OCS Study

MMS 92-O&O

DESIGN AND TESTING OF A MONITORING PROGRAM

FOR BEAUFORT SEA WATERFOWL

AND MARINE BIRDS

for

@

U.S. Department of Interior

Minerals Management Service

Procurement Operations, MS 2500

381 Elden Street

Herndon, VA 22070

Contract 14-35-0001-30491

LGL Report TA 863-lB

November 1992
OCS Study

MMS 92-0060

DESIGN AND TESTING OF A MONITORING PROGRAM

FOR BEAFORT SEA WATERFOWL

AND MARINE BIRDS

%’

Stephen R. Johnsonl and William J. Gazey2

1 LGL Limited, environmental research associates, 9768 2nd Street, Sidney, B.C. V8L 3Y8

2 W.J. Gazey Research, 1214 Cams Court, Victoria, B.C. V8X 4R1

Contract 1435-0001-30491

LGL Report TA863-lB

Submitted by

LGL Alaska Research Associates, Inc.

4175 Tudor Centre Drive, Suite 101

Anchorage, AK 99508-5917

Prepared for

U.S. Department of Interior

Minerals Management Service

Procurement Operations Branch, MS 2500

381 Elden SL, Herndon, VA 22070

November 1992

This study was funded by the U.S. Department of Interior, Alaska Outer Continental Shelf

Region of the Minerals Management Service (MMS), Anchorage, AK, under contract 14-35-0001-30491.

This report has been reviewed by the MMS and approved for publication. The opinions,

findings, conclusions, or recommendations expressd in the report are those of the authors and do not

necessarily reflect the views or policies of the MMS. Mention of trade names or commercial products

does not constitute endorsement or recommendation for use.

Project and Report Organization

PROJE~ AND REPORT ORGANIZATION

This project was conducted by LGL Limited, environmental research

associates, Sidney, British Columbia, through a contract from MMS to LGL

Alaska Research Associates, Inc., Anchorage, Alaska. The study was

conducted in cooperation with Golden Plover Air, Inc., Colville Village,

Alaska, and W.J. Gazey Research, Victoria, British Columbia. Golden Plover

Air provided the Cessna 206 aircraft, pilot, and field logistics support

associated with the aircraft for all of the surveys during 1989 through 1991.

W.J. Gazey helped with statistical analyses and interpretations.

The contract award for this project was 25 July 1989 and project

initiation was on 8 August 1989, when we tested the idea of conducting

several aerial surveys of the study areas over a short time period (three

surveys within a 5- to 7-day period).

An earlier report was submitted to MMS in November 1990 (Johnson

1990). That report presented the proposed design and rationale for the

Beaufort Sea waterfowl and marine bird monitoring protocol. The design

was based on an extensive analysis of nine years (1977-1984 and 1989) of aerial

survey data in the study area for the focal species, the oldsquaw duck

(Clangula 12yemlis). Based on the results of a multivariate analysis of

various factors affecting the distribution and abundance of old squaws in the

study area during the nine earlier years, the design report proposed a

statistically rigorous sampling program and an analysis of variance model for

analyzing data collected during the testing phase of the study — in 1990 and

1991. The testing phase provided an opportunity to test the original study

design and to make recommendations for improvements. This final

document reports on both the design and testing phases of the study.

This document should be cited as follows:

Johnson, S.R. and W.J. Gazey. 1992. Design and testing of a monitoring program for Beaufort

Sea waterfowl and marine birds. OCS Study MMS 92-0060. Rep. by LGL for U.S. Minerals

Manage. Service, Hemdon, VA. 114p.
Table o~Contenfs

TABLE OF CONTENTS

Project and Report Organization. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

List of Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

List of Figures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

List of Appendices. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Acknowledgements. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Research Hypotheses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Development of A Monitoring Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Study Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The Jones-Return Islands Industrial Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The Stockton-Maguire-Flaxman Islands Control Area..., . . . . . . . . . . . . . . . . .

Retrospective Assessment of Variables Affecting

Oldsquaw Densities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Results of Multiple Regression Analyses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Influences That May Affect Oldsquaw Density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Proposed Implementation of the Monitoring Protocol . . . . . . . . . . . . . . . . . . . . . . . . . .

Design Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Sampling Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Schedule of Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Data Recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Analysis Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The Proposed ANCOVA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Potential Problems and Sources of Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Repeated Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Statistical Independence of Covariate and Treatment . . . . . . . . . . . . . . . . .

Measurement Error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Testing of the Monitoring Protocol, 1990-1991 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Design Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Consideration of Other Independent Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Disturbance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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Table of Contents v

Seasonal Upwelling Potential. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Sampling Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Schedule of Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Analysis Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

The New ANCOVA Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Randomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Nesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Expected Mean Squares. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Covariates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Repeated Measures and Data %lection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Computing the Sensitivity of the ANCOVA model . . . . . . . . . . . . . . . . . . . . . . . . .

Statistical Software. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Results of the ANCOVA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Evaluation of Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Regional Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Local Disturbance Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Power of Key Statistical Tests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Literature Cited. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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/’i List of Tables

Table

1

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4

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8

LIST OF TABLES

Page

Summary of water depths and feeding efficiencies

of oldsquaws in the Jones-Return islands area,

Beaufort Sea, Alaska. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

Summary of results of multiple regression analyses

of historical oldsquaw density data collected in the

Jones-Return islands area, Beaufort Sea, Alaska,

during 1977 to 1984 and 1989. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Ordinal scale for recording levels and types of industrial

activities tiat may affect oldsquaw densities in the

Jones-Return islands area, Beaufort Sea, Alaska. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

The number of transects sampled in 5 different

habitats on different dates in different sampling

areas of the Industrial and Control study areas

in the Alaska Beaufort Sea, 1990 and 1991 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

Degrees of freedom and expected mean squares

of components in the ANCOVA of 1990-1991

oldsquaw density data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

Test statistics for each of the components in the

ANOVA of the 19890-1991 oldsquaw density data. . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Pearson correlation coefficients (r) for cross-

-correlation among potential covariates in the

ANCOVA of 1990-1991 oldsquaw density data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Results of ANOVA and ANCOVA of 1990-1991

oldsquaw density data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
List of Figures

LIST OF FIGURES

Figure Page

vii

1

2

3

4

5

6

7

8

Oldsquaw sightings as a percent of all waterbird

sightings in the central Alaska Beaufort Sea,

1977-1982,1984, and 1990-1991 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Central Alaska Beaufort Sea with Industrial

and Control study areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Industrial study area in the Jones-Return islands area,

central Alaska Beaufort Sea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Control study area in the Stockton-Maguire-Flaxman

islands area, central Alaska Beaufort Sea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Results of autocorrelation analyses of numbers

of oldsquaws in various barrier island-lagoon

habitats during four 30-hr cycles of activity

when oldsquaws were relatively undisturbed

and when waters were calm. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Mean * 95% C.I. of transformed densities of oldsquaws

on all barrier island and non-barrier island transects

during the molt period in the central Alaska. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Mean * 95% C.I. of transformed densities

of oldsquaws in three barrier island-lagoon

habitats in both the Industrial and Control study

areas in 1990 and 1991 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Figure 8. Percent change in the adjusted mean

density of oldsquaws for tests of disturbance and

year x area interaction, given different amounts

of sampling. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
. . . ml List of Appendices

LIST OF APPENDICES

Armendix AA

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Individual on-transect densities of oldsquaws

on transects in the Jones-Return and

Stockton-Maguire-Flaxman islands areas,

central Alaska Beaufort Sea, 5 June to 23 September,

1977-1984 and 1989-1991. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

SYSTAT multiple regression analysis

output for the complete season in the central

Alaska Beaufort Sea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

SYSTAT multiple regression analysis output

for the oldsquaw molt period in the central

Alaska Beaufort Sea. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Plots of multiple regression residuals vs. the

various independent variables used in the

complete season and molt period multiple

regression analyses. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Potential disturbance levels assoaated with

industrial activities recorded on or near transects

in the Industrial and Control study areas

in 1990 and 1991. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

Relationships between transformed oldsquaw density

[ln(oldsquaw density+ l)] on transects in the Industrial

and Control study areas vs. transformed wave height

in 1990 and 1991, disturbance in 1990, transformed day

of season in 1990 and 1991, transect location in 1990-1991,

transformed wind direction in 1990-1991, and

wind speed in 1990-1991 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Computed ANCOVA cell means for individual

transects in different habitats and study areas

sampled in 1990 and 1991 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

Computed adjusted mean densities of oldsquaws

in Control and Industrial study areas in 1990 and 1991 . . . . . . . . . . . . . . . . . . . . . 113

Numbers and percentages of oldsquaws counted during

aerial surveys in nearshore waters of the central Alaska

Beaufort Sea, 1977-1984 and 1989-1991 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
Abstract ix

ABSTRACT

The purpose of this project was to design and test a monitoring

protocol for marine birds in the Jones-Return islands area of the central

Alaska Beaufort Sea. Because of its overwhelming and widespread

abundance, relatively sedentary behavior, ease in counting, and the extensive

historical database, theoldsquaw duck (Clangula hyenudis)w asselectedasthe

focal species for this study. Two null hypotheses were formulated concerning

potential changes in the numbers and distribution of oldsquaws in relation to

petroleum development in the Industrial area, compared to a Control area

located about 50 km to the east.

A 9-year historical database was analyzed using multivariate

techniques to determine which of several predictor variables recorded during

past aerial surveys were significantly related to oldsquaw density in the study

areas. Separate analyses were conducted for the 5 June to 23 September

period, and for the mid July to late August-early September molt period of

oldsquaws. The results of the two multiple regression analyses indicated that

about 57% and 6870, respectively, of the total variation in oldsquaw density

during the two periods could be explained by predictor variables recorded

during aerial surveys. Predictor variables representing habitat, day of the

year, time of day, amount of ice, and wave height recorded on transect during

the survey were most closely associated with oldsquaw density.

Measurement error and influences outside the study area no doubt also had a

strong influence on the results.

Based on results of regression analyses of historical data, an intensive

program of aerial surveys and an analysis of covariance (ANCOVA) statistical

procedure were designed and tested in 1990 and 1991. The 2-year testing phase

of the study resulted in several revisions to the originally proposed sampling

procedures, to the survey schedule, and to the recommended statistical

procedures. Results of the ANCOVA indicated that, during the 2-year testing

phase of the study, there was no evidence of a change in oldsquaw densities

that may be attributable to disturbance in the Industrial study area. Other

analyses indicated that the revised sampling and analysis procedures would

be adequate to detect long-term trends in oldsquaw density and localized

disturbance effects, but that the monitoring program needs to be continued

well beyond the two years of the current study.
x Acknowledgements

ACKNOWLEDGEMENTS

This study was funded by the U.S. Department of Interior, Minerals

Management Service (MMS), Alaska OCS Region, Anchorage, Alaska. We thank

Steve Treaty, Cleve Cowles, and Jerry Imm of MMS for their help during this

study. We also thank Rod King, U.S. Fish and Wildlife Service, Office of

Migratory Bird Management, Fairbanks, Alaska, for kindly providing suxnmaries

of aerial breeding pair surveys of oldsquaws on the Arctic Coastal Plain of Alaska.

