped variance, peregrine falcon counts

were found to be increasing at a substantial rate, a

pattern supported by many other studies (Cade et al.

1988). Only at Derby Hill, where few peregrine falcons

were counted (Table 2), was there no detectable trend.

We found the route-regression method interesting in

that it detected a significant downward trend in

broad-winged hawk counts. Neither univariate method

indicated a significant downward trend at any of the six

sites (Table 3). It is difficult to correlate the

route-regression downward trend with other surveys

because broad-winged hawks are not subject to

independent intensive surveys as are conducted for

some species, such as the bald eagle and peregrine

falcon. The BBS is an extensive survey, which obtains

some data on broad-winged hawks, and Titus et al.

(1989b) analyzed BBS data for broad-winged hawks in

Table 4. Comparison ofroute-regression trend analysis and pooled nonparametric rank-trend analysistestingfor trend~ in

hawk CO/lilts at six eastem hawk lookouts.

Route regression method Nonparametric method

Species Total count" Variance Trendb Significance" Trend Significance

Northern harrier 5.09 7.05 Pas. * Pas. ***

Broad-winged hawk -2.74 1.23 Neg. ** NS

Peregrine falcon 15.30 29.83 Pas. *** Pas. ***

" Annualized percent increase or decrease in counts.

Trends indicated are: Pas. = positive: Neg. = negative.

C Significance' = P <0.10:" = P <0.05: ••• = P <0.01: NS = not significant.
the northeastern United States. They found an

increasing trend in the more-rural regions of the

Northeast, and a decreasing trend in the

more-developed regions. Therefore, the ability of the

route-regression method to detect a long-term trend

among these variable counts is of interest.

Absolute counts of broad-winged hawks are

notoriously variable (Fig. 2a). Other species that have

similar among-year variability include steppe buzzards

(Buteo buteo VIi/pinus) at Eilat, Israel (Shirihai 1987),

and honey buzzards (Pemis apivoms) at Falsterbo,

Sweden (Alerstam 1978). The route-regression method

might be useful for these species when counts from

various sites can be pooled.

In contrast to the route-regression method, the

pooled-sites nonparametric method was unable to

detect a trend in broad-winged hawk counts. The

nonparametric method detected a highly significant

(P = 0.004) positive trend in northern harrier counts,

whereas this trend was only marginally significant with

the route-regression method. An important distinction

among these two techniques is the weighting of the

route-regression method by abundance (counts),

compared to no weighing (all sites count equally) with

the nonparametric method. It might be possible to

modify equation 5 to use a weighted E(D) and Var (D).

Conclusions

Additional exploration of the statistical methods for

analyzing hawk count data for the purpose of detecting

trends would be useful. Simulation studies of the power

to detect trends (Gerrodette 1987), further exploration

of autoregressive trend analysis techniques (Edwards

and Coull 1987), and studies of the implications of

violating assumptions (Harris 1986) would be useful to

the many biologists and managers who require trend

information. Our study indicated that each of the

methods we used had merit under differing situations.

For example, researchers at Hawk Mountain are

currently analyzing their long series of count data.

Regression approaches are aptly being used, although

significant positive serial correlation has been found for

some of the species-count data (K. Titus, unpublished

analysis). It is not known the degree to which positive

autocorrelation influences the statistical significance of

linear regression approaches and the potential for type

I and type II errors, although Edwards and Coull (1987)

consider the3e problems to be serious.

Our nonparametric approach is easy to understand

and is useful to indicate only whether counts are

increasing or decreasing over time. This method gains

statistical powerwhen two or more sites are pooled, and,

unlike route-regression, it does not require the large

number of sites. The nonparametric method does not

AVIA1"l POPULATION TRENDS 111

weight sites based on counts as docs the

route-regression method, but a weighting procedure

could be incorporated. Finally, the nonparametric

statistical methodology is easily programmed, and we

have developed a SAS-MACRO program to execute it

(SAS 1987).

The route-regression method has intuitive appeal

given certain considerations. First, count data have to be

available from a number of sites. We believe that data

from as few as five sites can be used, even though those

interpreting BBS results with the route-regression

method advise against using data sets with less than 10

routes. Presently it is not possible to obtain standardized

data from 10 hawk count sites. However, hawk count

data typically have much larger counts for a species than

do BBS routes. Another consideration of the

route-regression method is that the sites must be

independent, and a final point is that long-term trends

are the objective of interest. The option of weighting

sites by abundance has appeal because we usually want

to make inferences about trends in a population, so sites

that sample a larger proportion influence the analysis

more than sites with low counts. The route-regression

methodology is not easy to understand; however, a

personal computer version of the analysis is available.

Our exploration of several techniques for detecting

trends in hawk count data provides options for

managers who interpret these data for monitoring

raptors. We encourage additional investigation of

statistical analyses of hawk counts and more research on

the influence of weather on counts and monitoring

efforts. Understanding the total variance structure is of

primary importance for determining the significance of

trends in hawk count data, which are inherently variable

(Fig. 2a). This is because the statistical methods might

not detect a trend when one actually exists, and a type

II error results. We suspect that this is the most likely

error to occur with hawk count data. Studies to assess

the power of the variety of trend analysis methods are

therefore important because there are limits to

improving the quality of the data obtained in the field.

Acknowledgments

We thank J. L. Ruos and the Fish and Wildlife

Service, Office of Migratory Bird Management for

initiating the standardized data collection, and the staff

and volunteers who collected the data. Data collection

was supported, in part, by the Office of Migratory Bird

Management through contracts with Cape May Bird

Observatory, Derby Hill Bird Observatory, Onondaga

Audubon Society, Whitefish Point Bird Observatory,

and the Duluth Audubon Society. L. Goodrich and S.

Senner of the Hawk Mountain Sanctuary Association

generously provided us with their recent data. Our
112 BIOLOGICAL REPORT 90(1)

analyses were supported by the Office of Migratory Bird

Management and by W. S. Seegar through the Virginia

Cooperative Fish and Wildlife Research Unit, with

M. R. Vaughan providing technical assistance. We

thank J. R. Sauer for conducting the route-regression

analyses. Reviews by S. Droege, P. Geissler, and

J. Hatfield were very helpful.

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AVIAN POPULATION TRENDS 113

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Ornithol. 59:209-214.
114 BIOLOGICAL REPORT 90(1)

Evaluation of the Sensitivity of Breeding Bird SurveysUsing a

Stochastic Simulation Model

by

James Cox

Nongame Wildlife Program

Florida Game and Fresh Water Fish Commission

620 South Meridian Street

Tallahassee, Florida 32399

ABSTRACT.-A stochastic simulation modcl was developed to mimic sampling along

breeding bird surveys. The model was then used 10 assess the influence of five variables

on the detection of declining bird populations. The variables analyzed were (1) the

number ofroutes sampled in a region, (2) the length oftime a population was monitored,

(3) the magnitude of the yearly decline in a population, (4) the initial mean number of

stops at which a species was detected along survey routes, and (5) the magnitude of

variation about the mean number ofstops at which a species was detected.

The number of years that surveys are conducted influences the detection of trends

most dramatically and accounts for approximately 80% ofthe variation in aecurate trend

detection. The number of routes sampled in an area is also very important in detecting

trends. These two variables can be directly manipulated by survey organizers and analysts.

The magnitude of the yearly decline and the magnitude of the variation in the mean

number of SlOpS at which a species occurs also influence the detection of trends, but

appreciably less than the former two variables. Using data obtained from Florida

breeding bird surveys and results of the simulations, coarse estimates are made of the

sample needed to detect population declines for common species.

Introduction

The North American Breeding Bird Survey (BBS) is

considered a valuable method for monitoring population

trends in common species over large areas (Thompson

1980; Bystrak 1981). The ability of this survey to monitor

trends accurately depends on many factors, however, such

as the magnitude of the change in the population size of

a species over time, the magnitude of stochastic

year-to-year fluctuations in population size, the lcngth of

time a population is monitored, the number of survey

routes sampled in a region of interest, and the sampling

error associated with surveys. Evaluating how these

factors influence the detection of population trends can

be important when deciding the time and effort one

should spend conducting surveys of this type. It may take

a 10-fold increase in the number of surveys in an area to

gain only a 2-fold increase in the rc1iability of trend

estimates for certain species. Of course the Ievc1 of

reliability desired will also influence the intensity of the

survey effort required.

Bystrak (1981) used BBS data to document sharp

declines in Carolina wren (Thl)'othonls ludovicianus)

populations in years following severe winters. Though

sharp changes in populations may be detected

consistently with breeding bird survey data over short

intervals of time, detecting gradual trends that extend

over several years or decades may be more difficult,

particularly if the sampling effort is relatively small in an

area of interest. Many resource-management

organizations are interested in monitoring the gradual

changes that might occur in a State or Province over

time, but very gradual trends may be masked over short

periods oftime by a number of other factors. In Florida,

for example, the common ground dove (Columbina

passerina) shows an unsteady decline in its frequency at

BBS stops over a 19-year period (Fig. 1). This apparent

declil,1e would be difficult to detect over smaller periods

of time, since there was considerable variation in the

frequency of stops at which the bird was detected.

Here I use a simple stochastic model for the

occurrence of a species at stops along survey routes to
AVIAN POPULATION TRENDS 115

(J)

0...

o

f-

(J)

>-

()

z

W

::J

a

w

et:

l.L

10

5 •

•

•

·1.·11·1I11 1 [ 111

j lj 1·1]1 ••••••

ill 1 11

Fig. 1. Plot of mean frequency of

occurrence against year for the

common ground dove (Collllnbina

passerina) in Florida. Error bars

represent a single standard deviation

about the mean.

74 77 80 8.3 86

YEAR

68 71

0-t--"---"---t--"---+-=-----t-"'---+----+----+-------1

65

evaluate how certain variables affect the sensitivity of

these surveys in following gradual population declines.

Droege (1990) described the close relation between the

occurrence (or frequency) of species at stops and the

overall abundance of species encountered along survey

routes, and this variable was used in other analyses of

bird population trends (Cox 1987; James et al. 1990).

Though several factors may influence the ability of

roadside surveys to monitor trends, only five variables

were analyzed here: (1) the number of different surveys

sampled in a region; (2) the length of time the

population was monitored; (3) the magnitude of the

decline in a species' population each year; (4) the initial

mean number of stops at which a species was detected

among surveys; and (5) the magnitude of the yearly

variation among surveys for the mean number of stops

at which a species was detected. Data collected for 73

species occurring along 37 Florida surveys conducted in

1972were used to estimate some ofthese parameters for

stochastic simulations.

Model Description and Methods

The simulation model uses the frequency (or rate) of

occurrence at stops along a group ofsurvey routes as an

index to population size for a species in a region (e.g.,

stratum, State, or Province). The average frequency of

occurrence at stops along all survey routes in a region is

set before the initiation of simulations. Variation about

the average frequency of occurrence is also set at the

outset and follows a uniform distribution. Florida data

collected in 1972 indicate that this distribution

reasonably approximates the actual variation observed

for many species.

The actual number of stops at which a species is

detected is determined by a pseudorandom number

generator for each route within each year. The

simulation tracks the fate of a hypothetical species along

a single route over the time period simulated before

resetting all variables and coriducting a simulation of

another route using the same initial set of conditions.

The magnitude of variation about the mean frequency

among routes is expressed as the coefficient of variation

(C.V.; Brown and Hollander 1977) throughout this

paper. An example of data generated from this model is

presented in Fig. 2.

Several levels for the frequency of occurrence of a

species along routes and for the C.V. of frequency

among routes were allowed. Initial frequencies of 3, 5,

10, and 15 stops per route were used, as were C.V.'s of

50, 80, and 110%. These ranges were selected based on

the Florida BBS data collected in 1972. A plot of the

mean frequency of occurrence versus its corresponding

C.V. for the 73 species (Fig. 3) showed that the values

used in the model approximate those observed for these

common species. Note, however, that the relation

between these variables is approximately log-linear and

that the C.V. increases with increasing frequency at

stops. This relation was not specifically accounted for in

these simulations, but a few different values for the C.V.

were modeled with different values for the frequency

along routes.

A declining trend was established among simulated

routes by multiplying the overall frequency among

routes by a percent change from the previous year. This

value then became the new average frequency for the

region and was used in the pseudo-random number

generation. Annual declines of 5, 10, and 15% were

simulated, which reflect moderate, strong, and severe

changes in the frequency of occurrence at stops. If no

variation about the frequency at stops was allowed, a

species with a 5% annual decline would show a .95/1

decrease in its frequency /I years after the simulationwas

commenced. The levels of decline modeled here are
116 BIOLOGICAL REPORT 90(1)

OOt Initial Frequency = 20 (s.d. = 8)

(Ii Annual Decline = 10%

[L 25 D f:::.

0

l-

(n

•

20 Fig. 2. Example of data generated from the f- D D stochastic model for four routes using an « .A

initial frequency of 20 stops per route, a >- 15

coefficient of variation for frequency of U ! z • 0 0 60%, and an annual decline of 10% per w 10 .A 0

year. :=> 0 !

0 .A 0 0

W & • D .A er:: 5 0

• .A

l.L .A • • • f:::..

0

0 2 3 4 5 6 7 8 9 10 11

YEARS

probably fairly severe compared to natural levels of

decline over large areas.

The influence of the number of routes surveyed

within a region each year and the influence of the

number of years monitored were evaluated using several

arbitrarily selected values. The number ofroutes was set

at 5, 10, 20, and 40 routes per year, and the number of

years which the simulations were carried out was set at

5, 10, 15, and 20.

Forty simulations were run for each of the various

combinations of years monitored, routes run,

proportion decline per year, and so forth. Each of the

simulations was then analyzed using linear regression

to determine how frequently a declining trend could

be detected for the 40 trials run under the same set of

conditions. The regression model used was

log(frequency) = constant + trend (or slope) * year.

Log transformations were made because the yearly

change in frequency along survey routes is log-linear

(i.e., a proportion per year). This analysis should

provide a coarse, statistical projection of the

population over the time period modeled. A declining

trend was considered accurately detected if a

one-sided test for a negative trend achieved a P-value

:::; 0.10. I used this somewhat liberal definition for

detection because linear regression analysis may not

be an appropriate method for analyzing trends such as

these (Geissler and Noon 1981).

There are 550 different unique combinations for the

variables analyzed here, but not all of these were

modeled. A subset of 220 different combinations was

used, .selected randomly from the total number of

possible combinations. This number of combinations is

still large, so a stepwise regression analysis was

performed to determine which of the variables best

explained the observed variation in the detection of

trends. The dependent variable for this analysis was the

proportion of accurately detected trends in 40 trials. An

>-

1.2 ••

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:=>

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• • • • Fig. 3. Plot of mean frequency at SlOpS

l.L

.~& • • • against the corresponding coefficient of

0::: • variation for 73 species. Species were

l.L

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•

• • •

detected on 37 surveys conducted in

(J) .... • Florida in 1972.

> 0.4

•

u r •

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AVERAGE FREQUENCY
arbitrarily selected alpha value of 0.15 was used to enter

or remove variables from this regression analysis.

Results

All five variables entered the stepwise regression

analysis with the alpha value used. Within the range of

values modeled here, the most important predictor of

successful trend detection was the number of years

simulated (Table). This variable accounted for about

80% of the sample variance. It was followed in

importance by (1) the number of routes surveyed in a

region; (2) the percent of annual decline; (3) the

coefficient of variation; and (4) the frequency of

occurrence along the route. All five variables taken

together explained about 97% of the sample variance.

The power curves presented in Figs. 4 and 5 help to

demonstrate the influence ofsome of these variables on

detecting declining trends. The influence of route

number and the number of years of coverage on trend

detection can be fairly substantial (Fig. 4) when other

variables are held constant. The influence ofthe number

of routes and the severity of the decline are also fairly

large when other variables are held constant (Fig. 5).

Discussion

It is encouraging to see that the two variables exerting

the greatest influence on detecting simulated trends are

variables that can be directly manipulated by survey

organizers. The number of years a population is

monitored (or simulated) and the number of routes

surveyed in a region each year account for most of the

variation observed here in trend detection. These

variables reflect the level of commitment given to such

surveys, and it is especially important to carry out

surveys over many years.

Within the range of values evaluated here, other

species-specific traits, such as a species' rclative

frequency on surveys, the year-to-year variation of a

spccics among survey routes, and a species' annual rate

of decline, affect the detection of trends in these

simulations less so than do the length and intensity of

AVIA.'Ii' POPULATION TRENDS 117

sampling. The relations between accurate trend

detection and the C.V. among survey routes and rate of

decline are intuitively obvious. The smaller the variation

in frequency among routes, the easier it is to detect

trends accurately. It is also easier to detect trends iflhe

rate of annual decline is greater. The negative relation

between the rate of occurrence at stops and the accurate

detection of trends may be an artifact of the simulation

or the regression techniques used here.

The C.V. for the frequency at stops might be reduced

by repeatedly sampling survey routes each year

(e.g. three surveys of each route) or by collecting habitat

information along routes and incorporating these data

into trend analyses. The reduction in C.V. would have

to be moderately large, however, for this measure to

outweigh the potential contribution of simply adding

additional routes in a region or extending the duration

of the survey period.

Organizations interested in monitoring bird

populations over small areas (e.g., a State or Province)

often ask how intense the sampling effort must be to

detect a persistent trend in a species, given a desired

level of confidence. Such estimates might be made using

breeding bird survey data for the area of interest, but it

is relatively difficult to hold all ofthe relevant conditions

constant or to have knowledge of underlying values for

many of the estimated parameters. The number of

routes surveyed in Florida each year, for example, has

fluctuated considerably since the inception of breeding

bird surveys in 1966, and only 17 routes have been

sampled consistently since 1969 (Cox 1987).

The stepwise regression equation obtained above

could provide a coarse "guesstimate" of the sampling

intensity needed to detect a declining trend (given the

criterion used here for successful detection, namely

linear regression analysis with a one-tailed P-value

:5 0.10). As an example, estimates of the mean

frequency and C.V. observed for species detectcd on

Florida breeding bird surveys in 1972 were used in

conjunction with the stepwise regression equation to

approximate the number of routes needed to detect at

least a gradual 5% dccline over a 5-year period. The

values for frequency of occurrence and C.V. are not

Table. Stepwise regressioll analysis for five variables used to predict the variation ill the proportion ofsuccessfully

detected trends. Variables are listed in the order in which they entered the model.

Variable Coefficient R2

Years of coverage 0.056 0.817

Routes covered 0.009 0.891

Annual decline 4.287 0.910

Coefficient of variation

for frequency -D.345 0.957

Frequency at stops -D.D08 0.968
118 BIOLOGICAl, REPORT 90(1)

(J)

W

Z

-l

o

W

o

I

f5:

100

0-0 5 Routes

• -. 10 Routes

6.-/:; 20 Routes

A-A 40 Routes

Fig. 4. Plot of the effects of number of

routes and years of coverage against

the proportion of accurate trend

detections for 40 trials. Values for

percent decline per year, initial

frequency, and coefficient of variation

were held constant at 10%, 5 stops,

and 60%, respectively.

10 15 20

o+-------+-------!-------+

5

YEARS

presented here for each species, but the number of

routes needed ranged from 57 to 109 for all species

analyzed. The number obtained for each speciesis a very

coarse estimate of the number of routes on which the

species would need to occur to be about 90% sure of

accurately detecting at least a 5% annual decline in the

species. These estimated samples are likely to be off by

a fair margin, since this analysis extrapolates well

beyond the number of routes (40) allowed for in the

simulations. In addition, the family-wide error rate

resulting from an analysis extended to all 73 species

would lower the level of confidence.

By increasing the number of years that this group of

species is monitored, however, considerably fewer

surveys would be needed to detect this level trend. To

detect the same trend over 10 years requires 26--66

routes, and to detect the same trend over 15 years

requires a range of 8-32 routes.

The importance of detecting declining trends early

versus the cost of providing a sufficient level ofsampling

can also be evaluated with the type of information

provided earlier. For example, the average number of

routes that these 73 species were detected on in 1972

was 21.3 (s.d. = 9.2), or roughly 60% of those surveyed

that year. Thus, a two- to fourfold increase in the number

of Florida routes seems appropriate if the goal of the

monitoring program is to detect gradually declining

trends over a 5-year period. The number of surveys in

Florida was doubled in 1987, but data for this and later

years have not been analyzed. An even larger sample is

probably needed to ensure that many uncommon

species are present on a sufficiently large number of

routes to satisfy this goal. The establishment of

nonrandom routes to sample areas containing rare

species (or groups of rare species) may be another

method of increasing detection levels.

Breeding bird atlas projects (Udvardy 1981) are

another monitoring tool that might be considered for

monitoring rare or uncommon species over one to two

decades, though atlas projects are probably less

effective in monitoring trends for common species

over short periods of time. The annual cost of the atlas

100

20

60

Fig. S. Plot of the effects of percent annual

decline and the number of routes

sampled against the proportion of

accurate trend detections for 40 trials.

Values for years of coverage, initial

frequency, and coefficient of variation

were held constant at 5 years, 15 stops,

and 60%.