Most of LGL’s earlier studies of marine waterbirds in the central Alaska

Beaufort Sea were funded and supported by the U.S. Department of Commerce,

National Oceanic and Atmospheric Administration (NOAA), and the U.S.

Department of Interior, Bureau of Land Management (BLM) and MMS, through

the Outer Continental Shelf Environmental Assessment Program (OCSEAP).

Other studies were supported by Shell Oil Company, and by ARCO Alaska Inc.

for the U.S. Army Corps of Engineers. We thank R.B. Metcalfe and N.L. Space,

Spill Response Specialists with ARCO Alaska, Inc., Kuparuk Operations, for

providing information on industry activities in the Industrial study area.

Rolph Davis, Alan Birdsall, Joe Truett, Benny Gallaway, Tom Wetmore,

John Richardson, Scott Anderson, Josh Baker, DeClan Troy, Lynn Maritzen, Brian

Harvey, and many others at LGL provided management and/or technical

assistance during some of our government and industry sponsored studies of

marine birds in the Beaufort Sea.

Many people worked in the field during 1977-1984 on the various studies

of marine birds; we especially thank Chip Welling, Jim Helmericks, Gary

Searing, John Richardson, Joe Truett, William Griffiths, Peter Craig, Rob

Dillinger, Doug Scharnel, Dale Herter, Phil Martin, Bob Burgess, Declan Troy,

Dave Rudholm, and Craig Hohenberger. Jim Helmericks and his family

provided aircraft and logistic support during many of our studies along the

Beaufort Sea coast. Jim Helmericks, Joe Truett, Ron Ridout, Derek Helmericks,

and Dave Wiggins helped with the aerial surveys in 1989-1991.

Joe Truett, Randall Howard and Bob Pollard were instrumental in

recovering some of the historical aerial survey data. We especially thank Peter

Wainwright, Gary Searing, and Dave Wiggins for working tirelessly to help

reformat, reorganize, and clean-up the historical aerial survey data.

John Richardson (LGL Limited) contributed significantly to the

experimental design for this study and suggested improvements to this report;

he also advised on some statistical matters. Rolph Davis also helped design the

study, and has helped in the management of the project. William Griffiths

helped obtain missing weather data for some survey periods and gave advice

regarding weather and oceanographic influences (seasonal upwelling potential)

in the study area. Karen Doiron was instrumental in the production of this

report.
Introduction 1

INTRODUCTION

In this introduction and the following section of the report, we review

the purpose and objectives of this study and provide background information

about the original design phase of the study (Johnson 1990). In the last half of

the report we present results and interpretations from the 2-year testing phase

of the study.

The Outer Continental Shelf Lands Act (OCSLA) and its amendments

include provisions for [1) post-lease monitoring studies to provide

information that can be compared with any previously collected information

in order to identify significant changes in quality and productivity of leased

environments, (2) establishing trends in the areas studied and monitored,

and for (3) designing experiments to identify the causes of such changes.

The purpose of this project was to design and test a monitoring

protocol for marine waterbirds, principally marine waterfowl, in nearshore

waters of the Alaska Beaufort Sea: waterfowl are abundant in this area

(Johnson and Richardson 1981, Johnson and Herter 1989). The need for such

a protocol is most urgent in the central Beaufort Sea area (e.g., Jones-Return

islands area), where nearshore exploration and coastal development have

occurred over the past decade, and are likely to escalate in the future.

In late September 1983, a MMS/NOAA-sponsored workshop was held

in Girdwood, Alaska, to develop a monitoring strategy for the Alaska

Beaufort Sea (Dames and Moore 1984). The concept of monitoring Beaufort

waterbirds is based on the following conclusions of the 1983 workshop:

Marine birds are abundant and are a biologically and socially important

component of the nearshore Beaufort Sea ecosystem.

Some species of Beaufort Sea marine birds, especially marine waterfowl

such as the oldsquaw duck (Chznguhz hyenuzlis), are ubiquitous,

relatively easy to detect and count, and have been well studied prior to

industrial development; therefore they are appropriate candidates for

monitoring.

A monitoring protocol should be designed to insure that industryrelated influences on marine birds are discernible from other natural
Introduction

influences, i.e., should involve a rigorous design and statistical

approach that includes both experimental (Industrial) and Control

areas and draws on all relevant historical information collected in the

study area.

The 1983 workshop identified several potential waterbird species for

monitoring. The oldsquaw duck was selected by the workshop over other

species because it is the most abundant and widespread local waterbird in the

nearshore Beaufort Sea, the zone where virtually all exploration and

development have occurred in the Beaufort marine system. Data presented

at the workshop confirmed that during the summer open-water period

oldsquaws represent most of the avian biomass in the nearshore Beaufort

environment. During July and August, when they molt their feathers, they

are flightless and thought to be particularly vulnerable to water-borne

contaminants and disturbances. They are overwhelmingly the most

abundant species of bird in the study area throughout the open water period

(Fig. 1). No other species of waterbird is present in sufficient numbers in the

study area for a period of time long enough to be considered a suitable

candidate for monitoring. Nevertheless, we have reviewed and compared

the suitabilities of other possible candidate species for the monitoring

program.

Several hundred Pacific eiders (Sornateria rnollissinza v-nigra), Arctic

terns (Sterna paradkaea), glaucous gulls (Larus hyperboreus), and brant

(Branta bernich) nest and rear their young on barrier islands in the central

Alaska Beaufort Sea, but their numbers are too few (eiders, terns, gulls, brant),

their distributions too clumped (eiders, gulls, terns, brant) and their habits too

secretive (eiders, brant, terns) for them to be considered suitable candidates for

monitoring without intensive ground-based monitoring.

Thousands of juvenile phalaropes and hundreds of Arctic terns and

glaucous gulls move to barrier island habitats to feed starting in early August,

prior to their southward fall migration (Johnson and Richardson 1981). But

the year-to-year variations in the numbers of these species encountered in

Beaufort Sea habitats are great (Johnson and Richardson 1981, Johnson 1984c).

In years with high reproductive success at tundra nesting locations, there are

many juvenile phalaropes, gulls and terns along the barrier islands in August
Ilzfroducfim’z 3

1977 1978 1979 1980 1981 1982 1984 1989 1990 1991

Year of Study

Figurel. Oldsquaw sightings as a percentage of all waterbird sightingsin

the central Alaska Beaufort Sea, 1977-1982, 1984, and 1989-1 991.
Introduction

and early September; in years of bad production, there may be few. This is

also true for eiders and brant.

Consequently, although these species are often detected and recorded

during ground and air based investigations, their numbers can be highly

variable, reflecting aspects of their life history not associated with activities in

coastal barrier island-lagoon and nearshore habitats.

On the other hand, tens of thousands to hundreds of thousands of

molting male oldsquaws use lagoon and nearshore habitats along the

Beaufort Sea coast during mid-July through late August or early September,

regardless of high or low production of young in tundra habitats. They

congregate at locations where there is protection from wind, waves and

moving ice and where there is abundant food to support them while they

replace virtually their entire plumage. During this molt period oldsquaws are

flightless for nearly a month (Johnson 1982a,b; 1983; 1984a,b; 1985). Thus,

oldsquaws are relatively sedentary during the July-August peak of the molt

period, and are relatively easy to count at this time. OCS-related activities in

nearshore environments are more likely to affect, in a consistent and

measurable manner, the local and general distribution and abundance of

oldsquaws than of the other species mentioned above.

In a monitoring program such as this one, it is essential to focus on the

species that offer the best chance of detecting changes related to development

in the area of interest. Such speaes should be present in the areas of concern

for a reasonable period of time, should be abundant and widespread, and

should be relatively easy to count reliably.

Furthermore, the biology of the focal species should be well enough

understood to allow separation of natural variability in numbers and

distribution from man-caused variability. There are few specks in the

nearshore Beaufort Sea that fit these criteria. The prime candidates are (1)

oldsquaws in most nearshore habitats during July and August, (2) the

phalaropes along barrier island beaches during a 10 to 20 day period in

August, and (3) glaucous gulls primarily along barrier island beaches during

mid-August through mid-September (Johnson and Richardson 1981). Only

the oldsquaw is present in sufficient numbers in most nearshore habitats

throughout the study area for a period long enough to be sufficient for

monitoring.
In froducfion 5

Research Hwotheses

A monitoring program that is designed to detect the influences of

industry activities on nearby birds must test speafic hypotheses that relate to

(1) the birds chosen to be monitored, and (2) the types of industry activities in

the study area. The following null hypotheses were constructed with such

factors in mind:

HOI: There will be no detectable change in relative densities of molting

male oldsquaws in selected Beaufort Sea index areas.

H02: Changes in male oldsquaw distribution patterns are not related to

OCS oil and gas development activity.

Hypothesis (1) relates to the possibility of a large-scale and long-term

change in relative densities in Industrial vs. Control study areas. Hypothesis

(2) concerns relationships between oldsquaw densities and short-term

localized variations in human disturbance. These two hypotheses, as

presented by LGL at the Beaufort Sea monitoring workshop (Dames and

Moore 1984), were constructed after six years of aerial surveys and

supplemental research on the distribution, abundance and behavior of

marine waterfowl, mainly oldsquaws, in the Jones-Return islands area of the

Alaska Beaufort Sea, During this period, aerial survey procedures were

modified to improve the distribution and resolution of sampling, and the

surveys were continued through 1984, thus establishing a 7-year base of useful

information on the distribution and abundance of oldsquaws (primarily

molting males) in and adjacent to the Jones-Return islands area.

At the start of this study we adopted the above two null hypotheses as

our working hypotheses, and we outlined an approach to test the hypotheses

by developing and testing a monitoring protocol that was based on a series of

low-level aerial surveys of oldsquaws (Johnson 1990). The design took into

account all historical information and was based on the premise that there are

several complex and interactive natural variables (i.e., migration schedules of

birds, time of year, time of day, wind speed and direction, presence or absence

of a barrier island nearby, distribution of ice, wave height, etc. ) that

significantly influence the behavior of these birds, and therefore significantly
Introduction

influence the results of aerial surveys of them in the nearshore Beaufort Sea

area.

The specific objectives of the design phase of this pr~ject were to (1)

conduct another year of surveys (1989) using procedures tentatively planned

for monitoring, (2) analyze existing historical data (including 1989) to assess

factors to be taken into account for the monitoring program, and (3) design

the monitoring approach to be used in following years (i.e., 1990 and 1991).

The full-scale monitoring program was tested in 1990 and 1991.

Systematic aerial surveys were conducted along established transects in

different habitats in the Industrial and Control study areas in both years. This

type of experimental design, involving a series of replicate surveys of an

experimental and control area over several years before and after human

perturbations, is consistent with procedures recommended by Green (1979)

and Underwood (1991 ) for detecting human environmental impacts in

natural populations. The following sections describe (1) the most important

details of the originally proposed monitoring protocol (from Johnson 1990),

and (2) results and recommendations arising from the testing phase of the

study.
DEVELOPMENT OF A MONITORING PROTOCOL

Development of Monitoring Protocol

Studv Areas

The Tones-Return Islands Industrial Area

The terms-of-reference identified the Jones-Return island chain, west

of Prudhoe Bay, Alaska, as the Industrial study area for this study (Figs. 2 and

3). These islands have remained relatively undeveloped over the past two

decades although there has been significant oil and gas exploration and

development on the adjacent mainland tundra.

The Stockton-Maaire-Flaxman Islands Control Area

The Stockton-Maguire-Flaxman islands area (Figs. 2 and 4), located

about 50 km east of the Industrial area, was selected as the Control area for the

present monitoring study. The area is similar in structure and size to the

Industrial area, it is used extensively by oldsquaws and other waterbirds, and

there was a base of historical aerial survey data for use in statistical analyses

and comparisons.

The Control area is situated along a part of the Beaufort Sea coast

where very little coastal or nearshore industrial activity has occurred.

Although several oil wells have been drilled during winter on or adjacent to

a few of the islands in the Control area (e.g., Challenge Island), and on the

adjacent mainland tundra (e.g., Pt. Thompson), the area is relatively pristine

and undisturbed compared to the Industrial study area.

Retrospective Assessment of Variables Affecthw Oldsauaw Densities

Several important and relatively well understood variables were

thought to influence the number of oldsquaws present in the two study areas.

Based on the results of earlier studies, we selected relevant predictor variables

for use in a multiple regression analysis which allowed us to determine

quantitatively which variables were most closely related to the densities of

oldsquaws recorded on aerial survey transects during 1977 through 1989.

7
Development of Monitoring Protocol

/ 7

(f$,,

c.

ISLANDS “’LURVP [ :

.-

n (7 . ‘J

—
~---—- --~

‘090’

.,. .,,

5 0 5 10 KILOMETERS

OEPTH IN FEH

7V”30’

Figure 3. Industrial study area in the Jones-Return islands area, central

Alaska Beaufort Sea. The West Dock (ARCO) Causeway is at the far

easternendsof transects 23 through 25.
Development of Monitoring Protocol

I

m.

I

,imw

,U?.w
Developnenf of Monitoring Protocol 11

Each of the relevant predictor variables (independent variables)

selected for use in the multiple regression analysis of oldsquaw density on

transects in the study areas is discussed below, and a brief rationale is given

for its inclusion in the analyses.

1. Year of study. Earlier studies (Johnson and Richardson 1981) and

subsequent analyses in this study (Append. 1) have shown that densities of

oldsquaws on specific transects in the Jones-Return islands area varied

considerably from one year to the next. Consequently we included a YEAR

term in the analyses to test for a long-term linear trend.

2. Time of the year (day of the season) that sampling occurred.

Previous studies clearly showed that use of neamhore habitats by oldsquaws

and other marine birds was highest during the summer open water period

(Johnson and Richardson 1981). Numbers and densities were consistently

high during the month-long period from mid-July to mid- to late August

when male oldsquaws congregated in nearshore lagoons to molt (Johnson

1985:Fig. 6; Garner and Reynolds 1986:129). Consequently we included a DAY

term in the multiple regression analyses. A second order term (D AY2 =

DAYTRAN) was also included to allow for a possible non-linear relationship

to this variable.

3. Time of day that sampling occurred. Long-term, continuous

observations in the Jones-Return island area showed that in undisturbed

situations molting oldsquaws exhibited a 24-hr cycle of distribution,

abundance and behavior in barrier island-lagoon habitats (Johnson 1982, 1983,

1985) (Fig. 5). We included TIME and TIMEZ terms in the analysis.

4. Water depth in the sampling area. Studies of prey density and

feeding behavior of oldsquaws in the Jones-Return islands area indicated that

they fed preferentially in the shallow nearshore lagoons. These studies also

showed that the invertebrate prey of oldsquaws was most abundant in the

deeper parts (2-3 m) of the lagoons, and that oldsquaws fed more efficiently

(had more food in their stomachs) in areas of the lagoon where invertebrates

were abundant (Johnson 1984a; Griffiths and Dillinger 1981) (Table 1). Thus, a

water D E P T H term was included in the multivariate analysis.
12 Development of Monitoring Protocol

1.0

8

 0“2 . . . . . . . . . . g ~.

-J-J “

o

E

5

-0.6 .

4

-0.8:

o 6 12 18 24 30

HOURS

0.8

0.6

0.4

0.2

0.0

-0.2

-0.4

-0.6

-0,8

-1,0 : 1

:

I I I 1 8

0 6 12 18 24 30

HOURS

Figure 5. Results of autocorrelation analyses of numbers of oldsquaws in

various barrier island-lagoon habitats during four 30-hr cycles (120 hrs) of

activity when oldsquaws were relatively undisturbed and when waters were

calm. Lag (hour) correlations of numbers of oldsquaws in beach, nearshore

and total lagoon habitats showed a 24-hr period; numbers in offshore habitats

showed little periodicity (from Johnson 1982a).
Table 1. Summary of water depths and feeding efficiencies of oldsquaws in the

Jones-Return islands area, Beaufort Sea, Alaska (Johnson 1984a).

1977 1978

All Oldsquaws Feeding Oldsquaws All Oldsquaws

(n=77) (n=81) (n=108)

Water

Depth (m) 2.09MI.178 2.07MI.179 2.05Ml.172

Spearman All Inverts. Mysids Amphipods

Correlation of

prey in Stomach vs. Spearman r 0.68 0.34 0.02

prey in Habitat P <0.001 0.02 0.1

(g. dry wt.) n 25 25 25

‘%
Development of Monitoring Protocol

5. Location of the sampling area along an east-west axis. Although

oldsquaws congregate in barrier island-lagoon habitats along the entire

Beaufort Sea coast where suitable habitat exists, earlier studies showed that

densities of oldsquaws were consistently higher in some parts of the study

area compared to others (Johnson and Richardson 1981; Johnson 1984b;

Garner and Reynolds 1986). As a consequence, we subdivided the coastline,

including the two study areas, into 12 west-east subdivisions (1 = transects at

the far western end of the Industrial area, and 12 = transects at the far east end

of the Control area), and included a WESTEAST term in our analyses.

6. Proximity of sampling area (transect) to a barrier island. Earlier

studies, and preliminary analysis of data in the present study, indicated that

the numbers and densities of oldsquaws and other waterbirds were generally

greater on transects close to barrier islands compared to other transects more

distant from barrier islands (Fig. 6)(Johnson 1985; Johnson and Richardson

1981; Brackney et al. 1985:350). It was not surprising that 95% confidence

limits of means were generally lower during years when large numbers of

transects were sampled (i.e., 1989-1990, Fig. 6). Two predictor variables

included in our analyses relate to proximity of the sampling area to a barrier

island. One measure (DIST) is the absolute value (on a continuous scale) of

the average distance of the transect from the nearest barrier island.

The other measure (HABITAT) is composed of five categories of

habitats: Habitat 1 is immediately lagoonward of the barrier islands; Habitat 2

is mid-lagoon; Habitat 3 is immediately lagoonward of the mainland

shoreline; Habitat 4 is 1.5 km seaward of the barrier islands; Habitat 5 is 5 km

seaward of the barrier islands (Figs. 2 and 3). In the multivariate analysis

HABITAT was a categorical variable represented by a set of four dummy

variables (see Wilkinson 1987; Draper and Smith 1981). Habitat 1 was the

standard against which others were compared; analysis results were combined

into a single F-ratio (with 4 d.f. rather than the usual 1 d. f.) reflecting the

overall effect of habitat on oldsquaw density. The HABITAT, DEPTH (water

depth), and DIST (distance of transect from the barrier island chain) variables

were interrelated, and the final regression analyses included only the single

categorical variable HABITAT.
Development of Monitoring Protocol 15

260

e

4

!!

Onlv Barrier-Island Transects

12-

11-

10-

9-

8-

7-

6

-

331

5

4

:.8

3-

2-

1-

t

I I 1

8

84

8

All Molt 1977 1978 1979 1980 1981 1982 1983 1984- 1989 1990 1991

Yeax of Study

Only Non Barrier-Island Transects

All Molt 1977 1978 1979 1980 1981 1982 1983 1984” 1989 1990 1991

Year of Study

Figure 6. Mean ~ 959o C.I. (confidence intervals) of transformed densities

[ln(l+density)] of oldsquaws on all barrier island ~ansects (Habitat 1) ~d

non-barrier island transects (Habitats 2-5) during the molt period (15 July21 August) in the central Alaska Beaufort Sea, 1977-1984, and 1989-1991.

Also shown, at left, are values for all years combined during (A) all survey

periods (5 June to 23 September), and (B) during the molt period alone.

Sample sizes (numbers of transects) are given for ea~ set of surveYs.
Development of Monitoring Protocol

7. Wind speed and direction in the sampling area during the sampling

period. Prevailing winds in the central Beaufort Sea during the summer are

northeasterly or northerly (Brewer et al. 1977). Earlier studies indicated that

oldsquaw densities on different transects in the Jones-Return island area are

significantly related to the speed and direction of wind during aerial and

ground-based sampling (Johnson and Richardson 1981). Wind speed (WSPD)

was measured in km/hr, and wind direction was measured on several scales

– a 360° scale (WDIR = COSWDIR), and on an ordinal octant scale (ORDWND).

Two other continuous measures of wind speed and direction were included

as predictor variables, the northern component (NCOMWND = wspd x

cos(wdir)) and northeastern component (NE C O M W N D = wspd x

cos(wdir+45)) of the wind. Since all of these variables overlapped to some

extent, we used only wind speed (WSPD ) and transformed wind direction

(cosine of degrees = WDIR) in the final analyses.

8. Percent ice-cover on-transect in the study area during the sampling

period. Earlier studies indicated that densities of oldsquaws during the male

molt period in the Jones-Return islands area were higher in lagoon habitats

where there was consistently less ice on-transect than in areas seaward of the

barrier islands (Johnson and Richardson 1981). Consequently, we included

the estimated percent ice cover on-transect (LN of percent ice = ICE) as a

predictor variable in the multiple regression analysis.

9. Wave height on-transect in the study area during the sampling

period. Wave height on transects in the sampling area is directly related to

the direction and speed of wind, which were discussed previously as separate

predictor variables. But wave height also has a significant influence on the

detectability of oldsquaws and other marine birds swimming on the water,

and thus affects the apparent density of oldsquaws recorded on-transect.

Wave height is a standard measurement recorded during LGL’s aerial

surveys; consequently we included it as a separate predictor variable

(WAVEHT) in our analyses.

10. Study Area. There are two distinct sampling areas in this study

(Industrial and Control areas). As a result, analyses included a single dummy

variable (Wilkinson 1987; Draper and Smith 1981) reflecting the overall effect

of the particular study area within which the transect is located.
Development of Monitoring Protocol

Results of Multiule Re~ession Analvses

17

Two separate multiple regression analyses were conducted on the 1977-

1984 and 89 data (Table 2). One analysis considered the complete study period

using all available data for all transects surveyed on any date (5 June to 23

September) during all years of study (1977 to 1984, and 1989). Only complete

sets of data, i.e., no missing variables, were used for this analysis. A similar

analysis was done using data only from the oldsquaw molt period (15 July to

about 15 to 20 August) during all years of study. Each survey of each transect

constituted a unit of observation.