(J)

W

Z

-l

o

W

o

I

f5:

so

40 ~~~:~:

",~o 0-0 5 Routes • .-. 10 Routes

/:;-,6 20 Routes

A-'" 40 Routes

0.10 0.15

PERCENT ANNUAL DECLINE

O+---------t-----------+-

0.05
project in Florida, however, is nearly 15 times that of

the current cost of the State's breeding bird survey

activities. (This estimate does not include

administrative and data entry services provided by the

U.S. Fish and Wildlife Service.) Such costs need to be

carefully considered, and there have not yet been any

temporal comparisons made of atlas data obtained for

a region. Completely novel survey techniques may be

needed to monitor some rare populations efficiently

(Sudman et a1. 1988).

Droege and Sauer (1987) assessed the statistical

significance of trends if a species was present on at least

10 routes in a stratum or Province. Though their analysis

considered changes in abundance rather than frequency

at stops (as modeled here), the two indices to population

size are comparable because there is a strong, positive

correlation between them (Droege 1990). Again, using

the success criterion defined here and data for Florida

birds sampled in 1972, this minimum sample would detect

an annual 5% decline about 43-83% of the time over a

5-year period, depending on the species. The same

sampling intensity would detect more than 99% of the

declinesiflosses were 15% per year over the same period.

Though these estimates ofrequisite sampling intensity are

crude, a more strict definition for a sufficient sample may

be needed to ensure accurate detection ofcertain gradual

trends over short periods ( < 10 years).

The minimum samples used (consistent occurrence

on at least two routes) in a IS-year analysis of Florida

data (Cox 1987) are inadequate for several uncommon

species that may be undergoing gradual declines. For

the 73 species observed in 1972, annual declines must

fan in the 3.5-14.4% range to be detected over this

IS-year period. This is the average minimum level of

decline that would be detected approximately 90% of

the time with the regression method applied here to the

simulation data.

The measure of abundance along routes might

enhance the detection oftrends along survey routes, but

this is more difficult to model and adds another level of

complexvariation to the problem (Bart 1985). There are

problems with this measure that may decrease its

AVIA1'>/ POPULATION TRENDS 119

effectiveness (James et al. 1990). The cyclic nature of

certain populations may also make it much more

difficult to estimate trends except with data sets that

extend over many years.

Acknowledgments

I thank C. Bunck and J. Hatfield for helpful

comments on an earlier draft of this paper.

Literature Cited

Bart, J. 1985. Causes of recording errors in singing bird

surveys. Wilson Bull. 97:161-172.

Brown, B.,Jr., and M. Hollander. 1977. Statistics, a biomedical

introduction. J. Wiley & Sons, New York.

Bystrak, D. 1981. The North American breeding bird survey.

Pages 34-41 in C. J. Ralph andJ. M. Scott, eds. Estimating

numbers of terrestrial birds. Stud. Avian BioI. 6.

Cox, J. 1987. The breeding bird survey in Florida: 1969-1983.

Fla. Field Nat. 15:29-56.

Droege, S. 1990. The North American Breeding Bird Survey.

Pages 1-4in J. R. Sauer and S. Droege, eds. Survey designs

and statistical methods for the estimation of avian

population trends. U.S. Fish WildI. Serv., BioI. Rep. 90(1).

166 pp.

Droege, S., and J. R. Sauer. 1987. Breeding bird

survey-administrative report to cooperators. U.S. Fish

and Wildlife Service, Laurel, Md. 8 pp.

Geissler, P. B., and B. R. Noon. 1981. Estimates of avian

population trends from the North American breeding bird

survey. Pages 42-51 in C. J. Ralph and J. M. Scott, cds.

Estimating numbers ofterrestrial birds. Stud. Avian BioI. 6.

James, F. c., C. E. McCulloch, and 1.. E. Wolfe. 1990.

Methodological issues in the estimation of trends in bird

populations with an example: the pine warbler. Pages

84-97 in J. R. Sauer and S. Droege, eds. Survey designs and

statistical methods for the estimation of avian population

trends. U.S. Fish Wildl. Serv., BioI. Rep. 90(1). 166 pp.

Sudman, S., M. Sirken, and C. Cowan. 1988. Sampling rare and

elusive populations. Science 240:991-996.

Thompson, 1.. 1980. Circle West wildlife monitoring study.

Second annual report. Mont. Dep. Nat. Resour., Circle

West Tech. Rep. 6. 10 pp.

Udvardy, M. 1981. An overview of grid-based atlas works in

ornithology. Pages 103-109 in C. J. Ralph and J. M. Scott,

cds. Estimating numbers of terrestrial birds. Stud. Avian

BioI. 6.
120 BIOLOGICAL REPORT 90(1)

Influence of Observer Effort on the Number ofIndividual

Birds Recorded on Christmas Bird Counts

by

Gregory S. Butcher

Comell Laboratol)' ofOmithology

159 Sapsucker Woods Road

1thaca, New York 14850

and

Charles E. McCulloch

Biometrics Unit

338 Warren Hall

Comell University

lthaca, New York 14850

ABSTRACT.-Ohserver effort affects results of all counts, surveys, and censuses.

Observer effort varies widely in Auduhon Christmas Bird Counts; therefore, we propose

a method to estimate for each species the number of individual hirds that would have

been reported (count) ifeffort had been standardized. We use party-hours or party-miles

to measure observer effort. We modeled the relation between count and effort using a

nonlinear regression of the form CT =eAXBYR C, where Cf = count, X = effort

measured in party-hours or party-miles, and YR = year. We chose this model because it

covers all three relations that we documented between count and effort: (1) count

increases linearly with effort (B = 1); (2) effort has no apparent effect on count (B = 0);

and (3) count increases with effort, but at a steadily declining rate (O<B< 1). The year

variable is included in the model because the average observer effort per Christmas Bird

Count has increased steadily over the years. The nonlinear regression is used to obtain

the best estimate ofB (11) for each species. We use the estimate of B (11) and a standard

value (SV) for effort to modify all counts to a number that would be expected ifthe effort

had been the standard value, using the equation SCT =Cf( SV/X) Ii. Two case studies

suggested that accounting for effort may be more important for a species that achieves

high levels of abundance at some locations in some years (American black duck, Anas

mbripes) than for a species that rarely has more than one or two individuals per location

per year (northern goshawk, Accipiter gentilis).

Introduction

Observer effort affects results of all counts, surveys,

and censuses. Observer effort is strictly controlled in the

roadside surveys of the U.S. Fish and Wildlife Service:

the Woodcock Singing-grounds Survey, the Mourning

Dove Call-count Survey, and the Breeding Bird Survey.

Differences in observer effort may affect the results of

atlases and checklists, spot-mapping censuses, banding

studies, raptor migration counts, and counts of breeding

colonial waterbirds. We suggest a new method of

compensating for effects of observer effort in Christmas

Bird Counts (CBC's). This method relies on empirically

determining a species-specific relation between effort

and number of individual birds reported (count) and

using that relation to estimate the number ofindividuals

that would have been reported for a particular location

in a particular year if effort had been standardized. This
method issimilar to one used by Lack (1986) for the atlas

of wintering birds in Britain and Ireland.

Measures ofEffort on CBC's

It was realized from the beginning that effort

affected the number of individual birds recorded on

CBC's (Chapman 1900). Since the early 1950's, effort

on CBC's has been measured in party-hours and

party-miles; in addition, number of observers and

number of parties is recorded for each location each

year. A party is a group of birders traveling together

and remaining within sight or sound of each other.

Party-miles are the number of miles traveled by all

parties on a CBC; if six people travel 100 miles during

the day in two cars, the total number of party-miles is

100 if the cars travel together but 200 if the cars travel

separately. Party-hours are the number of hours spent

looking for birds. Like party-miles, party-hours are

accumulated by groups. If six people travel together

for 6 h, they accumulate 6 party-hours; if they split into

two groups for 2 h, they accumulate 4 party-hours.

Party-miles are heavily influenced by the amount of

time spent in a car and are most useful for species that

can be spotted from cars. Party-hours are more useful

for birds that are seen and identified on foot. The three

measures of effort (observers, party-hours, and

party-miles) were correlated for North America in

1963-83 (Pearson Product-Moment Correlations; N

21,430): observers and party-hours, 0.74;

party-hours and party-miles, 0.66; party-miles and

observers, 0.61. Because of these correlations, it may

not matter which index of effort is used for many

species. The use of party statistics rather than

individual statistics derives from the belief that the

number of individual birders in a group has less effect

on the probability of detecting individual birds than

does the number of groups (but see Preston 1979 and

Bart 1985). This assumption remains untested for

situations similar to CBC's; a promising avenue for

future research is to determine how much residual

variation in the number of birds reported can be

accounted for by the number of individuals per party

after the effects of party-hours and party-miles have

been determined.

Differences in the Kinds and Amounts

ofEffort on CBC's

There are four major ways of looking for birds on a

CBC: (1) self-propelled (e.g., foot, skis, bicycle,

snowshoes) during daylight; (2) motor-powered (e.g.,

car, boat, snowmobile, golfcart, airplane, helicopter)

during daylight; (3) stationary (at bird feeders) during

daylight; and (4) at night (both self-propelled and

AVIA,'i POPULATION TRENDS 121

motor-powered). The four kinds of effort are reported

separately for each CBC location each year;

unfortunately, the spccies and individuals seen are not

reported separately. Thus, it is difficult to study the

relative abundance of feeder birds or night birds

because, for these groups, individuals are recorded by

more than one method. Consequently, it is difficult to

determine the effect of varying effort on the number of

individuals recorded. Therefore, we recommend that

birds seen at feeders or at night be reported separately

from birds seen by observers traveling by car or foot

during the day.

In addition to the four kinds of effort, wide differences

exist in amount of effort both within counts among years

and among counts within years. The differences exist for

all three measures of effort; for example, differences

occur in party-hours both within and between years (Figs.

1 and 2). In addition, there are important changes in

party-hours within count locations (Table). With these

differences in effort, especially within years and over

many years, it is vital to consider whether or not effort is

affecting the number of individuals reported and, if

necessary, to correct for effort.

Possible Relations Between Count and

Effort

Traditionally, number of individuals reported on a

CBC is divided by party-hours or party-miles to create

an index of abundance for each species at each location

each year (Bock and Root 1981). This adjustment

assumes not only that the number of individuals

reported increases linearly with effort, but also that the

line representing the relation between count and effort

passesthrough the origin (i.e., no effort implies no birds

counted). This is exactly the relation we found for the

red-tailed hawk (Buteo jomoicellsis) using data from the

48 contiguous States and southern Canada for 3 years in

the early 1970's and 3 years in the early 1980's: a highl~

significant linear relation between count and effort (B

[slope coefficient) = 0.007; t = 32.0; P = 0.0001) and a

nonsignificant y-intercept coefficient (-1.30; t = -1.6;

P = 0.108; Fig. 3).

However, Bock and Root (1981) noted that there

should be some species for which effort has no effect

on number of individuals reported. These are

primarily species that occur in habitats that are small

and can be covered in a few hours or less and that are

known to be good habitats for birds. Presumably,

count organizers send participants to these areas even

if there is very little time to count birds. Because the

habitat covers a small area, addition of more hours and

miles will not increase coverage in that habitat; thus,

few if any individuals of the species preferring that

habitat will be added.
122 BIOLOGICAL REPORT 90(1)

200

180

160

140

C

:::>

0 120

u

-0 100

....

Q.)

.D 80 E

:::>

:z: 60

40

20

0

j

I~~~~~~

Fig. 1. Distribution of party-hours at

individual Christmas Bird Count

locations in North America during the

winter of 1982-83 showing variation in a

single CBC season.

<10 20 40 60 80 100 120 140 160 180 >200

Party-hours

lOO

Fig. 2. Mean number of party-hours spent

on Christmas Bird Counts in North

America during the winter of 1962-63

through the winter of 1982-83. The

mean was calculated for each year to

remove the effect of variation among

locations within years, which was shown

in Fig. 1. Note that the between-year

variability for this period is much less

than the within-year variability for the

winter of 1982-83 (Fig. 1). Changes

through time within CDC locations are

shown in the Table.

c

:::>

o

u

....

Q.)

c..

'"

....

:::>

o

..c

I

Z-

....

o

c..

-o

c

o

Q.)

::;;

80

60

40

20

63 65 67 69 71 73 75

Year

77 79 81 83

Table. Challge ill party-hours withill particular COUllt locatiolls (1950-82).a

Locations No statistically Locations

Time Number of with increase significant change with decrease

span

b locations (%) (%) (%)

8-10 92 28 68 '4

14-24 302 52 44 4

25-32 363 76 22 2

, A simple linear regression was performed on party-hours for each location. If the slope showed a statistically significant (P < 0.05) increase

or decrease. then the location was assigned to the increase or decrease category; otherwise the location was assigned to the category entitled

"No statistically significant change."

The number of years between the first CDC at a particularlocation and the most recent one. For most locations. all interveningyears provided

data for the regression.
AVIAN POPULATION TRENDS 123

80

70

0 60 ::J

-0

:~-0 50 c

-0

'- 40

..0'"

E

::J 30 C

C

0

'" 20 ::;;:

10

0

Fig. 3. The relation between count and

effort for red-tailed hawks (Bllteo

jamaicensis). The data points represent

means of counts by to-party-mile

intervals using data for all North

American locations with at least one

red-tailed hawk during 1 of 3 years in

the early 1970's or 3 years in the early

1980's. The statistical test (see text)

used count and party-miles for each

location for each of the 6 years.

o 100 200 300 400 500 600 700 800 900

Po rty-rn iles

The bald eagle (Haliacctlls ICllcoccp/zallls; Fig. 4)

and mallard (Anas platyr/zync/zos; Fig. 5) seem to be

good examples of species that lack a positive relation

between count and effort. There was no significant

relation between count and effort in thc bald cagle

(B = -0.063; 0.05 < P < 0.10), using data from the

48 contiguous States for 3 years in the early 1970's and

3 years in the early 1980's (Fig. 4). In fact, therc was a

significant negative relation betwecn count and effort

in the mallard (B = -6.61; t = -3.66; P = 0.0003; Fig.

5), including data from thc United States and southern

Canada east of thc 100th meridian during 1949-50

through 1981-82.

9

8

7 o

::J

-0

:~ 6

-0

C

The negative relation between count and effort in the

mallard and bald eagle may be due to the "national

wildlife refuge effect": Good wetlands areas are highly

valued by birders, thus an area with good wetlands may

be chosen as a CBC site even ifit is far from urban areas.

Such aCBC will havc relatively low effort bccause of its

distance from population centers. In contrast, areas near

population centcrs will be chosen as CBC sites and will

have high effort even if they lack wetlands.

Finally, Preston (1979) and Johnson (1981) noted that

there should be cases whcre count increases with effort,

but not linearly with a zero intercept. There are two

general cases: (1) a linear increase, but nonzero intercept,

Fig. 4. Relation between count and

effort in thc bald cagle (llaliaeetm

lellcocephallls).

_ 5

o

'-

..0'" 4

E

::J

C 3

c

o

::::;: '" 2

x x

o 50 100 150 200 250

Po rty- hou rs

300 350
124 BIOLOGICAL REPORT 90(1)

x x x

Fig. 5. Relation between count and effort in

the mallard (Anas platyrhynchos).

2POO T

i

1,800 i

(Il 1,600 t 0 i

::::J

1,400 + x

-0

:~

;,cc j -0

C

-0

'- ',000

Q)

-D

::::J

E 800 r c

C

0

Q) cco1 ::::;; 400

I

200 1

0

0 50 100

x x x

150 200 250

Party-hours

300 350

and (2) the rate of increac;e in numbers declines as effort

increases. Speciesthatshow these patterns might be more

abundant in small high-quality habitats, but should alc;o

be found in lower numbers in other parts of a count circle.

A complicating factor is the relation between count

and effort varies with a number of variablcs: habitats,

weather, latitude, the relative abundance and

conspicuousness of a species in an area, and so forth.

Therefore, every species on every count circle on every

count day should have a unique relation between count

and effort. Unfortunately, we only have one data point

for each count circle on each count day. It is possible to

control for the effects of a few of these variablcs, but it

is not practical to include all of them as variables in an

equation used to calculate their effect on the number of

individual birds reported.

We are especially concerned with two variables that

affect the relation between the number of individual

birds reported and the amount of effort: time and

human population density. Christmas Bird Counts are

clustered near human population centers, and CBC's

near high human population densities have, on average,

higher effort than CBC's far from human population

centers. Many bird species avoid areas of high human

population density, for a variety of reasons. Thus, any

study of the effect of effort on the number of birds

reported on CBC's should control for human

population density. Unfortunately, we do not have any

measure of human population density for CBC

locations, and we must defer this aspect for future study.

The amount of effort on CBC's hasincreased through

time (Fig. 2). The rate of increase was even greater

before 1963. Thus, for any species with a time trend in

population, time must be included as a continuous

variable in the equation to properly measure the effect

of effort on the number of individual birds reported.

Thus, we have included year as a continuous variable in

this study.

Recommended Steps in Analysis of

CBCData

Despite the multitude of factors affecting the

relation between count and effort for each species, we

assumed for this study that the relation between count

and effort is similar at each location within the species'

range. We used the following procedure to evaluate

specific rc1ations between count and effort:

1. We included all data for all years from all count

locations where the species had ever been recorded, and

we excluded all count locations where the species had

never been recorded. Thus, we included many zeros

from locations from which a species had been reported

only once or only a few timesin 21 years, but we excluded

many zeros from locations where the species had never

been recorded. The decision of which zeros to include

or exclude greatly affects the rdation of count versus

party-hours or party-miles. However, the only quantity

estimated from the rdation that is used in our suggested

method is the coefficient that governs the slope on the

log scale (B, in equation (1)). This coefficient is only

slightly affected by the inclusion of many zeros.

2. Next, we did a linear regression of count versus

effort, including a year term. If the slope was

nonsignificant, zero, or negative, we could not be certain

that effort had no effect on the counts, but we could say

that other factors were so important that they

overwhelmed the influence of effort. For these cases
AVIAJ"l POPULATION TRENDS 125

(equation (1)), it follows that

A * =In(Cf) - (Bln(X)) - (Cln(YR )) . (3)

(2) SCT = Cf(sv/x) Ii

(based on equation (1)) where A* is any value that

makes the equation fit through the point (CT, X) using

theBdetermined for the species. Because

where Scf = standard count, CT = count, SV =

standard value (SO in party-hours, 300 in party-miles) and

X, YR, andBare as before. We derived equation (2) from

equation (1) in the following manner: Because we are

interested in what a standard count (SCT) would be if

there had been a standard effort (50 party-hours or 300

party-miles), we used the equation

when effort increases linearly. Thus, B must remain

between zero and one. If B = 0, then count does not

increase with effort; if B = 1, then count increases

linearly with effort. For all intermediate values, count

increases with effort, but the returns diminish with

increasing effort.

This model is the equivalent of a linear log-log

regression, but is estimated on the original scale. The

advantage of estimating the relation on the original

scale is that it avoids the problems with zero counts.

We used PROC NUN in the SAS statistical package

(SAS Institute, Inc., Cary, North Carolina) on the

Cornell University mainframe computer. We began

with a log-log regression to get starting values for the

slope and intercept parameters, and then we used

these starting values to run PROC NUN.

Theoretically, we should have used a weighted

nonlinear regression so that variance was weighted

proportionally to the mean; however, weighted

nonlinear regression proved impractical because of its

great expense with large datasets. Unweighted

nonlinear regression seemed to provide a reasonable

approximation of the results to be expected from the

weighted procedure.

From both the log-log regression and the nonlinear

model we estimated parameters A, B, and C. We used

the estimate ofB (B) to transform all counts (CT) into

standard counts (SCT) that would be expected if effort

were equal. To use Bin this way, we had to allow A to

vary for each count (CT) and effort (X). We used the

following equations:

(1)

Nonlinear Regression of Count Versus

Effort

(e.g., bald eagle and mallard; Figs. 4 and 5), number of

individual birds reported for eaeh CBC is

recommended for use in studies of relative abundance.

3. Alternatively, if the slope of count versus effort was

positive and significantly different from zero, then we

corrected the actual number of individuals reported to a

number that would be expected if the effort were

standard. The following sections are devoted to specific

recommendations for this procedure.

4. The next decision was to determine a measure of

standard effort. We chose a number near the mean of the

effort on all counts in the area and during the period

chosen for the study. Choosing a larger number requires

too much extrapolation, and choosing a smaller number

might minimize differences in counts. For the 21 years

between the 1962--63 count and 1982-83 count, the mean

for party-hourswas about 50 and the mean for party-miles

was about 300; these values are used in this paper.

5. The next decision was to determine how to model the

relation between count and effort.We have already shown

that division by party-hours or party-miles is appropriate

for some species, while actual counts suffice for others;

our challenge was to come up with an appropriate

treatment for other situations.

where CT = count (the number of individuals),A is the

intercept on the log-log scale (and the initial slope on

the original scale), X = effort expressed as party-hours

or party-miles, B is the slope on the log-log scale (and

the change in slope on the original scale) for the effect

of effort, YR = year, and C is the slope on the log-log

scale for the year effect. We chose this model because it

covers all realistic cases where count increases with

effort. The model has the valuable feature that the curve

passes through the origin, reflecting the fact that no

birds can be reported if no effort has been expended.

We cannot conceive of a realistic situation in which the

number of birds reported for an individual CBC would

decrease with increasing effort, nor one in which the

number of birds reported would increase exponentially

Initially, we tried log-log regression, but the results

were not completely satisfactory, primarily because

data for many species include a large number of zero

counts, and log-log regression requires adding a

constant to all zero counts. When there is a large

number of zero counts, adding the constant distorts

the relation between count and effort.