The multiple regression analysis for all dates and years (complete study

period) showed a relatively strong relationship between the predictor

variables and the density of oldsquaws (n = 474 transects, multiple R =

0.76) (Append. 2); several of the predictor variables were nominally significant

as predictors of the dependent variable DENSTRAN. About half (multiple R2

= 0.57) of the variation in oldsquaw density for the complete open-water

period was accounted for by the predictor variables and interaction terms used

in this multiple regression analysis (Table 2).

Results of the multiple regression analysis of data from the oldsquaw

molt period (15 July-20 August) were very similar to those from the overall

study period. There were strong relationships between several of the

predictor variables and the density of oldsquaws (n = 275 transects, multiple R

= 0.83) (Append. 3). About two-thirds (multiple R2 = 0.68) of the total

variation in oldsquaw density was accounted for by the variables and

interaction terms used in the present multiple regression analysis.

Wave height, habitat type, day of year, day of year x habitat interactions,

time of day x habitat interactions, and ice cover x habitat interactions all were

important (and statistically significant) variables that helped predict oldsquaw

density in the study areas. Analysis of residuals helped determine whether

necessary assumptions of multiple regression analysis were met (normally

distributed residuals, appropriate transformation of data, homogeneity of

variance, linearity of relationships, etc.) (Append. 4).
Table 2. Summary of results of multiple regression analyses of historical oldsquaw density data collected in the

Jones-Return islands area, Beaufort Sea, Alaska, during 1977 to 1984 and 1989’.

5 June to 23 September 15 July tO 15 August

(Squared Multiple R = 0.574, n =474) (Squared Multiple R= 0.6S1, n = 275)

Independent Degrees of Coefficients F-Ratios Nominal P Coet%cients F-Ratios Nominal P

Variables Freedom Values Values

CONSTANT

YEAR

DAYI’RAN

TIME

WESTEAST

WSPD

WDIR

lR WSPD *

ICETRAN

WAVETRAN

IH4’$t

AREA

HABITAT(l-5)*DAYTRAN

HAEmAT(l-5)KrIME

HABITAT(l-5)”WSPD

HABITAT(l-5)*WDIR

I-IABITAT(l-5)*WAVETRAN

N/A

1

1

1

1

1

1

1

1

1

1

4

1

1

4

4

4

4

4

4

-2.179

-0.038

0.097

-0.001

0.001

4.054

0.003

-0.003

0.000

-0.047

-0.376

-0.229; 3.911; -4.105; 1.513

-0.010

0.016

-0.000; 0.000; 0.000; 0.000

0.002; -0.003; 0.002; -0.002

0.004; -0.006; 0.062; -0.044

0.001; 0.000; -0.002; 0.004

0.227; 0.329; -0.754; 0.080

-0.357; 0.001; -0.051; 0.119

N/A

1.30

19.62

0.15

1.02

0.52

0.00

0.83

1.68

0.11

6.81

4.22

0.00

0.12

2.38

13.62

2.01

0.91

2.36

2.32

N/A

0.256

Sf!J?QE

0.702

0.314

0.474

0.965

0.362

0.196

0.745

-0009”’

QIQQz

0.989

0.732

0.051

<0.001 **

0.093

0.457

0.052

0.057

-1.269

-0.076

0.099

-0.001

0.001

-0.128

4.025

-0.007

0.000

0.374

-0.457

-1.645; 3.244; 2.415; 3.25B

-1.121

0.153

0.000; 0.000; -0.000; -0.001

0.002; 4.003; 0.001; -0.000

0.003; 0.004; 0.034; -0.077

0.003; 0.003; -0.002; 0.000

0.359; 0.198; -1.030; 0.210

4.322; 1.181; 0.195; -0.892

N/A

3.30

0.17

0.08

1.34

1.89

0.17

2.86

7.44

1.51

6.63

1.58

1.38

7.36

1.20

7.34

1.44

0.67

1.16

3.66

N/A

0.070

0.678

0.774

0.248

0.170

0.680

0.092

JLQQz2

0.220

0.021**

0.180

0.241

Jz!2QzU

0.310

< O.001**

0.221

0.616

0.329

!21QzE

* See Appendices 2 and 3 for a complete listing of the regression models and analysis of variance tables.

** Nominal P values s 0.050 were considered to be statistically significant.

*** Habitats are as follows 1 = S of barrier islands, 2 = mid-lagoon, 3= mainland shoreline, 4 = nearshore marine, and 5 = offshore marine. Habitat 1

is omitted because it is the ‘standard’ against which others were compared in this analysis.
Development of Monitoring Protocol 19

Influences That Mav Affect Oldsauaw Densitv

The overall proportions of variation explained by the predictor

variables in the two multiple regression analyses (R2) were about 0.57 for the

complete study period, and 0.68 for the molt period. These values, although

somewhat less than hoped for, are better than might be expected considering

that field data collected for purposes other than long-term monitoring were

analyzed retrospectively. Nevertheless, the R2 values were substantially less

than 1.0, indicating that factors other than those allowed for in the regression

models were affecting oldsquaw distribution, abundance and density. These

additional factors probably included (a) natural variability in waterfowl

behavior, (b) measurement error, and (c) unmeasured variables (inside and

outside the study area).

Measurement error is an unavoidable component in many of the

variables used in this study, especially environmental variables such as

amount of ice, wave height, wind direction and speed, etc., estimated ontransect during the course of aerial surveys. This type of error is somewhat

reduced when experienced observers conduct aerial surveys, but some

measurement error is inherent in any sampling program, especially one

conducted from a fast-moving aircraft flying at low level.

A shortcoming of earlier aerial surveys was that there was no record of

the level of human activity and disturbance on transects, aside from the

obvious, but often unrecorded, presence or absence of a major structure, such

as an artificial island, causeway, or drilling structure.

Another major confounding factor is the degree to which the

distribution and abundance of oldsquaws may be determined by influences

outside the study area, and therefore not measurable in a local or regional

monitoring program such as this one. This potential source of variation may

have a significant influence on the distribution, estimated abundance, and

density of oldsquaws in both study areas. Most waterfowl are highly

traditional in their behavior, e.g., they often nest, molt, migrate and overwinter in the same general area from one year to the next (Hochbaum 1955;

Lokemoen et al. 1990). There is uncertainty, however, about whether

oldsquaws occupy the same barrier island-lagoon systems from one year to

the next, or about the degree of movement of birds from one nearshore area

to the next. Radio telemetry studies of oldsquaw movements in barrier
Development of Monitoring Protocol

island-lagoon habitats in the Arctic National Wildlife Refuge, Alaska, showed

that molting oldsquaws moved an average distance of 0.69 km/day& s.d. 1.29,

and ranged from 0.01 to 3.85 km/day (Brackney et al. 1985). All movements of

these radio-tagged oldsquaws were local and within or adjacent to the lagoon

in which the birds were originally captured.

Nevertheless, the 500 km long chain of barrier islands, lagoons, bays

and large freshwater lakes along the Beaufort Sea coast from Point Barrow,

Alaska, to Cape Parry, NWT, Canada, are all used by marine waterfowl,

especially oldsquaws, but also by smaller numbers of scoters (Melarzitta spp.),

red-breasted mergansers (Mergus serrator), and Pacific eiders during the postbreeding molt period. There is little or no information that describes how

various environmental and biological factors interact to determine which

areas may be used by molting oldsquaws from one year to the next. The molt

migration by oldsquaws is generally westward along the Beaufort Sea coast in

late June through mid-July (Johnson and Richardson 1982). Influences such

as (1) amount of ice present and timing of ice break-up in a particular barrier

island-lagoon system, (2) oceanographic conditions along one part of the coast

in relation to another, and (3) weather during the molt migration (e.g.,

presence of favoring tailwinds versus headwinds), may all influence whether

some or all birds from one particular area may be attracted to a lagoon during

the molt period. The scale of such events is too large to be accounted for in a

monitoring program conducted along only one part of the Beaufort Sea coast;

a coast-wide study would be necessary to determine if there are large-scale

changes in abundance of oldsquaws (or any other of the widely distributed

species) in one part of the Beaufort Sea relative to another.

We suspect that such factors not included in the regression analyses

may have had a significant influence on the numbers and densities of

oldsquaws recorded during past years in central Beaufort Sea barrier islandlagoon systems. Although some of these influences would be difficult (if not

impossible) to measure, it was thought to be possible to design a monitoring

program so that much of the remaining variability could be accounted for.

The proposed implementation plan for such a program is described below.
Development of Monitoring Protocol

Prouosed Imdementation of a Monitorirw Protocol

21

Desire Considerations

Results from earlier studies (Johnson and Richardson 1981; Troy and

Johnson 1982), and from the multivariate analyses described above, have

indicated that some of the variation in apparent oldsquaw density is

attributable to sighting conditions, as influenced by wind, sea state, ice cover,

sun glare, etc. Additional variation may be attributable to local variations in

human activities and disturbance within the areas designated as either

Industrial or Control. It is important to account for the causes of as much of

this variation as possible in order to maximize the power of the statistical

procedures to identify the presence and magnitude of any industrial effects,

either broad-scale (i.e., Industrial Area vs. Control Area) or fine-scale

(tianse~-to-transect within one or both study areas). Consequently, in future

analyses associated with the Beaufort Waterbird Monitoring Protocol, one or

more additional independent variables that were which have been absent

from all earlier analyses need to be included. One variable should be “Level

and type of industrial activity on or near the transect during an aerial

survey.” We categorized these activities into five levels of possible

disturbance (Table 3); a value is assigned separately to each transect on ea~

survey date.

R was also thought that additional meteorological and oceanographic

factors could influence the distribution, abundance, and movements of

oldsquaw ducks. Factors such as seasonal upwelling potential and/or

seasonal mean wind speed and direction (Craig et al. 1984; LGL 1990a,b; LGL et

al. 1990a,b), which are known to influence the distribution, abundance and

movements of invertebrates and anadromous fish in the nearshore Beaufort,

may also need to be considered in the interpretation of oldsquaw data in

future analyses.
22 Development of Monitoring Protocol

Table 3. Ordinal scale for recording types of industry activities and disturbance levels

that may affect oldsquaw densities in the Jones-Return islands, Beaufort Sea, Alaska.

Values are assigned separately for each transect during each survey date.

Activity Disturbance Type of Industry Activity

Index Level

1 Nil

2 Low

3 Moderate

4 High

5 Extreme

No human activity or disturbance in area of interest.

Infrequent low-level aircraft overflights, boat traffic or human activity

on land or in the water during the survey period in the area of interest.

Regular** low-level aircraft overflights, boat traffic or human activity

on land or in the water during the survey period in the area of interest.

Frequentt low-level aircraft overflights, boat traffic or human activity, and/or

spillage of low levels of toxic materials (oil, fuel) and associated

clean-up activities on land or in the water during the survey period in the

area of interest, and/or semi-permanent structures established in the area

with frequent presence of humans and associated activity.

Major spill of toxic materials (oil, fuel) and associated clean-up

activities on land or in the water during the survey period affecting a large

area, including the area of interest, and/or permanent structures established

in the area with near-continuous presence of humans and associated

activity.

“ Less than five known occurrences during the 24-h survey period. Low-level overflights 500’ altitude.

** Five to nine known occurrences during the 24-h survey period.

+ Tenor more known occurrences during the 24-h survey period.
Development of Monitoring Protocol 23

Samdhw Procedures

It was clear that the need for powerful analytical approaches in the

monitoring program would necessitate the use of field sampling procedures

that would satisfy the requirements of those analysis methods. We planned

the 1990 and 1991 sampling in such a way as to obtain data for the following

spatial and temporal categories:

Two study areas (Industrial and Control).

Five habitat strata characterized by proximity of the sampling area to

barrier islands (e.g., 0.40 km south of barrier islands, 0.41 -1.61 km

south and north of barrier islands, and 1.62 km -4.83 km south and

north of barrier islands). These distances are equivalent to (1) barrier

island habitat (within 0.40 S of barrier islands), (2) mid-lagoon habitat

(variable distances S of barrier islands), (3) mainland shoreline habitat

(variable distances S of barrier islands), (4) inshore marine habitat (1.5

km north of the barrier islands), and (5) offshore marine habitat (5 km

north of the barrier islands).

Four contiguous (end-to-end) transects within each habitat stratum and

study area.

Four 5- to 7-day sampling periods during each year.

Three survey dates within each 5- to 7-day sampling period.

It was thought that this sampling hierarchy would provide the replicated and

structured data necessary to isolate the effects of the variables known to affect

bird densities. This experimental design was compatible with the powerful

ANOVA and ANCOVA statistical procedures that we proposed to use to

separate the effects of year, date, time, east-west location, study area, habitat,

amount of ice, wind, wave height, and level of disturbance, etc.

Although our original proposal did not recommend that mainland

shoreline habitats be sampled as part of the proposed monitoring, we later —

after the retrospective analyses were done — recommended that, within each
24 Development of Monitoring Protocol

of the study areas, a habitat stratum along the mainland shoreline be added to

the monitoring protocol. Relatively large numbers of oldsquaws (and other

marine birds) use mainland shoreline habitats sporadically during the

proposed sampling period, especially in the Control study area where various

small lagoons and spits along the mainland coast provide suitable sheltered

habitats for oldsquaws. Furthermore, birds in these mainland coastal areas

could well be the first to be affected by industrial activities; development

probably will occur along mainland shorelines areas before it does so on the

adjacent barrier islands. Also, changes in density along the mainland shore

may be important in understanding simultaneous changes in numbers in

other habitats.

Schedule of Survevs

Based on the results of earlier studies and on the results of the

regression analyses described above, the appropriate period for surveys of

marine birds in both Beaufort study areas (Industrial and Control) was from

mid-July until late August or early September, i.e., during the oldsquaw molt

period. Surveys during this period would sample only flightless male

oldsquaws. We recommended that four survey periods be scheduled during

the molting season at about 8-10 day intervals, starting on or about about 15

July. All transects were to be surveyed three times during each of four 5- to 7-

day survey periods. These repeated measurements were to provide the three

replicate surveys during each sampling period that are needed for variance

computations for each period.

We also recommended that surveys be conducted as quickly as

reasonable, and that they not be conducted during periods or in areas of high

winds (>20 kts.) and heavy ice (>30 70 cover). Since we recommended that

surveys start on 15 July, after ice break-up has usually occurred in the marine

system, heavy ice-cover would be less of a problem in the future than during

some previous years when some surveys began as early as 5 June. Beaufort

Sea lagoons are usually ice-free by mid-June. During some years, however

(e.g., 1974 and 1975), ice and associated fog persist in nearshore and offshore

marine regions of the Beaufort Sea throughout the summer. In such years

we recommended that only barrier island and lagoon transects be surveyed,

so that at least those data would be comparable from one year to the next.
Deve~opment of Monitoring Protocol

Data Recording

25

It was recommended that recording of aerial survey data be

standardized according to procedures established during a set of structured

surveys conducted in early August 1989. During those surveys we adopted 30-

sec time-period intervals for recording the numbers of birds on- and offtransect and for recording an array of information about the survey

conditions and prevailing environmental conditions. For each 30-sec

interval, factors recorded included amount of ice on- and off-transect, wave

height, glare on the water surface, wind speed and direction, proximity to

barrier island or other structure, apparent type and level of human activity

on- and off-transect during the time period, and changes in any particular

variable noted during that 30-sec interval. The 30-sec periods have been used

in most waterbird surveys in the study area since 1980; compared to 1- or 2-

min intervals, they provide better documentation of locations where birds

concentrate and where habitats change along transects. Consequently data

collected at 30-sec intervals are more useful than data collected by longer

intervals, especially if they might be mapped or included at a later date in a

database or in a Geographic Information System (GIS). It was recommended

that information be collected for all speaes of birds and mammals observed

on and off the transects.

Surveys are flown with two prime observers at an altitude of 45 m and

at a ground speed of 180 km/hr. Transect width is 400 m, 200 m on each side

of the aircraft; clinometers are used to calibrate distances from the aircraft.

Observers are trained to count large numbers of birds in dense concentrations

through a series of poppy-seed trials. Varying quantities of poppy-seeds (or

some other appropriately-sized granular material) are distributed on a sheet

of paper by an independent examiner isolated from the observers. The

observers are then allowed 5 seconds to estimate the number of seeds on the

paper. The trial is repeated 5-10 times with different numbers and patterns of

seeds. After the final trial, the scores of each observer are tallied and

compared with the actual number of seeds using Chi-squared techniques

(observed-expected). These trials help the observer to accurately estimate

large numbers of birds in dense concentrations; furthermore, inherent biases

in counting ability may also be detected and accounted for in corrected density
26 Development of Monitoring Protocol

computations. Computer programs are also available that assist in estimating

numbers of objects below an aircraft (J. Hodges pers. comm.).

During aerial surveys, tape recorders are used to record information

about the birds, their habitats and environmental conditions during the

survey. Data are later transcribed and coded onto standard coding forms that

provide for accurate recording of all of the information described above.

Linear and areal densities are computed for all species sighted on-transect

during all surveys; linear densities are also computed for on+off-transect

sightings. These data are manually and computer verified, validated, and

then computer tabulated by species, year, date, time-period, transect, and

observer.

Analvsis Procedures

The multiple regression approach described in the preceding sections is

optimum for examining historical data collected in a rather unstructured

manner. However, greater statistical power and preasion would be obtained

by collecting future data in the more structured fashion summarized above.

It was proposed that these data should be examined primarily by analysis of

variance (ANOVA) and analysis of covariance (ANCOVA) methods.

In the design phase of this study, we identified many variables that

should be taken into account in the analysis. These included variations in

waterbird density attributable to sampling period, time of day, water depth,

proximity to barrier island, east-west position within the study area, wind and

ice conditions during surveys, local variations in human activity, etc.

Because the proposed survey design was precisely structured with regard to

year, study area, sampling period, habitat, and transect, these were identified

as ~acfors in an analysis of variance. Wind direction and speed, ice cover,

wave height and other unpredictable continuously-distributed variables were

best handled as covariates rather than as categorical factors. Measurements of

human activity along each transect were a fifth covariate; by considering this

variable, it was thought that we could assess the possibility of medium-scale

industrial effects on waterbird density.
Development of Monitoring Protocol 27

The Prouosed ANCOVA Model

The ANCOVA model most appropriate to test for significant

differences in oldsquaw densities over space and time would include — in

addition to covariates, the following components:

Mean+A+Y+YA+H(Al +YH(A)+P(Y) +AP(Y)+T(H(A)) +Y’NH(A))+error,

where A = Area, Y = Year, H = Habitat, 1? = Periods, and T = Transects.

Parentheses indicate that some factors are nested within others, e.g., H(A) is

interpreted as habitat nested within area. An ANCOVA can be visualized as

an ANOVA with the addition of covariates to help standardize the basic unit

of analysis (oldsquaw density on a transect in a habitat in a study area during a

survey in a sampling period within a year). The ANOVA model is nested

(sampling period within year, habitat within study area, transect within

habitat) and factor effects are mixed, i.e., some are fixed and some are random.

Year, area and period are fixed and interpretations of analysis results of these

factors can only be extended to the levels tested. On the other hand, habitat

and transect were considered random effects since they could have been

defined in a variety of different ways to represent the spatial structure of the

factor.

The statistical significance of the year x area interaction, after allowance

for other factors in the analysis, is to be the main test of the possibility of a

large-scale industrial effect on oldsquaw density. This is one of the most

important statistical tests in the monitoring protocol, and is directly relevant

to the null hypotheses around which this study has been structured.

We recommended that the appropriate analysis of covariance

(ANCOVA) procedures (see Bliss 1970; Huitema 1980) areas follows:

1. Log-transform the density data in order to reduce the skewness

inherent in such data.

2. Conduct the ANCOVA with 5 covariates, including their 10 interaction

terms with year and area. Covariate interactions with the finer scale

temporal and spatial terms have been ignored since they are nested

within year and area.
Development of Monitoring Protocol

3. Non-significant interaction terms involving covariates should be

removed sequentially in such a way that the term with the greatest pvalue (least significant) is removed first; the ANCOVA model is then

rerun and the remaining interaction terms are examined. This process

is repeated until all statistically non-significant interaction terms have

been removed.

4. Conduct the ANCOVA using the factors, covariates and interaction

terms remaining after following the procedures outlined in 3. above.

Also conduct an ANOVA (no covariates), and an ANCOVA with only

the disturbance covariate so the overall effect of disturbance and the

“environmental” covariates (wind, waves, ice, etc.) can be isolated.

The proposed three surveys of each transect during each sampling

period in the field season provide the replication necessary for the ANCOVA.

The ANCOVA identifies how much of the variation in densities of

oldsquaws is attributable to each-factor, i.e., year, study area, sampling period,

habitat and transect; and to each covariate, i.e., wind speed and direction,

wave height, ice cover and disturbance. We developed an ordinal

measurement of industrial activity (values 1 through 5) that could be

assigned to each transect depending upon the type and amount of activity that

was occurring in or immediately adjacent to the transect during the sampling

period (Table 3).

Based on analyses of historical data and the preliminary test surveys

conducted in 1989, we were confident that the monitoring plan presented

above was the most appropriate and statistically powerful approach. This

approach was to be tested in 1990, the second year of the present project, and

the first full season of sampling. We also stated, however, that after one

complete season of data collection (i.e., after 1990) and subsequent analyses, it

might be necessary to modify some aspects of the field procedures or some of

the analyses. Such modifications were to be documented and a rationale for

changes in the protocol was to be provided.

Potential Problems and Sources of Error

The degree to which the various analysis assumptions are met in this

study is important in determining whether the proposed analysis procedures
Development of Monitoring Protocol

are appropriate and statistically valid. The assumptions needed to conduct an

ANCOVA such as the one recommended here are discussed by Huitema

(1980), and are as follows:

1.

2.

3.

4.

5.

6.

7.

8

Randomization, i.e., replicates are independent.

Homogeneity of within-group regressions, i.e., homogeneity of slopes

in regressions of oldsquaw density versus covariates (between

transects).

Linearity of within-group regressions, i.e., linearity of slopes in

regressions of density versus covariates (between transects).

Statistical independence of covariate and treatment, i.e., covariates

should not be correlated with treatment levels. Thus, wave height and

ice cover, for example, should be unrelated to habitat, sampling period,

etc.

Fixed covariate values that are known or measured without error.