Our second approach was to fit nonlinear models

of the form
126 BIOLOGICAL REPORT 90(1)

By replacing equation (3) in the modified version of

equation (1), it follows that

This equation is mathematically identical to equation

(2), which is the most convenient form for calculating

standard counts. The process of deriving standard

counts from actual counts is shown graphically in Fig. 6.

These standard counts are now ready to be used to

compare relative abundance through space and time.

Po rty-hours

Fig. 6. Graphical representation of our proposed method of

creating standard counts from actual counts using a

species-specific relation between count and effort. The

solid line represents the empirically determined relation

between count and effort. The dotted line represents a line

with the same "change in slope" (D), but with a different

"initial slope" (A*) that allows the line to pass through the

actual point for a particular CDC result. The standard

count is the y-value for the point where the dotted line

intercepts the x-value for the standard count (50

party-hours).

III

o

::J

"0

> SCT

"0

c

-o CT .....

Q)

.0

E

::J

Z

----- --1------""" !//

APH 50(SV)

Two Case Studies: Northern Goshawk

and American Black Duck

The northern goshawk (Accipter gelltilis) is one

species for which the relation between count and effort

does not fit either of the two classic models (Fig. 7). In

Fig. 7 we show the relation between count and effort in

four different ways: (1) using the nonlinear model; (2)

and (3) using two log-log regressions (with two different

constants added to zeros before taking the logarithm);

and (4) using means from the real data. Our visual

interpretation of this figure is that the nonlinear model

follows the actual data more closely than do the two

log-log models. Our decision on the relation between

count and effort affects our impression of the

population dynamics of northern goshawks from 1963

through 1983 (Fig. 8). Using raw, unmodified counts,

the northern goshawk population in the most recent 3

years (1980---81 through 1982-83) was 292% of the

population in the earliest 3 years (1962-63 through

1964-65). When dividing the raw counts by party-miles,

the early 1980's goshawk index was 236% of the early

1960's goshawk index. Finally, by calculating standard

counts as recommended earlier, the 1980's goshawk

index was 243% of the 1%0'5 goshawk index.

The American black duck (Alias nlbripes) is another

species with a nonlinear relation between count and

Fig. 7. Four ways to express the relation of

count versus effort for the northern

goshawk (Accipiter gentilis). A represents a

log-log regression of count versus effort

when a constant of0.5 is added to all counts.

B represents the nonlinear regression. C

represents a log-log regression when 1/6 is

added to all counts. D connects all the

means of counts per lO-party-mile groups.

E

-'='"

t:

o

:z:

.8

200 400 600

Par-ty-rniles

800

/

/

i

I

i

i

I

1,000
7 T

I C .6 i 0

0 I u

.... 5j '"

0..

.!!!

0 4+ OJ I

-0 I :~

'j -0

C

-0

.... I

'" 2 r ..0 !

E

OJ

z: .1 +

I

0 1

NGNLIN ,,' 1'0030

;,.,/

. /

,/(COUNT I . '-, +.0025 E

.:' I NGINOX ! :J- I j' .0020 ! :// 0..

/'v~/ I

......./\~~~~ ,~) t.0015 -5

1\.;/ VJ~ "_! I

r

! \ty\/\i/I )1,0010 C

! \.-"'".. / (; I ~/ .... ! .0005 E

OJ

z:

I

63 65 67 69 71 73 75 77 79 81 83

Year

AVIAN POPULATION TRENDS 127

Fig. 8. Three indices of population levels

for the northern goshawk (Accipiter

gentilis). None of the three indices

include any modifications for changes

in the locations represented in different

years. The high countsin the last 2years

represent major irruptions from more

northern wintering areas. The solid line

represents the relation as determined

from the raw, unmodified counts

(COUNT; using the scale on the left

side of the figure). The dotted line

connects data calculated from counts

that were standardized using the

nonlinear regression (NGNLIN; using

the left scale). The dashed line

represents population changes

calculated from counts that were

standardized by dividing the raw counts

by party-miles (NGINDX; using the

right scale).

effort (Fig. 9). Again, we feel that the nonlinear model

provides a closer fit to the actual data than do the two

log-log models; however, none of the models follow the

sharp decline in count at very high values for

party-hours. This deciine may be an artifact of a

correlation between high effort on CBC's and urban

environments that may be unsuitable for black ducks

(see Possible Relations Between Count and Effort). For

black ducks, we calculated a population trend index by

dividing a population estimate that used 3 years of data

from the early 1980's (1979-80 through 1981-82) by a

population estimate that used 3 years of data from the

early 1950's (1949-50 through 1951-52). Using raw,

unmodified counts, the black duck index was 41%.

Using raw counts divided by party-hours, the index was

20%. Using standard counts as recommended earlier,

the index was 28%.

For goshawks, the trend estimate from 1963 through

1983 is essentially the same no matter how the counts

are treated (Fig. 8); however, for black ducks, there is a

dramatic difference in the perceived severity of the

decline from 1950 through 1983 depending on how the

counts are modified (Fig. 10). The major reason

differences in treatments did not affect our estimate of

trends for the goshawk seems to be the large number of

zeros for that species (6,900 of the 9,100 data points in

the study). In most count areas, goshawk counts varied

from zero to one (Fig. 11), and this change produced

most ofthe noted changes in abundance. Modifications

of these small numbers had little effect. On the other

hand, some black duck counts were in the tens of

thousands (Fig. 12). Modifications ofthese numbers had

a large effect on our estimate ofthe population dynamics

of black ducks.

1,200

1,100 i\

1\ C

(/l 1,000 I" A .::f I \ ()

1\ I ::J 900 \

0 II I \

.::f I I I

() 800 I I I

0 I I

CD I I \ 700 \ ..... I 0 Ii \

L 600 .-1 \ \

OJ I \ ..0 I \

E 500 I \

::J I \ C \ 400 OJ J \

OJ \

0 300 ;1 \ L ;/ \ OJ ;1~

> \ « 200 D

100

100 200 300 400

Party-hours

Fig. 9. Four ways to express the relation of count versus effort

for the American black duck (Anas I7Ibripes).A represents

a log-log regression of count versus effort when a constant

of 0.5 is added to all counts. B represents the nonlinear

regression. C represents a log-log regression when 1/6 is

added to all counts. D connects all the means of counts per

to-party-hour groups.
128 BIOLOGICAL REPORT 90(1)

25

.... 15 '"

CL

o

:::J

-0

:~ 10 -0

C

Fig. 10. Three indices of population levels

for the American black duck (Anas

mbripes). None of the three indices

include any modifications for changes

in the locations represented in different

years. The solid line represents the

relation as determined from the raw,

unmodified counts (BLKDUC; using

the scale on the left side of the figure).

The dotted line connects data

calculated from counts that were

standardized using the nonlinear

regression (BDNLIN; using the left

scale). The dashed line represents

population changes calculated from

counts that were standardized by

dividing the raw counts by party-miles

(BDINDX; using the right scale).

'0

....

'".Q

E

:::J

:z

....

:::J

o

..c 20 I

.z:-

....

o

CL

5

BDNLlN

-........-------- ............. --- 8DINDX I

i ~~~~-~~~~-~~~~-~~~~~~---+-+I a

800

700 C

0

0

u 600

....

'"CL 500

en

0

:::J

-0 400

:~

-0

C

300

'0

....

'".Q 200

E

:::J

:z 100

a 50 52 54 56 58 60 62 64 66 68 70 72 74 76 78 80 82

Year

Conclusions

1. Birds observed at bird feeders and birds observed

at night should be reported and analyzed separately

from birds observed during the day by moving parties.

2. Analysts should determine the empirical relation

between count and effort to determine if counts need

to be adjusted for effort.

3. Because effort on eBC's has increased with time,

a year variable should be included when determining

the relation between count and effort.

4. If counts increase with effort, then nonlinear

regression should be used to estimate the slope of the

relation between count and efforLRaw counts should

be converted to standard counts that would be

expected with standard effort.

5. After conversion (or a decision that conversion is

unnecessary), standard counts can be used for any

studies that compare relative abundance through time

or space.

5.000

6.901 counts hod 0 Northern Goshawks 4,000

1.600

1.400

1.200

C

:::J

o 1.000

U

-0 800

....

.0'" 600 E

::J

Z 400

200

2 3 4 5+

Number of individuals

Fig. 11. Distribution ofcounts ofnorthern goshawks(Accipiter

genti/is; from 3 years in the early 1970's and 3 years in the

early 1980's). The maximum count in these 6 years was 16.

-'"C

::J

8 3.(JCJo

-o

....

Q)

.0 2000

E

::J

Z

1.000

o o 1 -9 10-99 100-999 1,000+

Number of individuals

Fig. 12. Distribution ofcounts ofAmerican black ducks (Anas

mbripes; during 1949-50 through 1981--82). The peak

count during this period was 36,000.
Acknowledgments

The Christmas Bird Count Population Project at

Cornell has been generously supported by J. KixMiller,

G. W. Knight, N. B. Delavan Foundation, E. I. du Pont

de Nemours and Company, Exxon Corporation, FMC

Corporation, IBM Corporation, Johnson Foundation

(Trust), MARS Inc., Merck Family Fund, Williams

Companies Foundation, Inc., and many others. We

thank J. Lowe for his extensive work with the CBC data

and his preparation of the figures, and T. Engstrom, J.

Sauer, and an anonymous reviewer who significantly

improved the manuscript. In addition, we thank the

many CBC volunteers who collected, recorded, and

edited the data; National Audubon Society and staff of

American Birds, who coordinate the CBC and publish

AVIAN POPULATION TRENDS 129

the results; and C. E. Bock and J. Shipman, who

provided us with computerized CBC data.

Literature Cited

Bart, J. 1985. Causes of recording errors in singing bird

surveys. Wilson Bull. 97:161-172.

Bock, C. E., and T. L. Root. 1981. The Christmas Bird Count

and avian ecology. Pages 17-23 in C. J. Ralph and J. M.

Scott, eds. Estimating numbers of terrestrial birds. Stud.

Avian BioI. 6.

Johnson, D. H. 1981. Summarizing remarks: estimating

relative abundance, Part 1. Pages 58-59 in C. J. Ralph and

J. M. Scott, eds. Estimating numbers of terrestrial hirds.

Stud. Avian BioI. 6.

Lack, P., editor. 1986. The atlas of wintering birds in Britain

and Ireland. T. & A. D. Poyser, calton, Staffordshire, U.K.

Preston, F. W. 1979. The invisible birds. Ecology 60:451-454.
130 BIOLOGICAL REPORT 90(1)

Population Trends in the Least Tern (Sterna antillarum) from

Maine to Virginia: 1975-1986

by

R. Todd Engstrom, Gregory S. Butcher, and James D. Lowe

Conzell Laborato')' ofOnzithology

159 SapsZlcker Woods Road

Ithaca, New York 14850

ABSTRACT.-The least tern (Stenza antillanlln) is endangered in 4 of the 10 coastal

States from Maine through Virginia. We used data from the C-Olonial Bird Register to

evaluate trends in the abundance of least terns from 1975 to 1986 for State populations

and for the entire region.

The total number of breeding pairs of least terns recorded for the region increased

during the study period from 6,740 in 1977 to 9,341 in 1986. Both 1977 and 1986 were

years in which extensive counts were made in the region. Direct comparison of annual

totals should be made with caution, because these totals can be influenced by which

colonies are censused in a year. Only 132 of 311 colony sites were censused for 6 or more

years during the study period.

The methods used to count terns varied among years and among colonies. Comparing

aerial to ground counts was of particular concern because more aerial counts were done

in the early part of the census period. We used correction factors to make aerial and

ground counts comparable.

We used weighted regression to obtain an estimate of population change over the

12-year study period. The regional trend is a combination of the slopes of individual

colonies weighted by colony size and an index ofsampling effort. The regression analysis,

originally developed to analyze Breeding Bird Survey data, can accommodate missing

data for some years at some sites. The results of the regression analysis of the regional

population of least terns indicated a 5.0% increase peryear over the 12-yearstudy period

(P =0.10), even though colonies with negative slopes outnumber colonies with positive

slopes 166 to 106. Our results indicated that the weight for colony size contributes to the

overall increasc.

These results were encouraging, but the increases may have been caused in part by

improved and expanded efforts to monitor the species rather than true population

changes. The least tern deserves continued attention because its preferred breeding

habitat, coastal sandy beaches, will continue to be subjected to heavy pressure from

urbanization and human recreation.

Introduction

In the Unitcd States, thc least tern (Stenz a

antillanll1l) usually nests in colonies along the Atlantic

and Gulf coasts from Maine to Tcxas, on the California

coast from San Francisco Bay southward, and in

freshwatcr habitats from North Dakota to Texas

(Amcrican Ornithologists' Union 1983; Carrckcr

1985). Least tern populations wcre severely depleted

in Massachusctts (Nisbet 1973) and possibly

climinated from New York (Bull 1964) in the late 19th

century by thc millinery industry. After legal

protection, the least tcrn has repopulatcd New York

(Gochfeld 1973) and increased in other States where

it was diminished. It also has extended its range into

Maine (Drury 1973; Huntcr 1975).

Although populations of thc least tcrn in California

and the intcrior United Statcs havc been dcclarcd

endangered by thc U.S. Fish and Wildlife Servicc (U.S.

Dcpartment ofthc Intcrior 1973; U.S. Fish and Wildlife

Service 1985), the castcrn population is not classified as

endangered, thrcatcned, or of management concern

(U.S. Fish and Wildlife Service 1987) by the Fcderal

Government. Thc least tern has no special State

classification in Connccticut, Delaware, and Virginia.

However, the least tcrn is considered endangered in
Maine, New Hampshire, New York, and New Jersey,

threatened in Rhode Island and Maryland, and a species

of special concern in Massachusetts. No \cast tern

colonies have been documented for New Hampshire

during the study period, although adults have been

sighted several times during the breeding season

(Gavutis, in preparation).

Concerns over the threat of offshore oil drilling

(Erwin 1979), the loss of breeding habitat caused by

urbanization, and disturbance from human recreation

(Gochfeld 1983; Kotliar and Burger 1986) have spurred

considerable effort to obtain quantitative data on

populations of \cast terns in the eastern United States

(Gochfcld 1973; Duffy 1977; Buckley and Buck\cy 1980;

Kress et al. 1983; Burger 1984; Spendclow and Patton

1988). Currently, \cast tern populations are monitored

annuaIly in many States (e.g., MacLean et al. 1988).

Downing (1973) and Fisk (1975) reported on a

cooperative survey of least terns along the Atlantic

coast, but the first intensive systematic effort to count

least terns occurred in 1977 as part of a survey and

census of all coastal colonial waterbirds from Maine to

Virginia (Erwin and Korschgen 1979).

The amount of concern by States suggested to us a

need to examine trends in populations of least terns for

the coastal States from Maine through Virginia. We

analyzed trends for least terns in this region for the

period 1975-86 using a weighted regression technique

(Geissler and Noon 1981). We also examined the

relation between regression slope, weighting factors,

and sign of the regression slope for each colony.

Methods

The Colonial Bird Register Data Bank

In 1975, the Cornell Laboratory of Ornithology and

the National Audubon Society jointly initiated the

Colonial Bird Register (CBR). The CBR is a

computerized bank of information on species

composition and abundance in colonies ofseabirds and

wading birds (McCrimmon 1978). The colony location,

date of visit to the colony, physical characteristics ofthe

colony site, and census method also are recorded on

standardized forms (Engstrom 1990). The survey and

census of colonial waterbirdsin thenortheastern United

Statesin 1977, coordinated by the U.S. Fish and Wildlife

Service (Erwin and Korschgen 1979), constitutes the

foundation of CBR data that were used in this study.

Most CBR least tern data were contributed by

organized efforts, such as the annual survey of Long

Island colonial waterbirds (MacLcan et al. 1988) and

various State-sponsored surveys.

AVIAJIi POPULATION TRENDS 131

Definition ofa Colony

Least terns may breed at different colony sites in

different years (Nisbet 1973) or move to new sites if

breeding is disrupted within a year; hence, it is difficult

to define a colony. Short-distance relocations of

colonies over years can result in a proliferation of colony

names, especiaIly when surveys are not centrally

organized. We consulted with the observers who

contributed data to the CBR to check colony site names,

locations, and number of \cast terns. We used colonies

as defined by contributors, although we pooled

subcolonies as named by contributors in some cases.

Counts in Virginia were complicated. Instead of

giving eaeh colony site its own name, sites in a locality

(usuaIly an island) were given a number as they were

encountered in a year. The number for the colony site

one year may be used for a different site the next. For

this reason, we pooled numbered colonies within

localities, such as islands, for each year.

Estimation ofthe Number ofBreeding Pairs

To make data comparab\c among colonies, we

standardized all census results to the number of

breeding pairs. Census results were reported to the

CBR as the number of adults, the number of nests, or

both on some CBR forms. If nests were counted, we

assumed that the number of breeding pairs equalled the

number of nests. However, a variety of correction

factors have been proposed to convert the number of

adults observed in a colony area into an estimate of the

number of breeding pairs. We were especially

concerned about the correction factor for estimating the

number of pairs from aerial counts of adults, because

aerial counts were done more frequently in the early

part of our study period. Underestimating the number

of pairs from aerial estimates in the early years would

bias trends upward.

In the U.S. Fish and Wildlife Service survey and

census of colonial waterbird colonies in the coastal

northeastern United States in 1977 (Erwin and

Korschgen 1979), aerial counts of least terns were

considered to be equiva\cnt to the number of breeding

pairs based on a regression of aerial estimates of adults

versus the number of nests (Erwin 1979:49). Buckley

and Buck\cy (1980) and Duffy (1977) used a correction

factor of 0.9 to convert counts of adults to the number

of breeding pairs. Thompson (1982), after an intensive

study, suggested a correction factor of 0.6 for the least

terns along the coast of Texas (adult count X 0.6

number of pairs).

In our analysis, if aerial techniques were used to

census a colony, we assumed a 1:1 ratio for the number

of adults and the numher of breeding pairs (Erwin's 1.0
11

where

the number of years when counts were made,

the year (Geissler and Noon 1981; Robbins

et al. 1986).

132 BIOLOGICAL REPORT 90(1)

correction factor) and used records from May and June

only to eliminate possible prebreeding aggregations or

late-season colony relocations. For ground counts of

adults, we multiplied the number of adults seen by a

correction factor of 0.5. We felt that this was a

conservative estimate of the number of pairs. If a nest

count and an adult count were provided on the same

form, we used the larger of the two estimates. Y

[(11 - 1)LO'-Y)2J 0.5

11

(3)

TrendAnalysis

We used a weighted linear regression procedure

(Geissler and Noon 1981) to estimate population trends

for the least tern from 1975 to 1986. The strength of the

Geissler and Noon (1981) weighted regression method,

designed specifically to estimate trend., for BreedingBird

Survey data, is that it can be used even when data are not

available for some locations in some years. For example,

the number of values used in a regression for a colony in

thisstudy is between 2 and 12. The technique distinguishes

between zeros and missing values. This is especially

important when coverage is uneven among years.

This procedure includes the following steps. The

estimated number of breeding pairs in a colony for a

year, plus a constant of 1.0 to adjust for zeros, is

transformed to its logarithm. The number of pairs is

regressed against year for each colony having 2 years or

more of data, using linear regression (GLM procedure;

SAS Institute Inc. 1982). The individual colony

regression slopes were back-transformed, then

combined to estimate a trend for each State and for the

northeastern region as a whole using

(1)

where

estimated trend on logarithmic scale for a

State or region,

br estimated trend for rth colony on logarithmic

scale [In(b»), and

Wr weight for the rth colony.

Before combining the regression values, they were

weighted according to the geometric mean of the

number of breeding pairs at each colony for all years,

Ninety percent, 95%, and 99% confidence intervals

were calculated for the State and regional trends by

bootstrap replications.

Results

Coverage, Colony Size, and Geographic

Distribution

In the study region, the consistency of count effort

for least tern colony sites varied among States and

years (Table 1; Fig. 1). For example, 8 of10 colony sites

in Maine were surveyed for 10 years from 1977 to 1986,

but in Maryland, 20 colony sites were censused for 3.2

years on average during the 12-year period, and only 2

of the colonies were censused for 6 years or more. The

most complete counts ofleast terns were conducted in

1977 and from 1982 to 1986 (Table 2) in most States.

In all States combined, 42% of all colonies (132/311)

were censused for 6 years or more during the

study period.

For all colonies over all years, colony size ranged

from 1 to 850 breeding pairs, with a median of25 pairs

(11 = 1307). This median colony size may be slightly

biased upward because large colonies tended to be

censused more frequently than small colonies in some

years. The median colony size ranged from 19 to 30

pairs per colony among years (Table 3). In the 1984

survey, New York reported the largest number of

breeding pairs of least terns of all States in the region

(Table 3; Fig. 2).

The annual total of breeding pairs indicates an

increase from 1977 to 1986 from 6,740 to 9,341

(Table 3), when efforts to visit all colony sites were

comparable. Note that more colonies were censused

by air in 1977 than in 1986.

(2) Linear Trend'!

where

C:V the count for the r th colony in the yth year and

there are p annual counts and an index of

sampling effort (the sample variance of the

observed years)

The rate of change in the regional population of

least terns, as estimated by the Geissler and Noon

(1981) weighted regression, is a 5.0% per year increase

(P = 0.10; Table 4). The overall trend is a 74%

increase over the 12-year period (P = 0.10). In trends

for individual States, Virginia (P = 0.05; 9.7% per
AVIA1" POPULATION TRENDS 133

Table 1. The consistency ofcensusing effortfor the least tem (Sterna antil/arum) by State during ]975-86for records

in the Colonial Bird Register (CBR). This includes colony sites where no tems were found.

Number of The number of

Total number census years colonies censused

State of colonies percolonl 2: 6 years

Maine 10 8.1 8

Massachusetts 75 5.8 35

Rhode Island 13 5.8 8

Connecticut 13 6.1 6

New York 93 4.9 34

New Jersey 49 5.3 19

Delaware 15 4.3 10

Maryland 20 3.2 2

Virginia 23 5.6 10

Total 311 5.2 132

a The total number of censuses of all colonies for the 12-year period divided by the total numher of colonies.

50

Fig. 1. The number of colonies and the classes of census-years

(n = 311). The 39 colonies that were only censused for 1

year during the study period were omitted from the

regression analysis.

year) and Rhode Island (P = 0.10; 18.6% per year;

Table 4) showed positive trends significantly different

from zero. Maryland was the only State that had a

negative regression slope (P = 0.05; -12.2% per year).

The negative regression slope for Maryland (Table

4) seems to be contradicted by an apparent increase

indicated by the changes in the annual total number of

terns counted (Table 3). This contradiction was

caused by the uneven coverage during the study period

(Tables 1 and 2). The most thorough coverage in

Maryland occurred in 1985 and 1986, when more than

twice as many colony sites were censused than during

any of the previous 10 years. The annual totals in

Maryland were influenced by which colonies were

censused in a particular year.

Analysis ofWeighting Factors

Colonies with negative regression slopes

outnumber colonies with positive regression slopes in

seven of nine States (Table 5). The overall ratio of

negative to positive slopes is 1.6:1 (n = 272). This

result seems to be the opposite of what one would

expect with the positive regional trend of 5.0% per

year. Three factors affect the regional trend:

regression slopes, weights for colony size (the

geometric mean of counts over all years), and weights

for the index ofsampling effort (sample variance ofthe

observed years). We analyzed these factors by dividing

the colonies into two groups: those with positive slopes

and those with negative slopes.

The absolute values ofslopes of decreasing colonies

(negative slopes) are significantly larger than slopes of

increasing colonies (Mann-Whitney test; P = 0.06).

Therefore, the magnitude of the slopes does not

contribute to the observed positive slope for the

region. The index of sampling effort also is not

significantly different between increasing and

decreasing colonies (Mann-Whitney test; P = 0.52).

However, size weights for colonies with positive slopes

are significantly larger than the weights given to

colonies with negative slopes (Mann-Whitney test;

P = 0.0003; Table 5) and larger colonies are given

more weight in the regression; therefore, the overall

trend is positive.

2 :5 4 5 6 7 8 9 10 11 12

Number of years censused

I

10

40

'"

<1> 'c 30

u

-0'" 20

E

:::J

:z
Table 2. The III/mber ofcolonies ofleast tems (Sterna antillarum) included in the Colonial Bird Register by State by year. Survey effort varies substantially among .... States among years; therefore, the annual totals should be compared with caution. '$:

Year

1975 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 t:l:l State 1976 0

t""

Maine 8 7 7 8 8 8 8 8 9 10 g

Massachusetts 10 32 44 34 40 38 36 36 34 48 43 40 ?i

Rhode Island 6 7 I 7 11 II 12 12 12

Connecticut 4 6 7 9 3 7 8 12 9 10 ::0

67 69 83 78 45 t!l New York 9 27 40 1 26 4 6 -=0 New Jersey 25 28 34 29 24 21 18 23 22 10 14 lI:l

.., Delaware I I 5 6 7 8 7 7 7 3 5 7 8 Maryland I I 5 4 6 1 3 4 2 6 16 15 ,.-..

.... Virginia 16 21 12 12 10 10 \I 10 II 8 7 '--'

Total number of

colonies 25 114 165 98 127 103 101 169 172 205 190 160

Table 3. 711e number ofbreeding pairs ofleast tems (Sterna antillarum) included in the Colonial Bird Register by State by year. Survey effort varies substantially

among States amongyears; therefore, the all/lllal totals should be compared with caution.

Year

State 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986

Maine 65 88 70 70 95 39 63 88 105 124

Massachusetts 511 1,354 1,527 1,434 2,009 2,040 1,856 1,812 2,199 2,400 2,321 2,245

Rhode Island 18 45 ° 87 237 248 162 146 127

Connecticut 134 169 121 127 155 426 172 372 820 1,108

New York 266 913 2,136 120 1,439 540 412 1,447 2,126 2,742 2,462 2,911

New Jersey 740 1,277 1,296 1,035 1,057 1,028 1,230 1,160 1,122 679 1,079

Delaware ° 50 148 495 721 560 717 780 600 362 496 480

Maryland 112 4 212 273 269 1 61 207 135 556 718 553

Virginia 858 1,209 136 206 398 934 293 1,104 966 378 714

Total number of

breeding pairs 1,023 4,106 6,740 3,842 5,749 4,793 5,345 6,471 7,807 8,770 8,125 9,341

Median colony size 25 19 30 26 29 30 27 28 27 23 24 30
AVIAN POPULATION TRENDS 135

Discussion

Monitoring Lea'lt Terns

Fig. 2. The percentage of least tern colonies in regions of

the northeastern United States as calculated from 1984

CBR data. The squares along the coast mark colonies

that contained 100 or more pairs of least terns in 1984.

The black line in Maine represents the northern limit of

breeding least terns.

Several elements are essential to develop a system for

monitoring least terns. First,standard counting methods

need to be established and used consistently over time

(Drury 1980). Second, a working definition of a colony

needs to be adopted and regularly employed. Third, the

best time of the year for counts should be defined for

different regions, although this may vary somewhat from

year to year. Finally, census techniques and data

management should be coordinated among States to

ensure that results wiII be comparab\c and compatible.

Programs of individual States that entail cooperation

among professionals and volunteers, such as the Long

Island Colonial Waterbird Survey (MacLean et al.

1988), could be the foundation for a broader regional or

national monitoring effort.

Correction factors strongly affect the results. The

difference between the 1,786 pairs of least terns in New

Jersey in 1977 (Erwin and Korschgen 1979), 942

individuals (Kane and Farrar 1977; Burger 1984), and

1,277 pairs (our results; Table 3) were caused by

different correction factors used for ground and aerial

counts for the same primary data.

A variety of counting techniques were used to collect

CBR data for the \cast tern. Different levels of precision

and biases associated with the various techniques could

invalidate the use ofCBR data for trend analysis (Erwin

1980). For this reason, we tried to correct for systematic

biases that might affect trend analysis. For examp\c, we

have pointed out that counts were made from the air

more frequently in 1977 than in later years. This could

potentiaIIy bias the trend upward if aerial counts tend to

be underestimates. To adjust for this potential bias, we

converted numbers of individuals into numbers of pairs

with a correction factor; however, it's possible that this

factor may not fully compensate for birds missed from

the air. If so, at \cast some of the reported increase in

least terns may be an artifact of the more complete

ground counts in the latter part of the study.

(% of 8770 nesting pairs)

• colony with 100 pairs

·21%

Least terns are difficult to count (Nisbet 1973)

because they may shift colony sites within a season and

among seasons. Faithfulness to nesting sites, however,

may be common as long as the sites remain

undisturbed (Gochfeld 1983; Burger 1984; Atwood

and Massey 1988). Regardless of the difficulties, the

species should be monitored within a weII-organized

regional program for the foIIowing reasons: (1) the

least tern is considered either threatened or

endangered by most of the coastal States from Maine

to Virginia; (2) least tern colony instability makes

surveys over large areas especiaIIy important; and (3)

much of the breeding habitat of least terns along the

Atlantic coastline is under extreme pressure from

human population growth (ParneII et al. 1988).

Population Trend'l

According to our analysis of CBR data, regression

slopes are positive for least tern populations in every

State in the region except for Maryland, although only

the positive trends in Rhode Island and Virginia are

significantly different from zero (Table 4). The

regional population shows a statisticaIIy significant

5.0% increase per year (P = 0.10). Increases in least

terns have been reported recently in other studies in

New York (GochfcId 1973; Buckley and Buck\cy 1980)

and New Jersey (Burger 1984): the States having the

first and third highest number ofleast terns in the study

region (Table 3).

The support from other studies strengthens our

conclusion that least terns have increased in the study
136 BIOLOGICAL REPORT 90(1)

Table 4. Population trends estimated with the Geissler and Noon (1981) weighted regression model for the least tem

(Sterna antillarum) from 1975 to 1986. TIle trends are expressed as percentage change. Confidence levels are:

* = 90% and ** = 95%.

Change Change

per year (1975--86)

State Number of coloniesa (%) (%)

Maine 9 2.9 37

Massachusetts 63 1.7 21

Rhode Island 13 18.6* 554*

Connecticut 12 6.5 100

New York 87 3.5 46

New Jersey 43 4.5 63

Delaware 9 4.3 58

Maryland 18 -12.2** -76**

Virginia 18 9.7** 178**

Total 272 5.0* 74*

3 The number of colonies with 2 years of data.

region. Of course, the results of any regression analysis

will depend on which years are included. When we

started this project, we analyzed only 10 years (1975-84)

of CBR data for the region. The results of this initial

analysis showed a nonsignificant increase (P > 0.10;

3.7% per year; 43% for the lO-year period). The sharp

increase from 1985 to 1986 probably tipped the increase

detected by regression to weak statistical significance.

When a population is likely to vary widely among

years, we may want more than a single trend value to

characterize changes within population over 12 years.

Annual indices show year-to-year population variability

and are an important supplement to the overall

population trend. Unfortunately, reliable annual indices

were not available for this study. Graphical techniques

to display annual changes or longer trends in relation to

geographical location and the pattern of abundance

would be desirable. For example, it might be possible to

superimpose over a map of a region, shades of color

indicating population change and a three-dimensional

contour of abundance. This type of exploratory analysis

would help researchers to define regions of rapid

change without having to rely on preestablished

groupings of the data.

We analyzed the components of the estimated trend

(slope, geometric mean of colony size, and the index of

Table 5. Median ofthe geometric means ofleast tcm (Stcma antillamm) colonics having positivc and ncgative

rcgrcssion slopcsfrom 1975 to 1986.

Colonies with

positive slope

Colonies with

negative slope

State

Maine

Massachusetts

Connecticut

Rhode Island

New York

New Jersey

Delaware

Maryland

Virginia

Region

a Significant at P < 0.05 in the Mann-Whitney test.

Median

17.9

21.0

12.4

4.5

10.3

72.3

121.8

51.9

24.3

14.9

N

3

29

5

7

40

8

2

3

9

106

Median

1.2

15.3

2.6"

2.0

5.2"

3.9"

25.0

6.0

21.1

6.9"

N

6

34

7

6

47

35

7

15

9

166
sampling effort) to assess how the components might

have affected the trend. The result that colonies having

positive slopes were significantly larger than those

having negative slopes (Tab\c 5) indicated that changes

in larger colonies controlled the regional trend. This

result has management implications for the least tern, if

smaller colonies are consolidating into larger colonies,

as suggested by Burger (1984) for New Jersey.

Concentration of the population into larger colonies at

fewer sites could make the population more susceptible

to catastrophe.

Identifying biological factors that are causing the

positive trend in the population over a 12-year period is

beyond the scope of this study. Increases in immigration,

productivity, and overwinter survival could all contribute

to the trend. Our data do not provide a direct index of

productivity. However, we feel that our analysis showed

that the population ofleast terns from Maine to Virginia

is increasing as indicated by the results of the weighted

regression analysis and the increase in total numbers of

breeding pairs from 1977 to the period 1982-86, when

efforts were made to survey all colonies of \cast terns.

While this result is encouraging, the recent success of

least terns may be attributab\c to the amount of

management effort expended in recent years (Jackson

and Jackson 1985; Kotliar and Burger 1986). Like the

endangered piping plover (Cltaradrills melodlls), the least

tern deserves sustained attention because the coastal

beaches they require for nesting will continue to be used

heavily for human recreation and development.

Acknowledgments

We thank the many people who took the time to count

least terns and to fill in CBR data forms. Our principal

contacts by State were: R. M. Duclos, F. Sib\Cy, and

J. Zickefoose (Connecticut); P. Jahn and J. Thomas

(Delaware); J. Arbuckle and A. Hutchinson (Maine);

D. Brinker and J. McKearnan (Maryland); B. Bloclget,

R. Forster, and T. French (Massachusetts); D. Jenkins

(New Jersey); T. Litwin, D. MacLean, D. Petersen, and

M. Scheibel (New York); C. Raithel (Rhode Island);

B. Williams and K. Terwilliger (Virginia).

We thank the staff of Bird Population Studies at the

Cornell Laboratory ofOrnithology, especially S. Sutcliffe,

and the many students and volunteers, E. Bachman in

particular, who have coded data into the computer and

double-checked colony names. J. L. Atwood, E. H. Burtt,

Jr., R. M. Erwin,J.M. Hagen,S. W. Kress,R.J.O'Connor,

C. R. Smith, and J. A. Spendelow made many useful

comments on the manuscript.

This report was funded in part by the U.S. Fish and

Wildlife Service under Cooperative Agreement Number

14-16-0009-85-957.

AVIAi'l POPULATION TRENDS 137

Literature Cited

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

American birds. 6th cd. American Ornithologists' Union,

Washington, D.C. 877 pp.

Atwood, J. L., and B. W. Massey. 1988. Site fidelity of least

terns in California. Condor 90:389-394.

Buckley, P. A.,and F. G. Buckley. 1980. Population and colony

site trends of Long Island waterbirds for five years in the

mid-1970's. Trans. Linn. Soc. N.Y. 9:23-56.

Bull, J. 1964. Birds of the New York area. Dover Publishing

Inc., New York. 540 pp.

Burger, J. 1984. Colony stability in least terns (Sterna

antillamm). Condor 86:61-67.

Carreker, R. G. 1985. Habitat suitability index models: least

tern. U.S. Fish Wildl. Serv., BioI. Rep. 82(10.103). 29 pp.

Downing, R. L. 1973. A preliminary nesting survey of least

terns and black skimmers in the cast. Am. Birds

27:946--949.

Drury, W. H. 1973. Population changes in New England

seabirds. Bird-Banding 44:267-313.

Drury, W. H. 1980. Coastal surveys-northeast and

northwest. Trans. Linn. Soc. N.Y. 9:57-75.

Duffy, D. 1977. Breeding populations of terns and skimmers

on Long Island Sound and Eastern Long Island, 1972-75.

Proc. Linn. Soc. N.Y. 73:1-48.

Engstrom, R. T. 1990. Evaluation of the Colonial Bird

Register. Pages 26-32 in J. R. Sauer and S. Droege, eds.

Survey designs and statistical methods for the estimation of

avian population trends. U.S. Fish Wildl. Serv., BioI. Rep.

90(1). 166 pp.

Erwin, R. M. 1979. Coastal waterbird colonies: Cape

Elizabeth, Maine to Virginia. U.S. Fish Wild\. Serv.,

FWS/OBS-79/l0. 212 pp.

Erwin, R. M. 1980. Censusing waterbird colonies: some

sampling experiments. Trans. Linn. Soc. N.Y. 9:77-84.

Erwin, R. M., and C. Korschgen. 1979. Coastal waterbird

colonies: Maine to Virginia, 1977-an atlasshowing colony

locations and species composition. U.S. Fish Wildl. Serv.,

FWS/OBS·79-08. 647 pp.

Fisk, E. J. 1975. Least tern: beleaguered, opportunistic and

roof-nesting. Am. Birds 29: 15-16.

Gavutis, G. W., Jr. 1989. Least tern (Sterna antillamm). In

C. Foss, D. Miller, and E. Miller, eds. The atlas of breeding

birds of New Hampshire. (In preparation)

Geissler, P. H., and B. R. Noon. 1981. Estimates of avian

population trends from the North American Breeding Bird

Survey. Pages 42-51 in C. J. Ralph and J. M. Scott, eds.

Estimating numbers of terrestrial birds. Stud. Avian BioI. 6.

Gochfeld, M. 1973. Recent status of the least tern, Sterna

albifrons, on the south shore of western Long Island.

Engelhardtia 6:1-9.

Gochfeld, M. 1983. Colony site selection by least terns: physical

attributes ofsites. Colon. Waterbirds 6:205-213.

Hunter, M. L. 1975. Least tern breeding range extension in

Maine. Auk 92:143-145.

Jackson, J. A., and B. J. Schardien Jackson. 1985. Status,

dispersion, and population changes of the least tern in

coastal Mississippi. Colon. Waterbirds 8:54-62.

Kane, R., and R. B. Farrar. 1977. 1977 Coastal Colonial Bird

Survey of New Jersey. Occas. Pap. 131, N.J. Audubon

3:188-194.

Kotliar, N. B., and J. Burger. 1986. Colony site selection and

abandonment by least terns Sterna antilla11lm in New

Jersey. BioI. Conserv. 37:1-22.
138 BIOLOGICAL REPORT 90(1)

Kress, S. W., E. H. Weinstein, and 1. C. T. Nisbet. 1983. The

status of tern populations in northeastern United States

and adjacent Canada. Colon. Waterhirds 6:84-106.

MacLean, D. c., T. S. Litwin, A. Ducey-Ortiz, and R. A. Lent.

1988. 1987 Long Island Colonial Watcrbird and Piping

Plover Survey. Seatuck Research Program, Cornell

Laboratory of Ornithology, Islip, N.Y. 174 pp.

McCrimmon, D. 1978. The collection, management, and

exchange of information on colonially nesting birds. Pages

187-1% in A. Sprunt, IV, J. Ogden, and S. Winckler, eds.

Wading birds. Nat\. Audubon Soc. Res. Rep. 7, New York.

Nisbet, 1. C. T. 1973. Terns in Massachusetts: Present numbers

and historical changes. Bird-Banding 44:27-55.

Parnell,J. F., D. G.Ainley, H. B1okpoel, B. Cain, T. W. Custer,

J. L. Dusi, S. Kress, J. A. Kushlan, W. E. Southern, L. E.

Stenzel, and B. C. Thompson. 1988. Colonial waterbird

management in North America. Colon. Waterbirds

11:129-169.

Robbins, C. S., D. Bystrak, and P. II. Geissler. 1986. The

Breeding Bird Survey: its first fifteen years, 1%5-1979.

U.S. Fish Wild\.Serv., Resour. Puh\. IS). 196 pp.

SAS Institute Inc. 1982. SAS user's guide: basics, 1982 ed.

Cary, North Carolina: SAS Inst. Inc. 923 pp.

Spendelow, J. A., and S. R. Patton. 1988. -National atlas of

coastal waterbird colonies in the contiguous United States:

1976-82. U.S. Fish Wild\. Serv., Bio\. Rep. 88(5). x +

326pp.

Thompson, B. C. 1982. Distribution, colony Characteristics, and

population status of least terns on the Texas coast. Ph.D.

dissertation, Texas A&M University, College Station.

U.S. Department of the Interior. 1973. Threatened wildlife in

the United States. Bureau of Sport Fisheries and Wildlife,

Washington, D.C. Resour. Pub\. 114.

U.S. Fish and Wildlife Service. 1985. Endangered and

threatened wildlife and plants: interior population of the

least tern determined to be endangered. 50 CFR Part

17:21784-21792.

U.S. Fish and Wildlife Service. 1987. Migratory nongame birds

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Unpublished administrative report, Office of Migratory

Bird Management, Washington, D.C. 58 pp.
AVIAl',"" POPULATION TRENDS 139

Trend Analyses for Raptor Nesting Productivity: An example

with Peregrine Falcon Data

by

Paul H. Geissler, Mark R. Fuller, and Lynne S. MeAllisterl

U.S. Fish a1ld Wildlife Service

Patuxe1lt Wildlife Research Ce1lter

Laurel, Mal)'la1ld 20708

ABSTRACI-Reproductive data can be used in determining the status of raptars and

setting research and management priorities. Many surveysto find raptor nests or to assess

raptor reproduction are conducted by government agencies, groups, and individuals.

Survey methods include a variety of field procedures that often are not standardized or

based on a statistical design. Appropriate analytical methods need to be identified or

developed for these surveys. In this paper, we describe statistical methods used to explore

the suitability of data gathered during a raptor nest survey for estimating trends in

productivity.

Introduction

Data from surveys of peregrine falcons (Falco

peregri1lus) in west-central Greenland during 1972-85

(Mattox and Scegar 1988) were examined to assess

temporal ehanges in peregrine productivity. The

measure of productivity in a given year was the number

of banding-age young (about 3 weeks old) per nest.

Several features of the survey make it difficult to

identify appropriate analyses: some sites (cliffs) at

which nesting occurred in earlier years were not

checked every year; more search effort was invested in

later years; nest searches were concentrated in the best

peregrine habitat and in a core study area; and most

sites were included in the data set only after being

found occupied. An occupied nest was inhabited by a

single peregrine or a pair. These survey characteristics

are not uncommon for other peregrine surveys or

those for other raptor species (Fuller and Mosher

1981; Cade et al. 1988). We examined several different

methods for analyzing these nest survey data.