Normality of conditional response scores, i.e., oldsquaw densities for

eaCh treatment are normally distributed after correction by the

covariates.

Homogeneity of variance of conditional response scores, i.e., variation

in oldsquaw densities is homogeneous for each treatment after

correction by the covariates.

Clearly defined treatment levels.

Most of these assumptions were satisfied given the proposed structured

experimental design. Several assumptions were problematic, however, and

those are discussed below.

Re~eated Survevs

One possible source of error that needed to be considered was that

replicate surveys flown within a 5- to 7-day sampling period may not be

independent of each other, i.e., the densities of oldsquaws seen on transects

during one survey may be related to those recorded on the same transect

during an earlier survey. The amount of day-to-day movement by oldsquaws

from one transect or habitat to another is unknown; thus the actual amount

of interdependency of oldsquaw densities on transects or habitats among

surveys is unknown. One radio-telemetry study (Brackney et al. 1985) did

29
Development of Monitoring Protocol

show, however, that oldsquaws molting in barrier island-lagoon habitats in

the Arctic National Wildlife Refuge (ANWR) moved as much as several km

per day, with an average of about 0.7 km per day. Thus, it is possible that

oldsquaws may move as much as 3 to 4 km or more within a 5- to 7-day

survey period. Such movements indicate that oldsquaws may not remain on

the same transect (or in the same habitat) for prolonged periods of time.

Consequently, we have assumed that replicate surveys do provide

independent samples of the populations of oldsquaws in the study areas.

As a possible alternative, we considered a Repeated Measures (RM)

analysis as an alternative to the basic ANCOVA proposed above. RM allows

for two or more non-independent surveys of the same area, and would thus

seem to be appropriate in the present situation.

The RM model appropriate for this study would have the following

basic structure, ignoring covariates:

Mean+A+Y+YA+H(A) +P(Y)+AP(Y)

A = Area

Y = Year

H(A) = Habitat nested within Area

P(Y) = Sampling Period nested within Year

This structure defines habitat within a year and area as the ‘subject’ which is

measured by repeatedly sampling transects. Sampling is replicated each

survey day.

A major shortcoming of the RM approach is the treatment of

covariates. If covariates are measured for a ‘subject’, such as habitat within

area, then the within-subject multivariate statistical tests are valid. If, on the

other hand, the covariates are measured at a lower level, such as transect

within habitat within area, then the within-subject multivariate tests are not

valid. In other words, for this study, the test for I&l (year x area interaction)

is valid regardless of the independence of the transect measurements or the

treatment of covariates in the RM model. In contrast, the test for H02

(disturbance) is valid only if the measurements at each transect are

independent. Considering these issues, we decided that the less restrictive

ANOVA/ ANCOVA approach, as opposed to the RM approach, was most

appropriate for this study.
Statistical Inde~endence of Covariate and Treatment

Development of Monitoring Protocol

It is probable that some covariates (e.g., wave height, wind speed and

direction, amount of ice on-transect) depend on some treatment factors (e.g.,

habitat, study area). For example, offshore habitats may be more exposed to

ice, and therefore transects in this habitat may have more ice than those

sheltered by barrier islands. Similarly, transects in barrier island habitats may

be sheltered from prevailing northerly winds, thereby affecting wave height

in this habitat as compared to exposed offshore areas.

Such relationships between the treatments and covariates violate a

fundamental assumption of ANCOVA. We have attempted to alleviate this

problem by recommending that surveys should not be conducted (or transect

densities should not be included in analyses) when extensive ice may be

encountered along transects or when strong winds and associated high waves

may be encountered. Although these recommended restrictions may limit

the number of surveys completed during a 5- to 7-day sampling period, they

are clearly required to insure that analyses do not violate ANCOVA

assumptions.

Measurement Error

Measurement error is a common problem where variables such as the

number of animals, percent ice cover, wave height, wind speed and direction,

and other factors are estimated from a fast-moving aircraft. Such

measurement error can affect the apparent strengths of relationships among

dependent and independent (predictor) variables. If two factors have equally

strong influences on oldsquaw density, but one factor is measured more

precisely than the other, the precisely-measured factor will seem to have a

stronger effect. Thus, measurement errors can confound the interpretation

and reduce the power of statistical tests.

We have attempted to reduce measurement error to the greatest extent

possible in this study by using well-trained and experienced aerial surveyors.

Furthermore, as mentioned earlier, we have implemented training sessions

designed to enhance the abilities of observers to estimate the number of birds

and other variables recorded along aerial survey transects . Regardless of

these efforts, some amount of measurement error is inherent in field

31
2 Development of Monitoring Protocol

investigations of this type and is bound to influence analysis design, results

and interpretations. Great care and concentration are necessary during aerial

surveys to insure that measurement error is reduced to a minimum.
Testing of Monitoring Protocol, 1990-1991 33

TESTING OF THE MONITORING

PROTOCOL, 1990-1991

As mentioned above, the original monitoring protocol was proposed

after an extensive review and analysis of historical aerial survey data, and

after a series of test aerial surveys in the study areas in early August 1989. The

following pages give results of the two years (1990-1991) of subsequent fullscale test surveys, and make recommendations for changes to the protocol

based on more field experience and data analyses in 1990-1991.

Desire Considerations

Consideration of Other Independent Variables

The original design for this project suggested that two additional

independent variables not previously considered should be recorded and

included in future analyses of factors potentially affecting oldsquaw densities

in the Industrial and Control study areas. These two additional factors were

(1) types of industrial activities and levels of disturbance for each transect

during each sampling period, and (2] seasonal upwelling potential andlor

seasonal mean wind speed and direction. Both of these factors were included

in our full-scale field studies in 1990-1991, but, for reasons described below,

seasonal upwelling potential was not used in our final analyses.

Disturbance

Types of industrial activities and associated disturbance levels were

categorized (Table 3) and recorded during all aerial surveys conducted in 1990

and 1991. Based on activities observed during the surveys, it was usually

possible to determine the types, extent and durations of the activities by

contacting various industry representatives. In some cases it was obvious

that considerable activity and potential disturbance had occurred in the

Industrial study area not only during the survey, but also prior to and

sometimes after the survey. For the purposes of our data analyses, the type of

activity and level of disturbance recorded for the survey period (i.e., observed

during surveys, and determined through discussions with industry
Testing of Monitoring Protocol, 1990-1991

representatives) was assigned to the relevant aerial survey transect. The types

of prease information needed from industry to further consider pre- and/or

post-survey-period activities or disturbances was not documented well

enough to be useful in our analyses; often such information was not

available. In addition, without more details on the long-term responses of

oldsquaws to each type of disturbance, including the degree to which they

habituate to various activities, it is not clear how long a time frame should be

considered in judging disturbance severity.

Seasonal Lh3wellirw Potential

Historical information on seasonal upwelling potential for years prior

to the initiation of this study (prior to 1989) was acquired from other

researchers at LGL who use this information in analyses of fisheries data (see

LGL 1990a,b; LGL et al. 1990a,b). These data were included in two new sets of

multivariate analyses, but results indicated no substantial changes in the

overall strengths of the relationships between dependent and independent

variables. Furthermore, the two factors defining seasonal upwelling potential

(wind speed and wind direction) are already inherent in other independent

variables, such as the interaction term WSPD x WDIR, and other terms such

as NCOMWND and NECOMWND (see above, and Johnson 1990). Also,

since seasonal mean upwelling potential is coded as a single annual value, it

does not contribute significantly to the proposed monitoring protocol after

only two years of sampling. Nevertheless, this annual variable should

continue to be recorded, and it should be reconsidered for use in the model at

a future date.

Samtdimz Procedures

The originally proposed sampling procedures involved the expeditious

survey of four transects in each of five habitats in each of two study areas

during each of three days (replicates) in four 5- to 7-day periods evenly spaced

from mid-July to early-September. Thus, surveys were planned on 12 days

per year — three survey days in each of four survey periods. However, in

both 1990 and 1991, poor weather (heavy ice, high winds, fog and poor
Testing of Monitoring Protocol, 1990-1991 35

visibility, and freezing rain) prevented the completion of several surveys

during several sampling periods (Table 4).

In 1990, sampling was done during all four sampling periods, and 11 of

the proposed 12 surveys were conducted. However, a full complement of

three surveys in each of the two study areas was possible only once, during

period 1 (Table 4). Furthermore, it was not possible to conduct useful surveys

of any of the offshore transects (Habitat 5, 5 km seaward of the barrier islands)

in 1990 because of heavy ice; ice cover was estimated as >60Y0 on all transects

in offshore habitats in each of the two study areas. Similarly, nearshore

habitats (Habitat 4, 1.5 km seaward of the barrier islands) were surveyed in

both the Industrial and Control study areas during only six of the 11 surveys

in 1990 (Table 4). Nevertheless, seven surveys of all 12 barrier island-lagoon

transects (Habitats 1-3) in both study areas were conducted during the peak of

the oldsquaw molt period (18 July through 20 August) in 1990. Data from

these seven surveys and three habitats were considered balanced (Milliken

and Johnson 1984), and were used in the final ANCOVA.

In 1991 the situation was worse. As in 1990, heavy ice (estimated >70%

cover) prevented useful surveys of offshore transects during every aerial

survey, and heavy ice (estimated >60?to) also prevented surveys of nearshore

transects during all but three surveys (Habitats 4 and 5, Table 4). One

complete sampling period was missed because of a long period of freezing

rain and fog in early September (Table 4). Nevertheless, eight surveys of all

barrier island-lagoon transects (Habitats 1-3) in both study areas were

completed during the peak of the oldsquaw molt period (19 July through 21

August) in 1991. Data from these eight surveys and three habitats were

considered balanced, and were used in the final ANCOVA.

In retrospect, the test surveys conducted in 1989, a year of fine weather,

were insufficient to evaluate the proposed sampling procedures during years

when weather and ice conditions are severe. Based on the problems

encountered during the two years of intensive field sampling, several

recommendations can be made to improve the sampling procedures. These

recommendations also affect the experimental design and therefore influence

statistical procedures. The recommendations are as follows:
36 Testing of Monitoring Protocol, 1990-1991

Table 4. The numbers of transects sampled in 5 different habitats on different dates in different sampling

mxiods in the Industrial and Control studv areas in the Alaska Beaufort Sea. 1990 and 1991.**

Proposed

Sampling

Periods

Replicate

Surveys

in 1990

1

2

3

4

1 18 Jul

2 20 Jul

3 23 Jul

1 03 Aug

2 04 Aug

3-

1 09 Aug

2 16 Aug

3 20 Aug

1 02 Sep

2 04 Sep

3 05se p

1990

Industrial Control

Habitats Habitats

1 2 3 4 5 1 2 3 4 5

.— —— —— —— .—

Proposed

Sampling

Periods

1

2

3

4

Replicate

Surveys

in 1991

1A 18 Jul

1 19 Jul

2 20 Jul

3 22 Jul

1 04 Aug

2 10 Aug

3-

1 14Aug

2 16 Aug

3 21 Aug

1-

2-

3 -

1991

Industial Control

Habitats Habitats

——1 2 ——3 4 ——5 1 ——2 3 ——4 5

““ Shaded values were not used in the final ANCOVA (see text).
Testing of Monitoring Protocol, 1990-1991 37

1.

2.

3.

4.