Ipresent address: Natural and Applied Sciences, University of

Wisconsin. Green Bay. Wis. 54311.

Methods

TrendAnalysi.'i

We used variations of the route-regression method

(Geisslcr 1984) to estimate production trends. This

method makes comparisons only among observations

made on the same nest sites in different years,

minimizing the adverse effects of not checking some

sites every year and of concentrating effort on the best

habitat and in the later years. For example, analyzing the

total observed productivity would show an increase

simply because more nests were checked in later years.

The route-regression method estimates the variance

among sites instead of among years. The variance must

be estimated among nest sites because nest sites are the

sampling units, and production in successive years is not

independent.

The route regression method uses a linear regression

to estimate the rate of change (trend) in productivity for

each nest site. The nest site is analogous to the survey

route in Geissler (1984). These nest-regressions are

used to predict the production in the middlc year of the

sampling period and the year after the middle year,

assuming the average rate of change for each site over

the period investigated occurred between these years.

These predicted productions are summed over nest sites
140 BIOLOGICAL REPORT 90(1)

to estimate the total production in each of those years.

The production trend is then estimated as the ratio of

the total production in the year after the midyear to the

t.1tal production in the midyear. That ratio estimates the

average annual change in productivity for the

population during the period of interest.

We used two route-regression methods to estimate

the average change (trend) for a nest site. An additive

linear regression estimates the rate of change in terms

ofthe number of young Iledged per year, using the slope

of an ordinary linear regression. A multiplicative model

estimates the percent change per year using a linear

regression and log transformation of the number of

young produced:

where y = number of young produced, X = year, e

error term, a = intercept, and b = trend. Using

logarithms, this model becomes a linear regression:

In(v + 0.5) = In(a) + In(b)X + In(e).

Both route-regression methods can be fit by the

ordinary Icast squares approach or the Theil

nonparametric method (see Hollander and Wolfe

1973:209). Theil's method uses the median of the trend

estimates calculated for every possible pair of years.

Although use ofthe median minimizes the effect of a few

discordant observations, it inhibits detection of a sharp

change in productivity.

Paired-t Comparisons

Paired t-tests were used to compare characteristics

within individual nest sites. We tested for changes in the

population after the ban ofDDT in 1972. Peregrines are

long-lived and usually do not breed until they are more

tbn 2 years old (Newton and Mearns 1988). The year

in which effects of the DDT ban might be apparent is

unknown; therefore, we compared two arbitrary

groupings of the data. The period from 1972 to 1978 was

compared with that from 1979 to 1985, and the period

from 1972 to 1979 was compared with that from 1980 to

1985. Groups ofyears within the 14-yearsurvey included

only nest cliffs known in both periods. For each group,

paired t-tests were conducted using nests as

observations with the following variables: (1) number of

young produced (banded) for all productive nest sites;

(2) number of young produced for all sites with

peregrine pairs; (3) number of young produced for all

nest sites occupied by a peregrine; and (4) number

produced for all nest cliffs surveyed in both periods.

Analysis ofVariance

We used analysis of variance to assess whether some

years or nests were consistently more productive than

others. Years were crossed with nests and the

interaction was used as the error term. Analyses were

conducted for nests occupied by peregrines, nests

checked, and productive nests. The dependent variable

was the number of banding-age young per nest.

Results

None of the trend analyses yielded significant results

(P > 0.05; Table 1). Paired-t comparisons between

1972-78 and 1979-85 indicated a significant increase

between periods of0.58 birds per nest (P < 0.05), based

on the number of young produced in all nests that were

checked. Paired-t comparisons between 1972 and 1979

and 1980-85 showed significant increases (P < 0.05) of

0.57 birds per nest for the number of young produced in

all productive nests, 0.70 birds per nest for the number

of young produced in all nests with adult pairs, and 0.64

birds per nest for the number of young produced in all

checked nest sites.

Some nest cliffs were consistently more productive

than others, based on analysis of variance (P < 0.01).

However, tests using analysis of variance showed no

significant relation between years and numbers of

banding-age young per nest.

Discussion

Testing for changes in productivity of raptor nests

can be approached in several ways. Trend analysis can

be used to estimate the average rate of production

change over the whole period. Alternatively, one can use

a paired t-test to determine if the mean production in

individual nests is the same in different periods. The

trend analysis and the paired t-test use nest sites as

independent replicates. Years are not independent

because the same peregrines might return to the same

nest site for several years (Ambrose and Riddle 1988;

Mattox and Seegar 1988; Newton and Mearns 1988),

and their decision to return might be inlluenced by their

success the previous year. An analysis of variance tests

the more general null hypothesis that there are no

differences among years, but requires the more

restrictive assumption that both years and sites are

independent. If they are not independent, the

interaction term (assumed to be zero) will inllate the

error term, making the tests conservative.

Consequently, our analysis of variance indicated no

significant changes when, in fact, changes might have

occurred. Together, these tests and estimates provide a

comprehensive picture of ehanges through time.
Table 1. Results ofroute regression methods to test for trends in productivity at peregrine (Falco peregrinus) nests in westem Greenland.

Mean Standard

annual change Significance d.o.f. error 95% c.l.

Multiplicative model (% change per year)

Parametric trend analysis

Productivity per occupied nest 2.69 n.s. 22 3.3 -3.2,9.5

Productivity per productive nest 1.33 n.s. 22 1.0 -0.4,3.3

Non-parametric trend analysis

Productivity per occupied nest -3.99 n.s. 21 4.6 -12.1,5.9

Productivity per productive nest --4.45 n.s. 18 3.0 -10.6, 1.2

Additive model (nestlings per year)

Parametric trend analysis

Productivity per occupied nest 0.040 n.s. 22 0.06 -0.1,0.1

Productivity per productive nest 0.047 n.s. 22 0.03 0.0,0.1

::!

E

o

z

-3

lI:l

t!l

Z

o

rIJ

.....

.j>.

.....
142 BIOLOGICAL REPORT 90(1)

Either an additive or a multiplicative model can be

used to estimate production trends with the routeregression technique. Multiplicative models often are

used to estimate population trends because the

population in one year is likely to be a multiple of the

population in the previous year, due to the multiplicative

effects of birth and death. Thus the annual increase or

decrease is expressed as a percentage of the current

population. However, production is not likely to be a

multiple of the previous year's production at the same

nest because production depends more on external

factors than on previous productivity. Consequently, an

additive model, expressing the annual increase in terms

of the number of young produced per year, should be

more appropriate.

The nest search procedures for most North

American peregrine surveys (Cade et al. 1988:27-121)

make the trend estimates difficult to interpret. Usually

a nest site is identified as such only after it is first

determined to be occupied by birds. After a site has

been identified, it is usually checked each year, and

biologists can easily document the abandonment of a

nest site, thus biasing estimates towards decreases.

When a potential nest site is found, it often is difficult to

determine ifthe site was previously occupied. Thus most

nest site records start with an occupied nest. To

minimize this bias, we did not use the record for the year

a nest site was identified, so the records for a site could

start with either an occupied or an unoccupied nest.

The increasing search effort in the recent years of the

Greenland Peregrine Falcon Survey (Mattox and Seegar

1988) complicates the study of trends. With the trend

analysis, a separate trend is estimated for each nest site.

H many nest sites have observations only for the last few

years, the trends will disproportionately reflect changes

that occurred in later years. This effect is counteracted by

the route regression method, in which each nest site is

weighted by the amount of information available (inverse

of the relative variance). Initially, many nests were

discovered because they were near the base camp at

Sondre Stromfjord. These nests were checked more

consistently because they were known and easier to reach.

The influence of these nests also is increased by the

weighting. An unweighted analysis would give all nests

equal influenee and would emphasize the trends in later

years. One cannot simultaneously adjust for both time and

location biases. We chose to use a weighted analysis

adjusting for time biases.

Conclusions

These peregrine nest production data were difficult

to interpret because of sampling biases. We have

discussed ways of minimizing the effects of these biases

and provided estimates ofthe change in production over

time. Field procedures also should be modified to

minimize future biases, although some problems remain

because of the difficult terrain. The need for random

sampling of nest sites cannot be overemphasized.

Route-regression analysis seems suitable for testing

changes in data gathered by the procedures used in

raptor nest surveys such as the Greenland Peregrine

Falcon Survey. The route regression with weighted .

analysis that adjusts for time, provides some

consideration for the survey results that include more

data in recent years. Also, we believe the additive model

is more appropriate than the multiplicative one, to test

for trends in productivity or yearly occupancy.

If there is concern about the accuracy of the

observations, Theil's regression could be used instead

of the ordinary least squares regression, in effect,

reducing the influence of aberrant points. However, if

these points are correct, discarding them makes the

estimates insensitive to abrupt changes in production.

For that reason, robust regressions such as Theil's

probably should not be used for monitoring trends. The

wildlife manager might better use the parametric

method of least squares and risk falsely concluding that

a change has occurred (type I error) rather than missing

an actual change (particularly in a declining population)

because a conservative test was used (e.g., Theil's).

H managers might err on the side of concluding that

a change has occurred, the additional tests we

conducted can be used to further evaluate possible

changes. Like route-regression results, ANOVA results

suggested no change among years. However, the paired

(-tests indicated an increase in the number of nestlings

per nest, based on all nests during the 1979-85 and the

1980-85 periods. Additional increases were suggested

(e.g., more nestlings per productive nests) for the

1980-85 period. Biologists might conclude, from all the

tests, that while there probably has been little change in

the entire 14-year survey data, it is possible that in the

most recent years an increase in the average number of

nestlings per nest has occurred.

Acknowledgments

We thank W. G. Mattox and W. S. Seegar for

providing data and information about the Greenland

Peregrine Falcon Survey. W. S. Seegar and F. P. Ward

provided support for our analyses. We thankJ. Hatfield,

J. R. Sauer, and K. Titus for reviewing our manuscript.

Literature Cited

Ambrose, R. E., and K. E. Riddle. 1988. Population, dispersal,

and migration of Alaska peregrines. Pages 677-{)84 in T. J.

Cade, J. II. Enderson, C. G. Thelander, and C. M. White,

eds. Peregrine falcons: their management and recovery.

The Peregrine Fund, Inc., Boise, Idaho.
Cade, T. J., J. E. Enderson, C. G. Thelander, and C. M. White,

editors. 1988. Peregrine falcon populations: their

management and recovery. The Peregrine Fund, Inc.

Boise, Idaho. 949 pp.

Fuller, M. R., and J. A. Mosher. 1981. Methods of detecting

and counting raptors: a review. Pages 235-246 in C. J.

Ralph and J. M. Scott, eds. Estimating the numbers of

terrestrial birds. Stud. Avian BioI. 6.

Geissler, P. H. 1984. Estimation of animal population trends

and annual indices from a survey of call counts or other

indicators. Proc. Am. Stat. A.,soc. Sect. Surv. Res. Methods

1984:472-477.

AVIA.l\; POPULATION TRENDS 143

Hollander, M., and D. A. Wolfe. 1973. Nonparametric

statistical methods. John Wiley & Sons, New York. 503 pp.

Mattox, W. G., and W. S. Seegar. 1988. The Greenland

Peregrine Falcon Survey, 1972-1985, with emphasis on

recent population status. Pages 27-36 in T. J. Cade, J. H.

Enderson, C. G. Thelander, and C. M. White, eds.

Peregrine falcons: their management and recovery. The

Peregrine Fund, Inc., Boise, Idaho.

Newton, I., and R. Mearns. 1988. Population ecology of

peregrines in south Scotland. Pages 651-665 in T. J. Cade,

J. H. Enderson, C. G. Thelander, and C. M. White, eds.

Peregrine falcons: their management and recovery. The

Peregrine Fund, Inc., Roise, Idaho.
144 BIOLOGICAL REPORT 90(1)

Current Thinking on United Kingdom Bird Monitoring

by

Raymond J. O'Connor

Departmellt ofWildlife

College ofForest Resources

Ulliversity ofMaille

OrollO, Maille 04469

ABSTRACT.-This paper describes current thinking about the monitoring of landbird

populations in Britain. It is based on recent reviews conducted by the British Trust for

Ornithology (BTO) reappraising the purposes and cost effectiveness of its monitoring

programs, particularly the Common Birds Census (CBC) and its analogues. In practice,

monitoring has not been an operational objective of the Common Birds Census, largely

because of the difficulties of interpreting the meaning of a single annual change in the

population of a common species and because of incomplete habitat surveillance,

particularly affecting scarce species. In addition, the abscnee of a stratified design for

these schemes has prevented the usc of their results as a national index. Financial limits

prevent expansion of the Common Birds Census to meet thesc difficulties. Despite these

problems, the results have proved useful in documenting the effect of habitat

modifications on bird populations within significant habitats in Britain, particularly in the

case of agricultural intensification and its effects.

The practice of using a simple annual index of population change for each species and

of chaining these estimates over time, rather than resorting to an optimized statistical

treatment of the data, has made the results accessible to a wider audience and user base

than would otherwise have been the casco Even so, there is a case for strengthening the

power of the available samples by using more sophisticated analysis, with the Mountford

(1982) tcchnique, in particular, a prime candidate for routine usc. Homogeneity tests

available with this approach would also help control drift in sample composition, the risks

of which have increased in recent years with dwindling participation in the O:nsus.

The BTO is experimenting with some new census techniques, partiCUlarly with a

Constant EiTort Sites scheme based on standardized banding effort. This scheme offers

the promise of yielding productivity data not available with the CBC but tests of the

validity of assumptions made with this method have not yet been pUblished. The routine

monitoring of the abundance ofscarce species remail1s problematical, but the BTO's usc

of single species surveys offers a practical approach. The integration of these schemes is

discussed.

The usefulness of various products currently available from the BTO's monitoring

program is reviewed. The availability of adequate habitat data is particularly limiting, as

is the high cost of manual processing of census maps. The strengths and weaknesses of

the various types of analysis of monitoring data developed in Britain are evaluated and

contrasted with North American practices.
Introduction

Perceptions ofthe role of bird monitoring programs

in Great Britain have changed substantially over the

last decade, partly because of funding requirements

and partly because progress in the computerization of

existing data has allowed new research and promoted

recognition of new information needs. Here I review

the thinking behind these changes. The emphasis

throughout this paper is on current thinking about,

rather than on factual description of, United Kingdom

bird monitoring. The intention is to set out for North

American readers the rationale behind the changes

that have occurred in the light of nearly 30 years of

United Kingdom experience. Differences in culture,

attitudes, and local circumstances between North

America and Britain limit the extent to which I can

identify the relevance of this experience to the former,

and I leave it largely to the reader to recognize such

points. One might note in passing that there exists

scope for an analogous paper in a British journal, to

set out North American experience that could usefully

be adapted to United Kingdom schemes. Examples of

such points include the greater attention paid to

statistical design of sampling and the reliance on

refereed publication of methodological checks to

promote the corresponding rigor.

The Common Birds Census (CBe) is the British

Trust for Ornithology's (BTO) principal bird

monitoring program and is described elsewhere in this

volume. The BTO's Waterways Bird Survey (WBS) is

effectively a linear CBC conducted along rivers,

streams, and canals and is treated here as synonymous

with the CBe. It is described in more detail in papers by

Marchant (1980), Marchant and Hyde (1980), and

Taylor (1983a, 1986). The CBC has been the subject of

a number of technical reviews in recent years (Milner

and Hornby 1977; O'Connor and Marchant 1981;

O'Connor and Fuller 1984). These reviews have differed

somewhat in their purpose, the first two being

concerned principally with technical aspects ofthe CBC

scheme, while the third also considered policy issues

associated with the scheme. As a result of these reviews,

the BTO has revised some aspects of the CBC scheme

and has initiated exploratory work on other methods of

bird monitoring. This paper summarizes the resultant

thinking in relation to the monitoring of landbird

populations in Britain. The monitoring of shorebirds

and wildfowl is explicitly excluded from consideration.

Details of the major schemes associated with these

groups can be found in Prater (1981) and in Owen et al.

(1986), and in the annual reports of the Birds of

Estuaries Enquiry and the National Wildfowl Counts.

Certain other United Kingdom monitoring schemes are

reviewed in O'Connor (1985b).

AVIA..': POPULATION TRENDS 145

A companion paper (O'Connor 1990) described a

number of shortcomings of the CBC scheme as

recently practiced in Britdi~. Several of these

problems seem set to disappear ifthe most recent CBC

instructions are rigorously enforced. Changes of

particular importance in the latest revision are the

re-definition of target habitats, increased emphasis on

the problems that arise if the guidelines are breached,

increased standardization of the recording

requirements for difficult species, and more detailed

guidance as to the habitat recording required. In

addition, the revised rules expressly prohibit the use of

tape recorded birdsong as an aid to censusing. The

main differences between the current CBC rules and

those of the International Bird Census Committee

(IECe) are noted by Marchant (1985). I assume here,

unless critical to do otherwise, that the revised rules

will achieve theh intended purpose.

Objectives in Monitoring

Studying PopulaJion Trends

The CBC was started with the intention of providing

an early warning monitoring scheme in relation to

environmental changes in Britain. Changes in the level

of bird populations can, in principle, renect

environmental changes in either the wintering grounds

or the breeding grounds. Thus, Winstanley et al. (1974)

found a correlation between a decline in the late 1960's

in the numbers of whitethroats (Sylvia commulIis)

'breeding in Britain and a drought-induced reduction in

the extent of this species' wintering habitat in the

Sahelian Zone of Africa. Similarly, the use of persistent

organochlorine agrochemicals during the 1950's was

refle..::ted in declines in the numbers of such species as

peregrine (Falco peregrillus), sparrowhawk (Accipiter

lIisus) and stock dove (Columba oellas), and the

subsequent introduction of controls on the use of these

chemicals was accompanied by a recovery in the

numbers of these birds (Ratcliffe 1980; O'Connor and

Mead 1984; Newton 1986). More recently, the intensive

management of agricultural land in Britain, particularly

on arable crops, has been accompanied by regional

decreases in the numbers of a variety of species

(O'Connor and Shrubb 1986a; Potts 1986). On the other

hand, population levels have also rcflected changes

naturally associated with weather or climate

(Williamson 1975a; Jarvinen and Vaisanen 1977a,

1977b) and within the normal ability of bird populations

for recovery. Not all population changes, therefore,

constitute cause for concern.

The CBC results have, in practice, hardly been used

for explicit monitoring. Two factors contribute to this.

First, because the scheme focuses on common birds that
146 BIOLOGICAL REPORT 90(1)

are neither threatened nor harvested, the results do not

form the basis for routine management action. In this

the scheme's results differ criticaIly from, say, the

monitoring ofwildfowl harvests in the United States and

Canada. Second, the body of knowledge needed to

interpret the meaning of the results does not yet exist.

Three areas are of concern here: (1) the extent to which

the CBC results are representative of bird populations

in Britain; (2) the extent of normal ecological variation

in population numbers; and (3) the need for techniques

to control for the many potential biases that can arise in

the analysis of extensive data sets gathered without

defined sampling protocols. Each of these points needs

more detailed consideration here.

IdealIy, the CBC data set should be based on a

representative sample of the farmland and woodland

habitats in Britain. In practice, each plot is individualIy

chosen by the observer, and the censusing of each

continues only as long as the observer's wilIingness to do

the work involved lasts. Such self-selection ofsites has a

eorresponding potential for bias in sampling. What

checks have been conducted to date are, however,

relatively reassuring. One risk of great importance for

monitoring is that observers might sclect sites with

farming regimes especially favorable for birds. As noted

elsewhere in this volume, however, the composition of

the farmland CBC plots in fact reflectsfarming practices

and land class distribution over a large part of England

and has been consistent in this over the period 1962-81,

despite major shifts in agricultural management (Fuller

et al. 1985). Land classes poorly sampled are essentialIy

those remote from centers of population (e.g., in the

Highlands of Scotland and in Wales). Armed with this

knowledge, it is possible to define the regions in which

the CBC is valid as a total population monitor, at least

for farmland. The absence of the equivalent study for

the woodland CBC plots continues to be a significant

weakness of the scheme. This uncertainty is in marked

contrast to the controlled statistical design of the North

American Breeding Birds Survey, in which the location

ofsurvey routes has been carefully determined from the

outset. This to my mind reflects a major difference in

emphasis in monitoring between Britain and North

America, the former viewing the results as of interest,

the latter as a viable management tool.

Another possible concern about using the CBC

results for monitoring is that observers might abandon

sites where bird numbers have decreased, that is, once

they arc no longer as good for birding. O'Connor and

Fuller (1984) reported an analysis of the stated reasons

for giving up CBC plots and found no evidence ofsuch

an effect.

A second generic difficulty is that we know little of

the normal fluctuations of bird populations. Suppose,

for example, that the CBC scheme were to report a 25%

decrease in the number of the migratory sedge warbler

(Acrocepholus schoenobonus). What answer can be

offered to the question "So what?" After all, British

species can decrease by factors of 8-10 during a severe

winter, yet recover quickly, and migrant populations are

particularly labile. Hence a single year's decrease,

though statisticalIy significant, need not be biologically

significant. A sustained decrease, though, is likely to

reflect environmental change, as with the whitethroat

(Winstanley et al. 1974), peregrine falcon (Ratcliffe

1980), sparrowhawk (Newton 1986), and various

farmland species (O'Connor and Shrubb 1986a, 1986b).

In all of these studies, both the passage of time and a

variety of corroborative evidence were necessary to

establish the ecological significance of population

changes detected. For the CBC scheme, therefore, the

perceived need is for improved understanding of the

dynamics ofBritish bird populations, both over time and

over space, to improve the assessment of the biological

significance of population changes observed. A

declared policy aim for the CBC scheme is to conduct

periodic (about every 5 years) reviews of population

trends of species or groups of species in relation to

variation in climate. The detection of parallel

population trends in groups ofspecies sharing common

ecological requirements offers one powerful way of

linking bird numbers to identified environmental

changes and is expected to be a major feature ofthe new

program of analysis. Recent examples of this approach

can be found in O'Connor and Shrubb (1986a, 1986b).

This emphasis on general interest and ecological

analysis of the results is less evident in North American

use of ornithological data banks, with only a handful of

authors at all active in this ficld (e.g., Bock and Root

1981; Bock and Ricklefs 1983; Bock 1984; Brown and

Maurer 1987).

FinaIly, controls of some type are necessary if an

observed change in numbers is to be attributed to a

specified environmental factor, both because of the

ecological variation just considered and because of the

potential for biases other than those already

considered. Readily applicable techniques for this

have been possible only recently, foIlowing

computerization of the available data (O'Connor and

Shrubb 1986a). It is probably fair to claim that the CBC

data set has become so extensive that appropriate

controls for most possible biases can now be found

within its domain.

Concern over issues of bias led the BTO in 1984 to

switch publication ofthe annual CBC reports from the

Trust's scientific journal, Bird Study, to its newsletter,

BTO News. Historically, the CBC results have been

published in Bird Study. Given the normal standards

of scientific caution required of such a publication, a

difficulty arises over the variety of consistency checks
that ought to be made to ensure proper interpretation

of the results. Inevitably, such checks are repeated

each year and therefore come under editorial pressure

about the space they consume. In addition, a rather low

proportion ofthe volunteer participants in the scheme

are interested in the methodological checks. Over time

the result has been to minimize the effect of the

cautions and caveats that need to be presented, and

the CBC results have often been reported as though

they were national trends equally applicable to all

parts of Britain. Under the new system, publication in

the newsletter distributed to all BTO members ensures

the desired feedback. On the other hand, while it does

not preclude the results being misused (e.g., as though

they were national indices), the authority for such

misinterpretation is overtly the citation of a newsletter

article rather than a formal scientific journal.

Thoughtful users of the CBC results, however, will

draw on formal scientific papers in which all caveats

are properly evaluated. This problem seems to be

more pronounced in Britain, where the tradition of

annual reporting of recurrent results in scientific

journalsis well-established. In North Ameriea, routine

reporting of data is less prevalent: it is more often

relegated to the bulletins of the data gathering scheme

itself, if published at all (Engstrom 1989).

Despite the reservations mentioned previously, a

ease can be made for simple indexing of certain

populations. For migrant species, in particular, few

sources of information are available to track

environmental influences on their numbers in the

wintering grounds, and an annual index, however poor,

may be valuable for that reason alone. For resident

species, though, other means can probably identify

adverse environmental changes in Britain more

efficiently than can a CBC index for individual species.

Improving the Efficiency ofthe CBC

Scheme

How Many PlotsAre Needed?

Species Coverage

To a person familiar with the bird community of

farmland or woodland in Britain, the list of species

currently covered by CBC indices contains some

notable absentees, such as house sparrows (Passer

domes/ieus), rooks (Corvus fmgilegus), and reed

warblers (Aeroeephalus scirpaeeus). This is because

the CBC technique is based on the identification of

defended territories and fails to work satisfactorily

with colonial or semicolonial species. Many less

common species are absent from the list because they

occur on too few plots to meet the 3O-plot requirement

AVIA:"I POPULATION TRENDS 147

for the annual reporting of an index value. This

requirement has not been rigorously observed for all

species in recent years, because to do so would break

the continuity of published indices for the species

concerned. This problem has been brought about by

the falling samples of CBC plots in general and is

therefore an urgent issue to be addressed if the CBC

is to be stringently limited by available funding and

associated contractual requirements. The breach of

minimum sample size is possibly acceptable in the

short term, but in the longer term the practice risks

opening the sample to the random walk effect

described by Geissler and Noon (1981). Many of the

analyses undertaken in the last decade become quite

suspect if the underlying continuity depends on a very

few plots in particular link years.

Plot Distribution

An important issue for the cost-effectiveness of the

CBC scheme, therefore, is the size of sample needed

to detect a given change with statistical reliability.

Trends detected to date have been those for which the

available samples were actually adequate to reveal the

trend concerned despite the variability of data. What

is less wcll understood is the extent to which the

current pattern of data collection may limit the

detection of other trends. Sampling efficiency is in

general improved if effort is distributed in proportion

to the standard deviation of densities within region

(Snedecor and Cochran 1967), but CBC plots are in

practice more numerous in southern England, where

for many species densities are greatest (O'Connor and

Shrubb 1986a) and, by implication, most uniform.

Little thought has been given to optimizing CBC plot

distribution for statistical efficiency, despite the

constraint imposed on total plot numbers by the need

to have the map interpretation for the CBC scheme

undertaken by a professional staff. Ways of reducing

the major staff commitment involved have been

considered. One possibility would be to interpret only

some of the maps for the most common species while

doing all of the returns for the scarcest species. A

major difficulty here is that few observers would

accept that part of their returns should go unused.

Most observers already wish to include all species they

find on their plot, even though some of the species do

not lend themselves to CBC analysis. A deliberate

policy of selective analysis would therefore be

unacceptable. This issue exemplifies one of the

problems peculiar to an organization such as the BTO,

a conflict between what is objectively optimal on cost

grounds and what is acceptable to the volunteers

whose time and effort are as essential as funding. It is

unlikely that North America differs much from Britain

in this respect. .
148 BIOLOGICAL REPORT 90(1)

Index Computation

Another statistical issue of concern to the CBC

scheme is the precision of the CBC index. The current

procedure is to estimate a percentage change from year

to year and apply the confidence intervals for this

change to an index value prorated on the earlier year.

The procedure is undesirable for two reasons. First, as

pointed out by Geissler and Noon (1981), errors can

accumulate over time as successive index values are

chained together, even though each value is itself within

its year-on-year confidence band. This process is most

severe with small samples, but is less likely with the

larger samples of the CBC scheme. Thus, Moss (1985)

found an at most 25% deviation over a 20-year period,

on subjecting simulated data typical of the CBC to the

chaining process. He also found that the calculated

index values for individual species were usually highly

correlated with directly computed densitiesfor the same

year. However, for one species, the spotted flycatcher

(Muscicapa striata), he found a low correlation,

apparently because new plots entering the CBC scheme

in certain years held more spotted flycatchers than those

leaving the scheme. It is not known to what extent this

problem influences the CBC results, and it is not

currently controlled for. One must also note that the

Geissler and Noon (1981) random walk problem will

influence the calculation of regional or other subset

indices to a greater extent than it will the indices based

on all plots in the country.

The second weakness of the present CBC index

computation is that it uses only part of the available

information. Each index 11/ is calculated only from data

for plots censused both in year /I and in year /I - 1, yet a

significant number of plots censused in year /I may also

have been censused 2 years earlier (year /I - 2). Even

with a 20% replacement each year, some 64% of the

plots censused in year /I will also have been surveyed in

year /I - 2 (and 51% of them also in year /I - 3, and so

on). Hence, the index for year /I can be calculated either

by chaining the percentage changes between years /I - 2

and /I - 1 and between years /I - 1 and /I or by estimating

the percentage change between year /I - 2 and year /I

from the 64% subsample in common, in either case then

prorating on In _ 2' In general, these two routes to

estimating In will not yield identical values. Mountford

(1982,1985) has developed a method of combining such

multiple pathways to 11/' His method yields significant

reduction in the confidence interval about each index

value as compared to the standard CBC method, thus

providing greater statistical efficiency in the use of the

data. This greater efficiency, however, is won at the

expense of requiring assumptions to hold over longer

periods of time, and this has to be checked expressly,

using a test provided by Mountford. Preliminary studies

suggest that Mountford's procedure may be applicable

for periods of a decade or so at a time. If so, it could

readily be used in the anticipated periodie reviews of

long-term trends in the CBC data set without

necessitating annual recalculation of previously

published index values. This procedure has potential

relevance to, for example, the Breeding Bird Survey

(BBS), because it is markedly lcss computationally

intensive than the Geissler and Noon (1981) method,

without losing much of the statistical sophistication of

that procedure.

Despite these two problems, it must be noted that the

Bailey (1967) index obtained by the chaining of

year-on-year population changes has been proven a very

valuable tool in the evolution oflong-term studies ofbird

populations in Britain. Its very simplicity, coupled with

the robustness provided by reasonably large sample

sizes, has allowed many researchers lacking major

statistical expertise to make effective use of CBC data,

particularly in the trial-and-error approach needed in

research. In contrast, the BBS data have proved less

amenable to analysis: the sophistication of the Geissler

and Noon (1981) algorithm seems to have defeated

many of the less statistically adept researchers

attempting their own analysis, and has necessitated

greater reliance on the BBS personnel to provide the

requested trend data. Despite the generous provision of

such support (at a higher rate than the BTO has ever

been able to supply to its data users), such reliance

inevitably limits the flexibility of research efforts. I

suggest that this has been at least in part responsible for

the great emphasis in North America on the use of BBS

data for monitoring, while in Britain emphasis has fallen

more on research than on monitoring use of the

Common Birds Census data.

Additional Censusing

Milner and Hornby (1977) noted that the absence of

a national index of bird populations in Britain could be

met by increasing habitat coverage using the CBC

scheme or by the introduction of a system of point counts

in areas in which the CBC was inappropriate, for

example, because of low availability of participants

willing to make the time commitment to a CBC plot.

Trials ofthese techniques in British habitats showed that

they could provide the information required (Fuller

1983; Fuller and Langslow 1984), though the nced for

observer training could prove a limiting factor (Verner

1985). In practice, however, the CBC scheme is funded

under a contract for the provision of ornithological

services to the statutory Nature Conservancy Council,

and financial limits to the contract funding limit the

possibility of doing this. The Trust has thcrefore

accepted that the main strength of the CBC scheme lies

in lowland farmland and woodland (excluding
coniferous woodland), and that the scheme provides a

means of gathering annual census data in these habitats.

Here, lowland farmland includes all intensively used

agricultural land. Coniferous woodland is excluded

partly because the majority ofit lies in areas where BTO

members are sparsely distributed and partly because of

its low conservation value. Semi-natural broadleaved

woodland, on the other hand, is an extremely valuable

habitat in Britain, both because of its absolute scarcity

and because of an increasing trend to replace it with

softwoods, so information from the habitat would be

particularly valuable. The current expectation,

therefore, is that the program should concentrate on

these habitats.

Regional Indices

An alternative to comprehensive monitoring of all

habitats to achieve a national index of bird numbers is

to analyze the data on a regional or habitat basis. In

this way regional indices might be produced for areas

in which the CBC scheme is strong, while data would

manifestly not be available for other areas. This option

has been considered by the Trust, but has been ruled·

out on two grounds: (1) the cost ofstafftime to prepare

the necessary annual indices, and (2) the absence of

any systematic program for use of the resulting indices

as an early warning system. Both problems are

potentially soluble by resorting to computer use, but in

practice the return for undertaking this work annually

is unlikely to be worthwhile. On the other hand, the

long-term potential for comparing regional trends is

particularly valuable, especially if the regions are

chosen to reflect major classes of land use or are areas

undergoing significant changes in agricultural

practice. However, current policy for the CBC scheme

calls for greater effort to improve regional

representation within the farmland and woodland

samples, within the limit of about 100 plots for each

sample set by the funding for CBC staff. Success in

doing so would lend more of a national character to

the CBC index, but at the same time would make the

index incompatible with that of the earlier years in

which the samples were not distributed

representatively (Fuller et al. 1985). This perhaps

highlights the need for more systematic investigation

of the homogeneity of the CBC samples (especially of

the woodland sample), perhaps using techniques

offered by Mountford (1985) and Fuller et al. (1985).

These same issues arise in North America, where the

huge cost of routine processing of the Breeding Birds

Survey data has limited reflective publication on the

Survey's results to occasional publication (Robbins

et al. 1986).

AVIAN POPULATION TRENDS 149

Alternative Monitoring Method'!

The Constant Effort Sites Scheme

In recent years in the United Kingdom, considerable

interest has developed in the application of bird ringing

efforts to population monitoring. Investigations of the

possibilities of using annual ringing totals have generally

been unsatisfactory, with too many uncontrolled

intervening variables prohibiting any form of

standardization ofthe data. O'Connor and Mead (1982,

1984), however, showed that an index of stock dove

numbers based on a standardized ringing total was

highly correlated with the results of the CBC's scheme.

However, this index was specifically selected for the

species only after substantial investigation and

cross-checking of potential biases and does not seem to

be valid as a general index. Mason and Hussey (1984)

also attempted to devise population indices based on

ringing totals (of nestlings) and obtained several

parallels with known population changes. However, the

majority of the trends they demonstrated lacked

validation, making it difficult to know what significance

to attach to them. Interest in using United Kingdom

ringing data for monitoring has therefore shifted to a

concept known as the Constant Effort Sites (CES)

scheme. This scheme is based on a protocol already

successfully in use in Germany (Berthold et al. 1986).

The idea is that participants in the scheme undertake to

standardize the amount of their netting activity on a site

by controlling the amount of netting used and by

undertaking trapping on a regular schedule through the

breeding season. Under these conditions the absolute

totals of birds trapped should be consistent from year to

year, barring successional changes or habitat

modification. By examining the catches of adults

obtained in the early part of the season, an assessment

of population change is possible. Data obtained later in

the season can be used to compute ratios ofjuveniles to

adults as a measure of annual productivity, and

information as to retraps between years can be used to

estimate survival rates.

Since 1981, the CES scheme has run on a trial basis,

funded by what is effectively a surcharge on banding

permit fees, and is currently the subject of intensive

methodological investigations. Little has been

published by way offormal study ofthe scheme, though

several internal BTO documents have reported on the

scheme's progress, and annual reporting in the public

domain has started (Baillie and Holden 1988).

Observers are asked to visit their site once in each of

1210-day periods from May through August. The same

set of standard net-sites, each using the same size and

type of net, must be used on each visit. Two protocols

are under investigation at presen~. One is rigid and

prohibits any additional netting within 400 m ofthe site
150 BIOLOGICAL REPORT 90(1)

and allows only the 12 trapping visits. The other is

flexible, permitting use of additional net-sites and

additional trapping visits, but holding these data

separate from the standardized work. Emphasis is

placed on using sites homogeneous in habitat,

particularly for reedbed and thorn scrub habitats wel1

favored by warblers but poorly covered by the CBC

scheme. (Warblers in Britain are considered a group

of potential conservation concern.) A particular aim

ofthe CES scheme is to determine how the population

dynamics of particular species vary between habitats.

Batten (1973) provides the major case study, for the

European blackbird (Turdus men/la), of this type

undertaken to date in Britain.

Some interesting philosophical problems are

associated with the use of the CES scheme for

monitoring. First, if the results for al1 species surveyed

by the CES method were to prove well corrc1ated with

the CBC, the CES method would add nothing to the

information on trends established by the monitoring

program. The correlation, however, adds confidence to

the validity of either scheme as a monitoring tool.

Second, a priori one might expect species differences in

temporal trends obtained in the two schemes. For

example, reed warblers feature prominently in mist net

catches but are poorly censused by the CBC scheme

because of features of their territorial behavior. Hence,

failure to find a correlation for individual species does

not invalidate the potential of the CES method, but

leaves the meaning of the CES index for that species in

question. Third, if divergences between CES and CBC

results are obtained, how does one interpret CES data

for species that are not monitored at all by the CBC?

Similar questions arise in rc1ation to the concept of

productivity monitoring, where the BTO's nest record

scheme provides the calibrating dataset. A major worry

about the CES scheme is that it has now reached its

seventh year without the detailed statistical analysis that

Taylor (1965) provided for the CBC scheme after just 3

years. Such schemes tend to devc10p a momentum of

their own and to be difficult to discontinue should their

results prove inviable. Despite these caveats, possibly

overemphasized here against the background ofthe very

thoroughly researched CBC scheme, research

conducted to date has been promising. First, the annual

capture patterns for a given species tend to be relatively

consistent from year to year, and seasonal recaptures

indicated that a large proportion ofthe adult population

is caught annually. Second, where CBC and CES data

have been compared within similar habitats (the habitat

coverage for the CES scheme differs markedly from that

of CBC), there is good agreement in the overall trends

apparent. These early results suggest that the CES

scheme may prove a useful complement to the CBC

monitoring program.

Coverage of Scarce Species

The CBC's name-the Common Birds

Census- emphasizes the basic significant bias of the

scheme towards birds that are particularly abundant.

These birds are often habitat generalists and are not the

ones that are most likely to be affected by selective

changes in land use. An important issue, therefore, is

how to index the populations of scarcer species. For

some years the Trust reported annually a Scarce Species

Index calculated by combining the results from all plots

that supported the target species, irrespective of the

habitats involved. Two difficulties, one technical, the

other practical, have arisen over this index. First, when

an index is formed by combining data from a variety of

habitats that have characteristically different densities

ofa species, any change in the annual composition ofthe

CBC returns has a direct effect on the index, quite

independent of the changes in bird numbers. Second,

the use of a scarce species index promotes the inclusion

of so-called special plots for which special pleading is

possible, for example, that a plot contains breeding

nightjars (Caprimulgus europaeus) or the like. The

temptation has been to include such a plot to obtain the

data for, in this case, the nightjars, but in so doing the

Trust staff undertakes a commitment to analyze all the

returnsfrom that plot, for common species and nightjars

alike. The inclusion ofspecial plots has proven to be far

from cost-effective, and the index was discontinued

following the review reported by O'Connor and Fuller

(1984). Neverthelcss, even the most recent annual

report of the CBC scheme continues to base results on

mixed habitat samples for 13 species (Marchant and

Whittington 1988).

If rare species are excluded from the monitoring

scheme on these grounds, what options are available?

One possibility is to recognize that there are several

types of rarity: some species are widely distributed in

small numbers, others have a very local distribution, and

yet others are widespread in reasonable densities but

only where 10cal1y available habitat is present for them.

Against this background, the optimal procedure isto use

a large-scale low-intensity survey to establish where

these species are present, followed where necessary by

more detailed survey work targeted on the sites known

to hold the species of interest. This type of monitoring

has long been undertaken by the BTO in the form of

single species surveys. These surveys are planned by a

small working group of experts on the species,

appropriate techniques are developed from personal

knowledge or through pilot field work, and the BTO

membership at large is then invited to participate in the

survey of species (or habitat) concerned in a particular

year. Coordination of this observer network is achieved

through a system of regional representatives, each of

whom is responsible for the local organization of
coverage for their area. Surprisingly, this approach has

hardly been tried in the United States, presumably

because most of the species charismatic enough to

attract funding with which to administer such a survey

are already so limited in distribution and statusthat they

are already monitored by professional workers.

A possible new use of the CBC scheme data would

be as an initial screen for single species survey

candidates. For many less-common or scarce species

the proportion of survey plots on which the species is

recorded is well correlated with its local abundance.

This is true both interspecifically (Fuller 1982;

O'Connor and Shrubb 1986a) and intraspecifically

(Gibbons 1987). Hence, even though a species may be

too scarce on the CBC plots to generate enough clusters

to allow computing a CBC index, changes in the

frequency of its listing on the CBC plots may be

informative. Marchant (1980) provides an example of

the effective use ofthis technique in tracing the recovery

of the European sparrowhawk from the effects of

organochlorine pesticides. It should therefore be

possible to use the CBC data for very scarce species to

identify those in need of greater monitoring attention

through the mechanism of the single species survey.

Cornell University's Laboratory of Ornithology has

used a rather similar approach in developing an

information base in respect to species of management

concern (G.S. Butcher, personal communication).

Even with such maximized exploitation of CBC

data, many species- particularly those in habitats not

surveyed by the CBC in Britain - remain to be

monitored. The use of large-scale atlases of breeding

birds is now considered to be a useful approach here.

The first such atlas of Britain and Ireland is that of

Sharrock (1976), for which the BTO and the Irish

Wildbird Conservancy (IWC) surveyed some 4,000

atlas squares, each a 10- x lO-km unit of the Ordnance

Survey National Grids, and mapped proof (or absence

of proof) of breeding of all species on the two islands.

A subsequent Atlas of Wintering Birds in Britain and

Ire/and (Lack 1986) mapped the midwinter

distribution of each species, the index of abundance

being related to the number of birds found in timed

searches of each lO-km square. The concept of a

quantified distribution atlas has been extended in the

light ofthe success of the winter project, and BTO and

IWC fieldwork to map relative numbers of all breeding

species started in 1988. In this work, relative

abundance is to be assessed from a frequency measure,

the proportion of tetrads (the 25 2- x 2-km squares

within each 1O-km2 atlas square) in which each species

is recorded. This atlasing technique can then be

repeated at 20- or 25-year intervals to provide a

periodic overview of relative abundance and

distribution of each bird species breeding in Britain

AVIAN POPULATION TRENDS 151

and Ireland, yielding a crude but probably effective

monitoring tool for the general run of species in the

two countries.