Abandon the concept of several 5- to 7-day sampling periods. Based on

our experiences in 1990-1991, weather conditions typically do not allow

three surveys to be conducted in all habitats in both study areas on each

of three days during each of the four 5- to 7-day sampling periods. A

more practical sampling approach involves a series of six to eight

replicate surveys conducted during the mid-July to mid- to late August

period. This eliminates one level of analysis (i.e., period) in the

statistical model, but, as discussed in more detail later, this does not

weaken the overall power of the critical ANOVA or ANCOVA tests.

Maintain a balanced sampling effort, which is required by ANCOVA

(i.e., devote equal sampling effort to each study area during each day of

sampling). In other words, ensure that both study areas can be sampled

during the same survey day. Both time and air charter costs can be

saved if — before commencing a day’s survey — both study areas

(Industrial and Control) are inspected during a brief reconnaissance

flight to ensure that they are fog-free and that wind, waves and ice in

each area are within acceptable limits.

Do not conduct aerial surveys in either study area, or in particular

habitats within either study area, if conditions known to strongly

reduce the apparent or actual density of oldsquaws are present, e.g.,

high winds, high waves, extensive ice cover, etc. In other words, do

not conduct surveys in a given habitat in either study area if that

habitat in one study area is extensively covered with ice or fog, or

under the influence of high winds. This approach maintains the

balanced survey design necessary for the ANCOVA, and avoids

conducting partial surveys

analyses.

Abandon surveys of habitats

whose results cannot be used in those

seaward of the barrier islands. Transects

in these habitats are iee-covered too often to allow for systematic aerial

survey coverage and, therefore, for them to be included in a balanced

ANCOVA. Furthermore, densities of oldsquaws in these habitats are

typically low and highly variable, i.e., unsuitable for monitoring.
Testing of Monitoring Protocol, 1990-1991

Schedule of Survevs

The original sampling schedule involved four sets of aerial surveys to

be conducted during the mid-July to early September molt period of

oldsquaws. Afurther andmore in-depth review of historical oldsquaw molt

data (Johnson 1985; LGLunpub. data) indicated that only a small proportion

of oldsquaws using barrier island-lagoon habitats are still molting and

flightless during early September. By early September, most male oldsquaws

have regrown their flight feathers and are able to fly. Furthermore, by early

September, females with flying young begin to move into coastal lagoons

from mainland lakes and ponds (Johnson and Richardson 1981; Johnson

1985). A more appropriate sampling schedule would cover the period midJuly through mid- to late August. This schedule would ensure that the same

segment of the oldsquaw population (molting and flightless males and nonbreeders) was sampled during each replicate aerial survey.

Thus, several recommendations can be made to improve the sampling

schedule. These recommendations also affect the experimental design and

statistical procedures, as discussed in more detail below. The recommended

changes to the survey schedule are as follows:

1. Conduct all surveys during the 4- to 5-week period during the peak of

the oldsquaw molt (mid-July through mid- to late August). This

involves the elimination of the proposed early September aerial

survey, and ensures that the same segment of the oldsquaw population

is sampled during all surveys.

2. As mentioned earlier, a series of six to eight replicate surveys should be

conducted, at about even intervals, throughout the 4- to 5-week

sampling period. Considering that poor flying weather is often

encountered in the study area during the summer open water period,

such a schedule is more practical than attempting to conduct several

surveys of all transects in all habitats in both study areas during each of

three or four 5- to 7-day blocks of time.
Testing of Monitoring Proiocol, 1990-1991 39

Analvsis Procedures

In the above discussion we recommended changes to the original

sampling procedures and sampling schedules described in the original

monitoring protocol. Here we explain the analysis implications of the

recommended changes. We also give the results of our ANOVA and

ANCOVA of the data collected in 1990 and 1991. We also evaluate our

original null hypotheses, and discuss the power of the statistical procedures

used in testing these hypotheses.

The New ANCOVA Model

The final model recommended to monitor densities of molting male

oldsquaws in relation to OCS development is as follows:

Ln(Density+l) = constant + WAVE + D + A + Y + AY + H(A) + YH(A) +

T(H(A)) + YT(H(A)),

where wave is a covariate representing the log transformed (Ln(x+l )) wave

height (inches) recorded on the transect, D is a factor representing disturbance

on the transect (Table 3), A is factor representing the study area (Industrial or

Control), Y is a factor representing the survey year, H is a factor representing

the habitat type within which the transect is located, and T is a factor

representing the location of the transect along a west-east gradient.

Parentheses designate that some factors are nested within others, e.g., H(A)

indicates that habitat is nested within area. The dependent variable is the

natural logarithm of the density of oldsquaws measured each sampling day

on each transect, in each habitat, and in each area during a given year.

All factors and covariates used in this model are identical to those used

in the earlier multiple regression analyses (Johnson 1990), with the exception

that only 1990 and 1991 data have been used in the current model. Also,

some covariate names have been changed slightly (e.g., WAVE rather than

WAVEHT or WAVETRAN), and some data have been transformed

differently than originally recommended, e.g., percent ICE cover has been
Testing of Monitoring Protocol, 1990-1991

transformed using an arc sine square route transformation, rather than a log

transformation.

This model differs from that recommended earlier (Johnson 1990)

because of changes in the sampling procedures and sampling schedules in

1990 and 1991, and because of refinements and re-evaluations of some

statistical procedures. The rationale for the structure of the new model is

presented below.

Factors

The period factor has been dropped from the model because

insufficient replicate surveys could be conducted in some sampling periods,

and because it was necessary to drop one whole sampling period (in

September) in order to restrict surveys to the peak of the oldsquaw molt

period. The elimination of period from the model does not have a significant

impact on our ability to test the original null hypotheses. Although the

grouping of surveys into periods may help define possible habitat x year

interactions, it is done at the expense of a substantial reduction in the degrees

of freedom in testing for disturbance effects, which is one of the primary

objectives of the study (~2).

A more detailed consideration of the structure of the proposed model,

in particular of disturb ante, suggested that it should be treated as an

incomplete blocking (stratification) variable, rather than as a covariate.

The rationale for treating a variable, in this case disturbance (d), as

either a blocking variable or a ‘covariate’ in an ANOVA design, is discussed

in Huitema (1980), Milliken and Johnson (1984), and Wirier et al. (1991).

Milliken and Johnson (1984) point out that when type III sums of squares are

calculated (i.e., when all parameters are estimated using least squares

regression techniques), and the variable of interest (disturbance in this case)

does not interact with any of the other variables (the usual assumption for a

covariate), then the two approaches (blocking variable vs. covariate) differ

only in the assumed relationship between the dependent variable (oldsquaw

density) and disturbance. This is true even if disturbance does or does not

occurs in combination with other factors. When treated as a covariate, the

relationship between disturbance and oldsquaw density is assumed linear,

and two parameters are estimated. When disturbance is treated as a blocking
Testing of Monitoring Protocol, 1990-1991 41

variable, the deviation from the mean for all d disturbance levels are

considered, and d-1 parameters are estimated.

In general, the covariate approach is advantageous because fewer

parameters are estimated for more than three levels of disturbance, whereas

the blocking approach is advantageous because it is ‘function-free’. If the

covariance relationships of oldsquaw density vs. disturbance are not linear

(which is not easily discernible with only two seasons of data), then the

treatment of disturbance as a blocking variable generally provides greater

reduction in the experimental error, and, therefore, results in a more

powerful test (Wirier 1971; Wirier et al. 1991). Disturbance is also treated as a

blocking factor because it can only be observed at five distinct levels, i.e., it is

not a continuous variable.

Only three levels of disturbance have been recorded in this study so far

– categories 1, 2, and 3 from Table 3. We assume that disturbance does not

interact with any of the treatments, i.e., disturbance always has the same

short-term effect on the

Randomization

When the levels

birds.

of a factor are chosen on a systematic, non-random

basis, then the factor is considered a fixed factor, and inference can only be

applied to the tested levels. Disturbance, year and area are clearly fixed factors.

On the other hand, if the levels are chosen at random from a very large

population of potential levels, then the factor is considered random, and any

inference extends to the entire population. Although habitats and transects

were not selected at random, they were selected from many alternatives

(many different flight paths, and many different measurement locations

along the flight paths). Hence, these factors are treated as “random” during

the analyses. This means that they contribute fewer terms to the expected

mean squares for the corresponding statistical tests than would be the case if

they were fixed factors for this design (i.e., treating the factors as random

would be less powerful in our particular design). However, since habitats and

transects were not selected at random, inferences involving these factors

should still be restricted only to the tested levels, and should not be extended

to the entire population (Milliken and Johnson 1984).
Testing of Monitoring Protocol, 1990-1991

If the unique effects associated with one factor are confined to a specific

level of another factor, the factors are said to be nested. For example, the

measurements taken on a transect can only be considered in the context of

both habitat and area. In this case transects are said to be nested within area

and habitat. Similarly, the nesting of habitat within area requires that habitat

cannot be considered without knowledge of the area. The implicit

assumption is that all interactions between area and habitat are confined to

each of the study areas. The sums of squares for nested factors can be

computed as the sums of the factors used in a fully factorial, or fully crossed,

model, i.e.,

H(A) = H + HA,

YH(A) = YH + YHA

T(H(A)) = T + TA + TH + TAH

YT(H(A)) = YT + YTA -t- YTH + YTAH

The pooled components YH and YHA are separate measures of the

variation of the year x habitat interaction (if habitat is indeed nested within

area). The requirement of homogeneity y, which is necessary for pooling in

this case, is equivalent to the requirement that the correlations of habitat and

year are the same within each of the areas (Wirier 1971). In contrast, if habitat

were crossed with area, i.e., if habitat were classified in an identical fashion in

each study area (were the same in both areas), then pooling the terms would

not be appropriate (Wirier 1971). We tested for homogeneity of YH with YHA

to ensure that the requirements for nesting were satisfied. Results showed

that mean square error terms for HY and HAY were almost equal (mean

square errors of 13.405 vs. 13.6$4, respectively), thus providing some empirical

justification for nesting.

A biological justification for nesting habitats within areas is the fact

that habitats are not identical in the two study areas. The mainland shoreline

of the Control area is characterized by many small bays and lagoons with

protective spits and islands (i.e., favored seaduck habitat); such habitats are

mostly absent from the Industrial area. Such a difference in habitats between

the two study areas would necessitate nesting them within areas.
Testing of Monitoring Protocol, 1990-1991 43

Emected Mean Sauare

The expected mean square error terms, and the degrees of freedom

used in statistical tests of the factors in this study, are given in Tables 5 and 6.

A critical test in this study is for disturbance (D), which uses the residual error

mean square as the error term (Table 6). This statistical test directly addresses

our working hypothesis I-I& The other important statistical test is the year x

area interaction (AY). In this test we use the interaction of year x habitat

within area, YH(A), as the error term (Table 6). This test directly addresses

our working hypothesis ml.

It is useful to consider the situation where habitat and area are crossed,

HA, as discussed in the earlier section, rather than nested H(A), as in the

current model (Tables 5 and 6). If this were the case, then the statistical test

for the year x area interaction (AY) would use the error term AYH with (yI)(h-l)(a-1) degrees of freedom, rather than the error term YH(A). The nested

design YH(A) has more degrees of freedom [(y-l) (h-l)a] than the pooled

design. It should be noted that there is a considerable loss in degrees of

freedom (i.e., statistical power) if AYH is inappropriately used as the error

term when habitat is truly nested within area, as in the present analysis.