Productsfrom the Monitoring Program

A significant shift in thinking about the purposes of

a monitoring program has revolutionized the BTO's

work with CBC, WBS, and other such data in recent

years. Given the high cost of servicing the gathering of

field data by amateurs for the CBC and other

programs, the indices of population levels generated

are no longer seen as sufficient product for the public

money spent with the Trust by the Nature Conservancy

Council (NCe). Instead, the emphasis has shifted to

producing other products, especially research papers,

relevant to the applied needs of the NCe. The

possibilities for such products are therefore

considered in the following sections.

Population Studies

Despite the deficiencies in the present CBC indices,

it has proven possible to use them in studying the

population dynamics of various species, especially the

most common ones, in Britain. Snow (1966) pioneered

the use of the BTO's data for this purpose, in a detailed

study of the biology of the European blackbird. Batten

(1977) subsequently deployed data from the full range

of the BTO's annual surveys to study this species'

population dynamicsin greater detail. Both studieswere

remarkable for being undertaken at a time when the

data concerned were available only as clerical records.

As part of an initial investigation of the potential of

computerized records, O'Connor (1980a) subsequently

studied the population dynamics of the great tit (Pants

major) in Britain and showed that the results obtained

from such extensive data banks were very similar to

those drawn from the intensive studies of this species in

Wytham Wood in Oxfordshire (Perrins 1965). As a

result, one can expect greater use ofthe BTO data banks

for population studies of various sorts. One problem

here is that the existing data are not yet fully

computerized, requiring significant work before they

can be used for studies of the national populations of

target species (see below). The immediate priority,

therefore, is to study the impact of severe weather on

bird populations in Britain, since this requires less

comprehensive data files. Early examples of the use of

BTO data for this purpose include studies by

Williamson (1975b, 1976), O'Connor and Cawthorne

(1982), O'Connor et al. (1982), and Baillie (1984).

An important strength of the BTO in using

monitoring data for studies ofthese types is that most of

the relevant schemes are organized from the BTO's

offices. As a result, all relevant files are available at a
]52 BIOLOGICAL REPORT 90(1)

single location and under homogenous conditions

regarding access. This is probably the single most

important factor behind the BTO's record in using the

extensive field data of amateurs for scientific purposes.

Thus, for most common species, data on population

levels are available from the Common Birds Census,

data on timing and success in breeding can be obtained

from the Nest Records Scheme, data on movement and

survival can be obtained from the Ringing Scheme, and

data on winter feeding can be obtained from the Garden

Birds Feeding Survey. Examples of how these data can

be integrated within a species can be found in the work

by Snow (1966), Batten (1977), O'Connor (198Oa, 1982),

and O'Connor and Mead (1984). Although the dispersal

across several agencies of data from such North

American schemes as the Breeding Bird Survey, the

Christmas Bird Count, the CornelI Nest Record

Program, and thc Bird-Banding Program must hinder

ready development of single species studies based on

data intcgration such as described earlier, it remains

surprising that major population studies based on these

data have not appeared. Presumably funding problems,

aggravated by a view in some quartersthat data gathered

by volunteers cannot possibly be taken seriously,

underlie this gap.

Habitat Studies

Although the CBC is theoretically an indicator of

environmental changc, it is in fact not suitable for

monitoring habitat loss as such. It is simpler, for

example, to study gross habitat losses from aerial

photographs or satellite imagery, procedures that are

not confounded with distributional biases on the part of

the observers. On the other hand, the CBC data are

particularly useful in rclation to the modification of bird

habitats, providcd the habitat data submitted by

observers is of high quality.

Data on habitat modification are required for several

different purposes. One is to assess the likely effect on

bird populations if currently observed rates of habitat

modification continue. Such information can also be

useful in evaluating projected land use patterns. A

second use for habitat modification studies lies in

determining how to manage habitats for a particular

species, either to ameliorate the effects of, say,

economics-driven land-use changes or to maximize the

carrying capacity of nature reserves for the species of

interest. Several approaches to the analysis of habitat

modification effects are possible. One is to conduct

cross-sectional studies of a number of different plots

that vary in the habitat characteristics of interest. Here

the number of territories on the plot constitute the

response variable and can be related through a variety

of statistical techniques to the habitat variables. This

approach showed a fairly large number of habitat

correlates of farmland species where cluster data have

been used (Morgan and O'Connor ]980; O'Connor and

Fuller 1985; O'Connor 1987). Similarly, woodland

habitat preferences, at least at a gross level, have been

established from CBC data (WiUiamson 1968;

Williamson 1974; Batten 1976; FuUer and Taylor 1984).

In these studies the essential requirement is that the

differences in habitat across plots are larger than any

variations in census efficiency for the target species in

these habitat types. Perhaps the classic example of this

type is Wil1iamson's (1969a) study of how wrens

(Troglodytes troglodytes) expanded their habitat use on

British farmland as the population recovered from a

population crash due to an extremely severe winter.

A second form of habitat study exploits the before

and after nature of the data gathered continuously by

CBC participants on plots on which a particular habitat

change occurs. On agricultural land, for example,

examples of such habitat changes might be the removal

of a pond, the grubbing-out of a hedgerow, the removal

of a smaU woodlot, and the canalization of a

watercourse. Such events often occur unpredictahly, but

the availability of before and after data provides an

extremely valuable case history. Thus, BulI et al. (1976)

traced a sequence of population changes on a

long-running CBC plot on a Norfolk farm to specific

events in its management history. Single instances of

such studies are almost anecdotal, but the simplicity of

the story they tell can be extremely valuable in

conservation education, and the accumulation ofsimilar

case histories generates statistical weight.

A significant issue rclating to the use of monitoring

data from the CBC scheme for habitat studies relates

to the scale of data to be used. The raw observations

gathered are the bird registrations, while the clusters

constitute derived information. The use of the original

registrations potentiaUy offers the finest

discrimination, in that it guarantees that a particular

bird was using a particular habitat clement at that

particular time. In contrast, clusters represent an

integration of the spatial information and may link

registrations that properly do not belong together, so

their boundaries may encompass a habitat in fact

unused by the birds concerned. On the other hand, use.

of individual registrations can be problematical,

because some may involve the same bird seen on more

than one occasion, thus influencing the degrees of

freedom. Individual registrations are also biased

toward territorial behavior, which constitutes but a

subset of the bird's total behavior. A system of spatial

integration could, therefore, detect features that

would have otherwise gone unseen. For example, most

song thrushes (Tllrdlls philomelos) might be seen

singing in taU trees, but their association with the

number of ponds on a farm would not be detected in
such observations, even though they feed for

invertebrates in the soft ground around the ponds. One

problem with the use of specific case studies is that

such work can readily be generated by individual field

workers, while the BTO CBC scheme is intended to be

a national monitoring program. It can therefore be

argued that the BTO's research program should

concentrate on the use of the national data set (Milner

and Hornby 1977). Such studies are, however, data

~xtensive and require good computing facilities and

the existence of the historic data in appropriate files.

Such facilities have been available to the BTO only

during the 1980's, but since then have greatly

influenced the Trust's research program. Examples

include work by Morgan and O'Connor (1980),

Pearson (1980), and Fuller and Taylor (1983), all of

which used subsets of the CBC returns to obtain

broader-scale pictures of population dynamics. Such

analyses do nced careful undertaking, however.

Johnson (1981) points out that the statistical issues arc

not trivial, since (1) birds may respond in a nonlinear

fashion to habitat features, (2) the confidence zone of

statistical predictions needs defining, and (3) even

then the particular studies need to be validated with

respect to their overall representation in the country

as a whole. One special strength of the CBC scheme is

its ability to provide both the wide-ranging data bank

needed for this type of analysis of habitat correlates

and the range of before and after case histories with

which to cross-check these statistical conclusions.

The ready availability of good quality habitat data,

at least for agricultural census plots, has been

invaluable in permitting the British studies reviewed

here. This suggests that one of the major weaknesses

in the North American databanks is the absence of

good habitat data directly associated with the census

points. The Breeding Bird Census - the scheme most

analogous to the United Kingdom Common Birds

Census- plots are too sparse to meet the need. The

U.S. Fish and Wildlife Service has conducted a I-year

survey of habitat at BBS routes, and the results will be

interesting to review in this context. To study the

effects of habitat modification on bird populations,

however, such data must be collected periodically, if

not annually. Data are already available in the United

States to show that agricultural bird populations are

being affected by changing land use practices (e.g.,

Natural Research Council 1982), and the issue of

habitat fragmentation and its effect on migrant birds

has already generated a substantial literature (e.g.,

Robbins 1980; McLellan obbinset al. 1986). The

routine collection and analysis of habitat data thus

seems, in the light of British experience, an

extraordinary omission.

AVIA,'II POPULATION TRENDS 153

Dynamics of Habitat Use

An important interplay ofmonitoring information and

habitat use relatesto the dynamical use of habitat bybirds.

Theoretical work by Brown (1%9) and Fretwell and

Lucas (1969) showed that at high densities species may

expand the range of habitats they occupy. In some

circumstances, densities in these secondarily occupied

habitats may even exceed the densities observed in the

preferred habitats (van Horne 1983). Hence, if one took

the widely held view that relative densities reflect the

relative values of different habitats, one would conclude

erroneously that the habitats occupied at high densities

were the most desirable for conservation management. A

significant part of the BTO's recent research with CBC

data has therefore focussed on habitat dynamics. Major

reviews of the underlying theory are provided by

O'Connor(1980a, 1985) and O'Connor and Fuller (1985),

and examples of how individual species are affected arc

given by Williamson (1%9a), Williamson and Ballen

(1977), and O'Connor (1980b, 1982, 1986). One

interesting product of such research is Pearson's (1980)

suggestion that the temporal stability of foliage layer use

may be a better predictor of such usc than is the classic

foliage height diversity index of MacArthur iversityand

MacArthur iversity(1961). He found that southern

English woodlands with the preferred foliage layers

present were less variable in their CBC counts than were

woodlandslacking these layers. Hence, at least for species

with labile populations, annual surveys of species

presence or absence in particular habitats could yield

information on habitat preferences without resorting to

detailed census work. The development of such new

insights into avian habitat needs is a significant basis for

the BTO's interest in such use of the monitoring data.

Studies on Habitat Succession and Avian Responses

Reserve management in Britain is frequently

concernedwith the modification ofsuccessional change.

For this, long-term census data are especially valuable,

and researches ofthis type, based on the longer running

woodland CBC plots, are likely to receive greater

attention in the future (e.g., Taylor 1983b; Fuller 1986).

A particular difficulty here is that the BTO's CBC

volunteers arc competent birdwatchers rather than

trained ecologists. The detailed habitat recording

required for successional studies, therefore, have to be

acquired by professionals. Several early woodland CBC

studies, mostly based on the expeditionary approach

already mentioned, were in fact directed to successional

studies (Williamson 1972a, 1972b, 1975). In these

studies, the detailed vegetation data needed were either

gathered by professional workers accompanying the

volunteer teams or were taken from forestry records.

More recently, Fuller and his colleagues (Fuller and

Taylor 1983) have led new research thrusts in this area.
154 BIOLOGICAl. REPORT 90(1)

It is worth noting that their work avoids the excessively

detailed vegetation data-gathering seen in many North

American studies of bird habitat, but continues the

tradition of Williamson's early studies by focusing on

habitat descriptors that are immediately relevant to

forest and woodland managers.

Site Assessment

Although the CBC method is not intended to

provide a national program of site assessment, it can

have value in providing information about individual

sites. Of major interest here are sites of special

conservation value, such as nature reserves and the

United Kingdom's Sites of Special Scientific Interest.

Such sites, however, provide a special dilemma for the

CBC scheme, because their protected status means

that their bird populations are artificially protected

against the environmental changes that the scheme

was designed to study in the first instance. One

resolution of this dilemma may be to recognizc that

experienced CBC volunteers are a resource more

valuable than the CBC scheme, and devise ways of

using their pool of experience for conservation

evaluation. Williamson and his colleagues (Williamson

1969b, 1974; Batten 1976) organized an early and very

successful CBC training program of expeditionary

fieldwork in habitats or areas of special conservation

interest. The program provided not only training

opportunities but also a wealth of hitherto unavailable

data on the typical breeding densities of species in

these habitats. The program gradually dwindled as the

CBC scheme developed, in part because the same core

of enthusiasts repeatedly participated, undermining

the training component then being emphasized, and in

part because of concern that the restricted time span

of such mapping censuses might bias results. This last

issue has since been examined experimentaUy and

found not to be a problem (Edwards 1977; O'Connor

1980c), and the BTO has recently resumed a program

of combined CBC training and site investigation (e.g.,

Fuller and Taylor 1983).

Conclusion

Thinking about the nature and purposes of a bird

monitoring program in Britain has changed

dramatically in the course of the 25 years for which the

CBC has now run. Emphasis has shifted from the

production of simple indices of population levels to a

sophisticated program of research in avian ecology

addressed to pressing issues of avian conservation.

Some of the answers generated arc broad, providing

background information on such questions as regional

and habitat variation in typical densities. Others are

quite specifically addressed to issues in theoretical

ecology, to generate understanding of how bird

populations function. Finally, yet other questions are

strictly applied, either to the assessment of the bird

populations of key sites or to the continued

improvement of the cost-effectiveness of the overall

program. It has become absolutely clear that to freeze

the CBC scheme into a fixed regime of fieldwork,

analysis, and routine reporting would mean its rapid

demise. Instead, the challenge for the BTO's leadership

is to recognize the unfolding potential for the Trust's

monitoring programs and to exploit that potential as

fully as possible without destroying the elements of

standardization that must be retained. Such a program

is not easily imported into North America, with its

different cultural traditions and much greater per capita

land mass than Britain. Nevertheless, some elements

would be particularlyvaluable in enhancing the rich data

banks ofbird population information in North America.

Greater emphasis on acquiring habitat data in parallel

with the bird data, increased research effort outside the

monitoring context (and particularly in introducing bird

data bank studies to the ecological literature in North

America), and a systematic program to promote the

standing of data bank-based studies would likely prove

especially rewarding. Taylor (1989) provides a recent

review of the BTO's bird monitoring program in the

context of long-term ecological research that develops

the generality of the specific points made here.

Acknowledgments

This paper is based on work undertaken while

Director ofthe British Trust for Ornithology and funded

under a contract from the United Kingdom Nature

Conservancy Council. My participation in the Patuxent

workshop was funded by the U.S. Fish and Wildlife

Service. I am grateful to both for their financial support.

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have all the whitethroats gone? Bird StUdy 21: 1-14.
Part III: Scissor-tailed Flycatcher

Analysis

We selected the scissor-tailed flycatcher (Tyrall/llis forficallls) data as a sample data set for analysis because the

flycatcher's breeding range is almost completely encompassed by the North American Breeding Bird Survey.

Populations vary greatly in abundance, both among States and among routes within States. Also, the species

apparently experienced a population crash in the mid-1970's, but has been recovering during the 1980's. These

features make it difficult to adequately summarize population trends.

Although several participants in the workshop presented analyses of the scissor-tailed flycatcher data set, only

three authors submitted papers for the proceedings. Two ofthe papers present wute-regression analyses (although

differences exist in the application of the method), while the third paper presents a nonparametric analysis. The

route-regression analyses differed in their approach to accommodating the decline in the mid-]()70's. Collins

(1990) modeled the decline and subsequent population increase by estimating trends within subperiods of the

1967-87 interval, while Sauer (1990) presented annual indices of abundance thal displayed the decline. Although

it is unlikely that a long-term trend can be estimated from these data, both analyses were successful in modeling

the dynamics of the population over the interval. Moses and Rabinowitz (1990) use the scissor-tailed flycatcher

data to illustrate their modellitting procedure (The scissor-tailed flycatcher data set is available to individuals who

want to use it for comparative purposes; it can be obtained from the editors.)

Literature Cited

Collins, B. T. 1990. Analysis of scissor-tailed flycatcher population changes. Pages 157-159 in J. R. Sauer and S. Drocge, cds.

Survey designs and statistical methods for the estimation of avian population trends. U.S. Fish. Wild\. Serv., Bio\. Rep. 90(1).

166 pp.

Moses, L. E., and D. Rabinowitz. 1990. Estimating (relative) species abundance from route counts ofthe Breeding Bird Survey.

Pages 71-79 in J. R. Sauer and S. Droege, cds. Survey designs and statisticnl methods for the estimation of avian population

trends. U.S. Fish. Wild\. Serv., Bio\. Rep. 90(1).166 pp.

Sauer, J. R. 1990. Route-regression analysis ofscissor-tailed nyc,lIcher population trends. Pages 160-163 in J. R. Sauer and S.

Droege, eds. Survey designs and statistical methods for the estimation of avian population trends. U.S. Fish. Wildl. Serv.,

Bio\. Rep. 90(1).166 pp.

AVIAN POPULATION TRENDS 157

Analysis of Scissor-tailed Flycatcher Population Changes

by

Brian T. Collins

Canadian Wildlife Ser...ice

Ottawa, Ontario

Canada KIA OH3

The data were analyzed (1) separatc1y for each State,

(2) separately for each physiographic stratum, and (3)

for all States together. In order to assess whether a singlc

trend could be used to analyze the entire 22-year data

set, the analysis was done for five periods oftime: (1) the

most recent 5 years (1983-87), (2) the period up to the

most recent 5 years (1966-83), (3) the most recent 10

years (1978-87), (4) the period up to the most recent 10

years (1966-78), and (5) the entire data set (1966--87).

In the analysis presented here, no adjustments were

made for observers, and the area weights assigned to the

routes were the same for all time periods. Since the

routes which are run change with time, the area weights

should be reassigned for each time period under study.

For example, in Kansas there were 37 usablc routes in

the period 1966-83, but there were only22 usablc routes

in the period 1983-87. This is caused partly because

some routes were not run in the most recent 5-year

period. In light of this, the area weight assigned to these

routes should be larger for the most recent 5-year

period, but this was not done because I did not have the

required information.

The results of the trend analysis for the 22 years of

data arc shown in Table 1. This table shows several

different methods of presenting the estimates of trend

and should allow comparison with other analyses.

The estimated trends were negative for all States with

more than 10 routes. The declines were significant

(P < 0.05) for Texas, Oklahoma, Arkansas, and Kansas.

These arc the States with the largest number of routes,

and the lack of significance for Missouri and Louisiana

may reOect the small number of suitable routes run in

those States rather than a stable population. The mean

count per route was so small that the estimate of trend

may have a substantial bias for all States except Texas

and Oklahoma.

The analysis by physiographic stratum revealcd a

wider range of estimated trends. This may be partly due

to the large number of strata which hence tend to have

fewer observations than the States. There was a

significant (P < 0.05) increase in stratum 53 and a

significant decline was detected in four strata: 8, 34, 35,

and 56. All 16 routes in stratum 53 are in Texas, and the

estimated increase is counter to the overall estimate of

decline for that State. The increase in stratum 53 is

counteracted by the significant declines in strata 8, 34,

and 56, which combine to give an overall estimate of

decline for the State.

The analysis separating the trend into smaller time

frames is shown in Table 2. In this tab1c the trends are

presented as percentage change that would occur over

a lO-year period, and the number of routes used to

calculate the trend is shown in brackets. It can be seen

that this method of presenting results can reveal

unrealistic measures oftrend when the number ofroutes

and the time frame arc small, such as that for stratum 5

in the period 1983-87. In addition, since the analyses for

different time frames are sometimes based on different

routes, there is a lack of consistency between the

analyses for different time periods. For example, in

stratum 19 the estimated trend is negative for the

periods 1966--78 and 1978-87, but the combined trend

for 1966-87 is positive.

The important thing to note in Tab1c 2 is the lack of

consistency in the estimates from different time periods.

For the combined strata the estimated trend is negative

for the initial portion ofthe survey, followed by a positive

trend in the more recent years. The decline was

significant for the period 1966--78, and the increase was

significant for the period 1978-87. The results for the

individual States for these time periods reveals a similar

pattern of decline followed by an increase for six States:

New Mexico, Texas, Oklahoma, Arkansas, Kansas, and

Louisiana. The declines were significant for all of these

States except New Mexico, which had only a few

observations, and the increases were significant only for

Texas. Similarly, the trends for the strata reveal a decline

in the period 1966--78, followed by an increase for the

period 1978-87 for 10 strata: 55, 53, 32, 7, 54, 33, 4, 6, 35,

and 56. These declines were significant for all but two of
158 BIOLOGICAL REPORT 90(1)

Table 1. Estimated trellds ill scissor-tailed flycatcher (Tyrannus forficatus) 1966-87.

Transformation log(x+0.23)

Windsorizing value 100

Number Estimate of Doubling Percentage change

of slope in or half- Per Over

Stratum routes LOGtoscalc life year 10 years

Kentucky l

a 0.0094 d 32 2.18 24

New Mexico 3a 0.0080 d 37 1.86 20

Iowa l

a -0.0007 hgnC -0.16 -2

Texas 109 -O.0042*b h 72 -0.96 -9

Oklahoma 35 -0.0046* h 65 -1.05 -to

Missouri 14a -0.0099 h 30 -2.25 -20

Arkansas 22a -0.0102* h 29 -2.32 -21

Kansas 37a -0.0178* h 17 --4.02 -34

Louisiana 11a -0.0187 h 16 --4.21 -35

3 1 0.0392 d 8 9.45 147

55 8 0.0220 d 14 5.20 66

53 16 0.0158* d 19 3.70 44

14 1

a 0.0094 d 31 2.18 24

32 6a 0.0069 d 44 1.60 17

19 21a 0.0067 d 45 1.55 17

81 l

a 0.0051 d 59 1.18 12

7 18 0.0024 d dgn 0.55 6

5 6a 0.0006 dgn 0.14 1

54 12 -0.0011 hgn -0.25 -2

33 36 -0.0039 h 77 -0.89 -9

4 25 -0.0042 h 72 -0.96 -9

6 13 -0.0076* h 40 -1.73 -16

35 16 -0.0113* h 26 -2.52 -23

36 l

a -0.0153 h 20 -3.46 -30

8 21 -0.0159* h 19 -3.59 -31

56 9 -0.0199* h 15 --4.47 -37

34 22 -0.0267* h 11 -5.96 --45

Combined 233 -0.0049* h 62 -1.12 -10.7

abMean route counts are less than two. and trend may be substantially biased.

• = significant trend (P < 0.05) based on 1.000 rerandomizations.

hgn~ = half-life greater than 99.

dgn = doubling-life greater than 99.

these States, 55 and 32, whilc the increases were

significant for four of them 53, 7, 54, and 33. In the

analysis presented in Table 1, stratum 53 was the only

region which showed an overall significant increase and

hence appeared different from the other strata. By

separating the data into shorter time periods it is

revealed that the population changes in this stratum are

similar to those in other strata except that the increase

over the most recent 10 years is more pronounced.

The estimates of trend for stratum 8 are similar in

magnitude and direction for all time periods

considered. This suggests that for this stratum

calculating an overall trend line for the entire period of

the survey is valid. For all other strata and all States,

however, the estimates of trend are not consistent for

the different time frames, which indicates that

estimating the trend over the entire 22-year period of

the survey is not a tenable summary of the data.
AVIAJ' POPPLATlOJ' TRENDS 159

Table 2. Estimated trendsin scissor- tailedflycatcher (Tyrannus farficat us)forselectedtime periods. Trends are shown

as the percentage change that would take place overa 10-yearperiod, with the numberofroutes used in the analysis

shown in brackets.

Transformation log(x+0.23)

Windsorizing value 100

Period

Stratum 1966-83 1983--87 1966--78 1978--87 1966---87

Kentucky 55 (1) 99 (1) 20 (1) 24 (1)

New Mexico 20 (3) 45 (2) 97 (2) 20 (3)

Iowa 7 (1) 51 (1) 2 (1)

Texas 253 (108) 683 (76) 493 (104) 463 (99) 93 (109)

Oklahoma 163 (35) 10 (23) 273 (33) 14 (34) 103 (35)

Missouri 14 (13) 16 (11) 51 (9) 24 (14) 20 (14)

Arkansas 24 (15) 44 (17) 493 (14) 22 (18) 21 3 (22)

Kansas 433 (34) 14 (22) 763 (34) 24 (28) 343 (37)

Louisiana 623 (11) 734 (4) 753 (11) 192 (6) 35 (11)

3 222 (1) 981 (1) 29 (1) 76 (1) 147 (1)

55 41 (7) 23 (4) 69 (6) 272 (7) 66 (8)

53 15 (16) 99 (12) 343 (16) 1503 (16) 443 (16)

14 55 (1) 99 (1) 20 (1) 24 (1)

32 38 (5) 75 (3) 10 (4) 90 (4) 17 (6)

19 28 (17) 86 (19) 20 (13) 13 (21 ) 17 (21 )

SI 43 (1) 10 (1) 12 (1)

7 2S3 (IS) 1743 (12) 553 (18) 1583 (14) 6 (18)

5 9 (4). 5,2703 (2) 30 (4) 1953 (2) 1 (6)

54 IS3 (12) 76 (8) 403 (11) 393 (11) 2 (12)

33 21 3 (34) 118 (26) 423 (33) 14 (35) 9 (36)

4 27 (24) 6 (14) 533 (24) 333 (20) 9 (25)

6 273 (13) 36 (8) 493 (13) 32 (10) 163 (13)

35 333 (16) 95 (11 ) 393 (16) 14 (15) 233 (16)

36 37 (1) 55 (1) 30 (1)

S 293 (21) 53 (19) 463 (20) 21 (21 ) 313 . (21)

56 583 (9) 1,783 (3) 60 (8) 43 (7) 373 (9)

34 473 (1) 4S (11 ) 61 3 (20) 24 (16) 453 (22)

Combined 243 (221) 47 (154) 453 (208) 383 (202) 113 (233)

, Signific3nt trend (P < 0.05) b3sed on 1.000 rer3ndomiz3tions.
160 BIOLOGICAL REPORT 90(1)

Route-regression Analysis of Scissor-tailed Flycatcher

Population Trends

by

John R. Sauer

u.s. Fish and Wildlife Service

PatlLr:ent Wildlife Research Center

Laure~ Maryland 20708

Introduction

The Office ofMigratoryBird Management ofthe U.S.

Fish and Wildlife Service has supported the development

of several computer programs for the estimation of

population trends and annual indices of abundance from

North American Breeding Bird Survey (BBS) data. The

programs presently in use are written in FORTRAN,

although alternative programs are available which are

written for standard statistical packages (e.g., the

SAS-based programs used to estimate trends in Robbins

et al. 1986). This statistical analysis of scissor-tailed

flycatcher (Tyrallllus forficatlls) population trends

provides an example of the present method adopted by

the U.S. Fish and Wildlife Service to estimate population

trends and annual indices of abundance.

Methods

Population trends are estimated using the route

regression method (Geissler and Sauer 1990). We

include observers as covariahlcs in the route

regressions. The median of the bootstrapped estimates

of trend from individual routes is used as the estimate

of population trend in this analysis. The trend is

presentcd as a percent change per year, which is the

median trend -1 times 100 %. Statistical significance of

the trend estimates (the test of the null hypothesis of no

trend) is assessed using z-tests. Indices are estimated

using the residual method (Sauer and Geissler 1990).

Population trends and annual indices of abundance

are estimated for State, physiographic stratum (see

Butcher 1990 for a listing of the present BBS

physiographic strata), and the entire region of the

survey. In addition to the trend estimates, I also

present the proportion of routes in each region which

had negative point estimates of trend. These

proportions represent an unweighted measure of the

consistency of population declines in the region.

Finally, I present the average density (in number of

birds recorded per BBS route) of scissor-tailed

flycatchers on BBS routes within the regions.

Results and Discussion

Trend E'ltimates

Scissor-tailed flycatchers declined in the United

States during 1%7-87. Statistically significant (P < 0.1)

declines occurred in Arkansas, Kansas, and Oklahoma,

and Missouri and Texas had negative but nonsignificant

point estimates of trend (Table). Point estimates of

trend were also negative in 9 of the 14 physiographic

strata in which scissor-tailed flycatchers occur, and

three of the declines were statistically significant.

Over the entire area surveyed, the estimate of trend

was -0.89 (N = 229; P < 0.1).

Because trend estimates are weighted by the density

of birds on the route, they do not always show the same

patterns as proportion of declining routes in a region. In

general, though, there is a close correspondence

between the two estimators of population change in a

region (Table).

The relative contribution of State or physiographic

stratum trends to the survey-wide trend estimate is

determined by both the land area in the region and the

relative density of birds in the region. Densities vary

greatly among States and physiographic strata (Table),

with some States (e.g., Louisiana) having extremely

low densities of birds, but others (e.g., Oklahoma)

having high densities. It is interesting to note that the

only States with positive point estimates of population

trend have low densities of scissor-tailed flycatchers,

and therefore probably do not contribute greatly to the

overall trend estimate.
AVIA1'" POPULATION TRENDS 161

Table. Population trends ofscissor-tailedj7ycatchers (Tyrannus forticatus) for 1967-87for States, physiographic strata,

and the entire breedingrange in the United States. The estimate oftrend is the median ofthe bootstrappedpopulation

trends for the region, and is presented as percent change per year. The significance ofthe trend estimate (P, test of

Ho:trend = 0) is presented as *:P < 0.1; ***:P < 0.01. Sample size (N) is the numberofBBS rolites in the regions,

Pdec is the proportion ofrolltes in the region with declining populations, andA wrage is the average density ofbirds

on routes in the region. Trends are not presented for regions with N 2 routes.

Region Trend

State

Arkansas --6.36

Kansas -1.79

Louisiana 1.63

Missouri -1.32

New Mexico 6.47

Oklahoma -2.01

Texas -.29

Stratum

4. -3.56

5. -D.05

6. -D.OI

7. 0.03

8. -2.49

19. -1.71

32. -10.17

33. 0.22

34. -4.52

35. -3.47

53. 2.56

54. 0.24

55. 1.01

56. --6.70

Breeding range

U.S. -D.89

P

***

*

***

***

*

***

*

N Pdec Average

22 0.500 1.23

36 0.611 2.09

11 0.545 0.66

14 0.357 0.38

3 0.000 0.34

35 0.629 21.90

108 0.546 18.88

25 0.600 6.18

6 0.500 0.04

13 0.538 13.09

17 0.706 23.60

21 0.762 29.39

21 0.333 1.74

6 0.500 0.04

36 0.528 19.22

22 0.545 3.20

15 0.667 15.05

16 0.375 17.96

12 0.500 29.51

8 0.375 2.06

9 0.556 1.42

229 0.546 10.918

Annual Indices ofAbundance

Annual indices of abundance are quite variable in

most of the States in which scissor-tailed flycatchers

occur (Fig. 1), but a general pattern of annual indices

below the predicted trend line during the mid-1970's

occurs in all of the high-density States. Texas in

particular shows a distinct minimum in 1977.

Indices of abundance from the entire survey (Fig. 2)

indicate that scissor-tailed flycatchers declined

through the early 1970's and reached minimum

densities in 1978. After 1978, the population has been

stable or increasing slightly, but has not reached

density levels noted in the late 1960's and early 1970's.

The indices from the entire survey area generally

refle~t the patterns of change seen in Oklahoma and

Texas, where scissor-tailed flycatchers reach their

highest densities.

What Factors Influence Population Changes in

Scissor-tailed Flycatchers?

Robbins et aJ. (1986) documented the decline in

scissor-tailed flycatcher populations through the

mid-1970's. Here, I show that populations have been

generally increasing after the low year of 1978. Over the

entire time interval, however, the population still shows a

slight decline, indicating that the flycatchers have not yet

achieved the levels of abundance that existed during the

early years of the survey.

It is difficult to explain the patterns of population

change in the scissor-tailed flycatchers. The scissor-tailed

flycatcher pattern of decline, then increase, is remarkably

similar to the pattern of population change in several

short-distance migrant and permanent resident species,

such as Carolina wrens (ThryotllOnLS ludovicianus),

eastern bluebird., (Sialia sialis), and mourning doves
162 BIOLOGICAL REPORT 90(1)

Ar-konsas Missouri UJ 0 N

0

N

X X

X X

x QJen x QJen u~

c uo,

C l- x x x x

x x x x x x x x x x x x x x 0 x x x x x x x x x x

UJ 0 0 67 69 71 73 75 77 79 81 83 85 87 0 67 69 71 73 75 77 79 81 83 85 87

,n Kansas (Jklahoma n ", c',

0

n ~\ x

en x ~~ x N ,n

x CJ

U

QJ

x (j) --~ n x C x x C x -0 X X

N X X 0

l-: ' X X X X X X X X

X

X X ~~

en UJ

0 0 67 69 71 73 75 77 79 81 83 85 87 67 69 71 73 75 77 79 81 83 85 87

Louisiana TexClS UJ w',

n

'-~)

c")

0

X X CJ

X x QJ X Q)

U x X \"J

C , x X C

,-J X UJ X X X X X X ~,. x

0 x x x

75 77 79 81 83 85 87

'(eo r

o c" L-_~_~ L.-_~_-L __'--_-'--_-'-__,-----.J

0 67 69 71 73 75 77 79 81 83 85 87 t"1 /1 73

Year

Fig. 1.Annual indices ofabundance (x) and predicted trend for scissor-tailed flycatchers (Tyrannllsforficahls) by State (excepting

New Mexico, in which the species was recorded on N =3 routes).
y

l'

T1

C

OJ

United ties

v X

AVIAN POPULATION TRENDS 163

the route regression estimates oftrend., in conjunction

with annual indices of abundance, provide sufficient

information for a complete analysis of scissor-tailed

flycatcher population changes over any interval of

interest.

Acknowledgments

I thank D. K. Dawson, M. Howe, andJ. D. Nichols

for reviewing this manuscript. H. C. Bourne assisted

with preparing the data for analysis.

Literature Cited

CD l-_L-.-----'_----l-_--'-_-L_-'---_----'---_--'-_.L-_

67 fi9 71 73 75 77 79 81 83 85 87

'r eor

Fig. 2. Annual indices of abundance (x) and predicted trend

for scissor-tailed flycatchers (Tyrannlls forficatlls) for the

entire surveyarea in the United States.

(Zenaida macroura). These species apparently declined

in response to a series ofsevere winters during the 1970's

(Robbins et a1. 1986). The scissor-tailed flycatcher is a

neotropical migrantspecies, but primarilywinters in open

habitats and is probably not affected by the tropical

deforestation that could affect forest-wintering migrants

(Robbins et a1. 1989). It is beyond the scope ofthis paper

to fully examine the possible explanations for the observed

population trends in scissor-tailed flycatchers. However,

Butcher, G. S. 1990. Audubon Christmas Bird Counts.

Pages 5-13 in J. R. Sauer and S. Droege, eds. Survey

designs and statistical methods for the estimation of avian

population trends. U.S. Fish Wildl. Serv., BioI. Rep. 90(1).

166 pp.

Geissler, P. II., and J. R. Sauer. 1990. Topics in routeregression analysis. Pages 54-57 in J. R. Sauer and S.

Droege, eds. Survey designs and statistical methods for the

estimation of avian population trends. U.S. Fish Wildl.

Serv., BioI. Rep. 90( 1). 166 pp.

Robbins, C. S., D. Bystrak, and P. H. Geissler. 1986. The

Breeding Bird Survey: Its first fifteen years, 1965-1979.

U.S. Fish Wildl. Serv., Resour. Publ. 157. 1% pp.

Robbins, C. S., J. R. Sauer, R. S. Greenberg, and S. Droege.

1989. Recent declines in populations of birds that migrate

to the neotropics. Proc. Nat. Acad. Sci. 86:7658-7662.

Sauer, J. R., and P. H. Geissler. 1990. Estimation of annual

indices from roadside surveys. Pages 58-62 in J. R. Sauer

and S. Droege, eds. Survey designs and statistical methods

for the estimation of avian population trends. U.S. Fish

WildI. Serv., BioI. Rep. 90(1). 166 pp.
164 BIOLOGICAL REPORT 90(1)

Trends in Counts of Scissor-tailed Flycatchers Based on a

Nonparametric Rank-trend Analysis

by

Kimberly Titusl

u.s. Fish and Wildlife Service

Virginia Cooperative Fish and Wildlife Research Unit

Deparlment ofFisheries and Wildlife Sciences

Virginia Polytechnic Institute and State University

Blacksburg, Virginia 24061

Introduction

A variety of statistical methods exist for estimating

trends in animal population sizes over time (e.g.,

Geissler and Noon 1981; Gerrodette 1987; Sauer 1990).

Many of these trend methods rely on regression type

procedures. While route-regression trend analyses

procedures provide information on the significance of a

trend (Geissler and Noon 1981), they also provide an

estimate of the annual change in counts over the time

period. In some instances, nonparametric methods may

be useful for determining trends over time, especially if

data were obtained in the form of ranks or if the

assumptions of route-regression methods are not valid.

These tests are usually variations of the sign test

(Conover 1980). One non parametric time-trend

estimation method was developed by Lehmann (1975).

This method has been expanded to allow for multiple

counts in a given time period, and is thus similar to some

regression procedures where counts from the same time

period can be combined to estimate a trend.

I compare results from a nonparametric rank-trend

analysis ofscissor-tailed flycatcher (Tyrannusfotjicatus)

BreedingBird Survey data with those obtained from the

standard route-regression trend analysis of the same

data (Sauer 1990). Empirical comparisons ofthe results

obtained from these methods is useful so that the value

and limitations of the less commonly used

nonparametric method are identified. In this way,

biologists may be better able to choose the appropriate

method for their application.

1 Present address: Alaska Department of Fish and Game, Division

of Wildlife ConselVation. P.O. Box 20. Douglas. Alaska 99824.

Methods

Breeding Bird Survey data for scissor-tailed

flycatchers were used in this analysis. A nonparametric

rank-trend analysis statistical procedure was applied to

the data. This method was described in Titus et al.

(1990) and was based on a two-tailed expansion of the

test statistic developed in Lehmann (1975). The

nonparametric rank-trend analysis program was

developed using the Statistical Analysis System (SAS

1985, 1987) by D. Jacobs. This program tested for a

trend in each of the 230 routes separately; it pooled

routes within States to determine an overall trend for

each State, and finally, all routes were pooled to

determine a single overall trend estimate. Results of the

nonparametric method were expressed as an increasing

or decreasing trend over time, along with an associated

level of significance.

Results and Discussion

Based on the nonparametric rank-trend analysis,

scissor-tailed flycatchers were declining throughout

their range as a whole (P = 0.0004), in Kansas

(P = 0.00(1), and in Oklahoma (P = 0.026; Table 1).

Only in Missouri was there a nonsignificant increase in

the trend estimate.

Comparison ofMethods

The route-regression and nonparametric methods

resulted in similar interpretations except for counts

from Arkansas, where the nonparametric method

detected no trend while the route regression method
Table 1. Poplllation trends ofscissor-tailed flycatchers (Tyrannus forficatus) from 1967-87 by Stale, based 011 nonparamelric rank-trend and rollle regression

trend analyses.

Nonparametrie method Route-regression method

State N 'Irend p Pdee" '[rend Significanceb Pdec Comment

Arkansas 22 decline 0.402 0.36 decline ••• 0.50 results very different; rank test not significant

while route regression highly significant

Kansas 37 decline 0.001 0.76 decline • 0.61 results similar between methods

Louisiana II decline 0.254 O.l) I increase NS 0.55 results similar; both insignificant, although

trends differ

Missouri 14 incrcase 0.191 0.36 decline NS 0.36 results similar; both insignificant, although

trends differ

New Mexico 3 dccline 0.485 0.67 increase NS 0.00 results similar; both insignificant, although

trends differ

Oklahoma 35 decline 0.026 0.63 decline ••• 0.63 results similar between methods

"has 108 decline 0.345 0.58 decline NS 0.63 results similar between methods

U.S. 230 decline < 0.00 I 0.60 decline • 0.55 results similar between methods

" Proportion of routes that had a declining trend.

b Significance of route-regression trend estimate; • = l' < 0.1; ... = l' < 0.01; NS = l' > 0.1.

*8

:::!

E

*'"l

;l:l

:"1

Z

;:;l

.....

0\

V1
166 BIOLOGICAL REPORT 90(1)

detected a significant decline (Table 1). This

discrepancy was likely due to the unbalanced nature of

the Arkansas data set. Data were available from 22

routes in Arkansas. One route had high counts (10-20

birds per route per year), a few routes had moderate to

low counts (2-8 birds per route per year), and on most

other routes observers counted no scissor-tailed

flycatchers in a given year. The single route with the

highest counts declined over this time, and the

route-regression method weighted this route more

heavily in the analysis. Thus, route-regression (Geissler

and Sauer 1990) can adjust for extreme differences in

counts among routes, while the nonparametric method

contains no weighting function.

This example bears out the important difference that

a weighting function can have on the future data

interpretation. Weighting may not be appropriate in

some situations, and biologists need to understand how

different statistical techniques might influence their

interpretations. The nonparametric method contained

no weighting function, although one could be

incorporated into the statistical procedure. In the

context of avian surveys, the nonparametric method

treats each area equally and is not influenced by the

population size of any survey location.

The scissor-tailed flycatcher data set contained many

routes with zeros. This resulted in many tied ranks, an

unfavorable situation for non parametric procedures

that evaluate ranks. The influence ofties may be to make

the results more significant because of the long length

of consecutive zero ties (Siegel 1956). Even though the

procedure developed in Titus et al. (1990) accounted for

tied ranks, the scissor-tailed flycatcher data set may be

better analyzed by a method that is less sensitive to the

many zero counts. Inspection of the proportion of

individual routes where counts were declining indicated

general agreement among methods (Table 1). Overall,

5% more of the individual routes had declining trends

in counts based on the nonparametric method. This

higher proportion of routes which were declining,

combined with the sample size dependent variance

estimate of the nonparametric method likely led to the

more highly significant overall trend when compared

with a the route-regression method.

Conclusion

The nonparametric rank-trend analysis method is

most appropriate in situations where the data contain

few zeros (or ties), and the counts may be estimates

rather than exact numbers, so an analysis based upon

ranks may be appropriate. The method provides less

information than a complete route-regression analysis

and annual indices of abundance (Sauer 1990).

However, if one desires information about an increase

or decrease in the population of interest, and they

cannot meet all the assumptions of regression methods,

the nonparametric method is appropriate.

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