Covariates

Covariates originally considered for the model were wind speed

(WSPD), transformed wind direction (WDIR), northern component of wind

(NCOMWND), transformed percent ice cover (ICE), and transformed wave

height (WAVE). Pearson correlation coeffiaents among all pairs of candidate

covariates were computed to identify those that were highly correlated (Table

7). Any statistically non-significant covariates or interactions with year and

area were eliminated following the step-wise procedure described in our

earlier report (Johnson 1990) and recommended by Bliss (1970) and Huitema

(1980).
Testing of Monitoring Protocol, 1990-1991

Repeated Measures and Data Selection

All strata in our final experimental design (unshaded area in Table 4)

were measured during each sampling period (survey day). Hence the design

was balanced and may be appropriately viewed as a two-factor experiment

(year and area were treatments) with a hierarchy of repeated measurements

(habitats and transects were measured during each survey in each of the two

study areas). Year and area are at a different level of randomization (fixed)

compared to habitat and transect (random). For this type of design, tests of the

year and year x area interaction are not confounded by differences among

habitats and transects (Wirier 1971, Milliken and Johnson 1984). In other

words, each survey serves as its own control with respect to year and year x

area interaction. The practical implication of this fact for our study design is

that, as long as the analysis is restricted to data arising from surveys that are

complete (balanced), then the critical test for ml (year x area interaction) is

valid regardless of any correlation between or among transects and habitats.

This critical test will also remain valid in the presence of additive or carryover effects (i.e., a situation where an event during one survey may affect the

density of oldsquaws in a subsequent survey).

In contrast, the direct measure of disturbance (H02) is confounded by

differences among habitats and transects. In other words, the univariate test

for disturbance is only valid if the measurements taken in a cell (year, area,

habitat and transect combination) can be regarded as truly independent

replicates.
Table 5. Degrees of freedom and expected mean squares of factor components in the ANOVA

of 1990-1991 oldscmaw densim data.

Component Degrees of Expected Mean Square

Freedom

D

A

Y

AY

H(A)

YH(A)

T(H(A))

YT(H(A))

d-1

a-1

y-l

(a-l) (y-1)

(h-l)a

(y-l) (h-l)a

(t-l)ha

(y-l) (t-l)ha

error + ayhtn*Var[D]

error + dyn*Var[T(H(A))] + dytn*Var[H(A)] + dyhtn*Var[A]

error + dn*Var[YT(H(A))] + dtn*Var[YH(A)] + dahn*vdM

error + dn*Var[YT(H(A))l + dtn*Var[YH(A)] + dhtn*Var[AYl

error + dyn*Var[T(H(A))] + dytn*Var[H(A)]

error + dn*Var[YT(H(A))] + dtn*Var[YH(A)]

error + dyn*Var[T(H(A))]

error + dn*Var[YT(H(A))]

MODEL ln(Density+l) = constant+D+A+Y+AY+H(A) +YH(A)+T(H(A))+YT(H(A))

Facto~ D A Y H T

Fixed/Random: F F F R R

Levels: d a Y h t

n = number of observations per cell.

Factor Definitions: D = disturbance

A = area

Y = year

H = habitat

T = transect
Testing of Monitoring Protocol, 1990-1991

Table6. Teststatistics foreach of thecomponents

in the ANOVA of the 1990-1991 oldsquaw density

data (see expected mean squares in Table 5).

Component Error Term

D

A

Y

AY

H(A)

YH(A)

T(H(A))

YT(H(A))

residual error

H(A)

YH(A)

YH(A)

TH(A)

YT(H(A))

residual error

residual error
Testing of Monitoring Protocol, 1990-1991 47

Table 7. Pearson correlation coefficients (r) for cross-correlations among potential

covariates in the ANCOVA of 1990-1991 oldsquaw density data (n = 360).

WSPD COS(WDIR) NCOMWND WAVE ICE

WSPD 1.000

COS(WDIR) -0.425”” 1.000

NCOMWND 0.071 0.713”’ 1.000

WAVE 0.644”” -0.075 0.303”” 1.000

ICE 0.100 -0.078 -0.033 -0.001 1.000

*“p <<0.01
Testing of Monitoring Protocol, 1990-1991

Comrmtimz the Sensitivitv (Power) of the ANCOVA Model

An important issue in a monitoring program of this type is the degree

to which the sampling and analytical procedures are able to test critical

hypotheses. In this study we considered the degree to which the current

model can be improved, i.e., made more powerful in order to detect smaller

percent changes in the adjusted mean density of oldsquaws for the two terms

in the model (disturbance and year x area interaction) that relate to the two

hypotheses being tested. We did this by computing the ability of the model to

detect given percent changes (at a 95% confidence level) in adjusted mean

densities of oldsquaws by (1) increasing the amount of sampling within a

year, and/or (2) increasing the number of years of sampling.

Burdick and Graybill (1992:36-39) show how to estimate exact

confidence intervals on linear combinations of expected mean squares with

different signs, as follows: 6 = Clel - c20z where the c’s are constants and the ~s

are expected mean squares. This form corresponds exactly to our design. For

example, the test for the year x area interaction sets & = O, C1 = C2 = 1/ (dhtn), el

= the expected mean square of the AY component, and 62, = the expected

mean square of the YH(A) component (see earlier discussion, and Tables 5

and 6). We applied Burdick and Graybill’s methodology to obtain the upper

95% confidence level for 5 at various sampling regimes (i.e., only y and n can

vary). We assumed that the variation about the cell means observed in 1990

and 1991, adjusted for wave height, would remain the same in future years.

For convenience in computation, we further assumed that an equal number

of surveys would be conducted each year, and that only three levels of

disturbance would be recorded. The upper 95% confidence level variance

measure was then transformed to the Ln oldsquaw density deviation with the

appropriate t-distribution, and expressed as the percentage of the Control area

mean observed in 1990 and 1991, and adjusted for wave height (the covariate).

Statistical Software

We found that the best software package available to conduct the

ANOVA and ANCOVA procedures for this study is the general linear

modeling (GLMH) module in SYSTAT (Wilkinson 1987), This is a powerful

and flexible micro-computer based package of programs available for both
Testing of Monitoring Protocol, 1990-1991 49

DOS and Macintosh operating systems. The balanced design of our sampling

program and the requirement for use of Type III sums of squares (SS)

(Milliken and Johnson 1984:166-171) for hypothesis testing makes SYSTAT a

particularly useful package (Type III SS are the default SS in SYSTAT). The

availability of such a user-friendly and well documented package of programs

for use in a monitoring protocol of this type is an important aspect of the

design and implementation. It reduces the time and expense necessary to set

up and execute computer programs for the complex analyses required.

Results of the ANCOVA

As recommended in Johnson (1990), an initial analysis of variance of

the 1990-91 data was conducted using no covariates (Table 8, Case A). Three of

the terms, h(a), yh(a), and t(ah) were highly significant. The results for h(a)

indicated that oldsquaw densities in habitats within areas were different

within a year, h(a), as well as between years, yh(a), and that oldsquaw

densities in different habitats differed (see also Fig. 7). The results for t(ah)

showed that, within areas and habitats, densities on different transects varied

in a consistent way. These results are consistent with results from earlier

studies (Johnson and Richardson 1981) where densities of oldsquaws were

found to be different among habitats and transects in Beaufort Sea lagoons. In

fact, as mentioned in the design phase of this project (Johnson 1990), the study

areas were originally subdivided into different habitats, with several transects

in each habitat, because of suspected differences in oldsquaw abundance in

these different locations. One noteworthy result was the difference in

oldsquaw densities on transects within habitats and areas, t (ah). Some

transects had consistently higher densities of oldsquaws compared to others

in the same areas and habitats (see Append. 7). Earlier work on oldsquaws in

the study areas has also documented this phenomenon (Johnson and

Richardson 1981).

An initial ANCOVA was conducted using all possible covariates.

Covariates considered in a preliminary model were ice (arcsine square-root

transform of % ice), wspd, wdir (cosine transform), ncomwnd, wave, and all

possible interactions with year and area. Since wspd and wdir are the
Testing of Monitoring Protocol, 1990-1991

essential components of ncomwnd (ncomwnd = coswdir x wspd), these three

covariates (wspd, wdir and ncomwnd) were not used in the same model.

The covariates remaining in the various runs of the model, after

sequentially removing non-significant ones (as discussed earlier), are given in

Table 8, Cases B and C. Case B shows results for the model that considered

wspd, wdir, ice and wave, along with all appropriate interaction terms. In

this case the covariates wspd and a x wspd had statistically significant effects

on oldsquaw density. Apparent oldsquaw density tended to be lower when

wind speed was high (Append. 6F). Case C shows results for the model that

considered ncomwnd (rather than wsp d and wdir), ice, wave , and

appropriate interaction terms. In Case C, the covariate wave was statistically

significant. Apparent oldsquaw density tended to be lower when wave height

was high (Append. 6B). The results for the main factors and interactions

terms are the same in Cases B and C as those in Case A (described above), with

the additional interpretations that observed oldsquaw densities differed

significantly according to wind speed (B) or wave height (C).

At first glance, the Case B version of the model appears to be best

because of the marginally smaller residual mean square error for d (0.754 vs.

0.763, Table 7, Cases B and C, respectively). However, the model resulting in

Case C of Table 7 is recommended for the following reasons:

1. The significant area x wspd interaction in Case B is a confounding

factor in the interpretation of the year x area interaction, ay (e.g.,

there may be a significant ay effect at some level(s) of wind speed, but

not at other levels of wind speed).

2. Observer efficiency is known to be related directly to wave height,

rather than only indirectly (through wave height) to classifications of

wind speed.

It is notable that, in all three versions of the model, the three terms

h(a), yh(a), and t(ah) were statistically significant. The interpretations and

implications of this are discussed above (Case A). Even more significant,

however, is the fact that disturbance (d) and the year x area interaction (ay)

terms were not statistically significant in any version of the model. So far we

have recorded only three levels of disturbance (1, 2, and 3; see Table 3) in the
Testing of Monitoring Protocol, 1990-1991 51

Table 8. Results of ANOVA and ANCOVA tests of 1990-1991 oldsquaw density data. Three cases

are presented: Case A = no covariates; Case B = one covariate, wind speed (wspd); Case C = one

covariate, wave height (wave).

Term SSQ SSQ(test) df df(test) M S MS(test) F P

Case A: No Covanates, R squared = 0.797

2.824

78.087

28.662

28.587

586.592

53.180

185.968

14.951

244.964

586.592

53.180

53.180

185.968

14.951

244.964

244.964

2

1

1

1

4

4

18

18

310

4

4

4

18

18

310

310

1.412

78.087

28.662

28.587

146.648

13.295

10.332

0.831

0.790

1%.648

13.295

13.295

10.332

0.831

0.790

0.790

1.787

0.532

2.156

2.150

14.194

16.007

13.074

1.051

0.169

0.506

0.216

0.216

AQQQ

0.00Q

0.402

Case B Covanate = WSPD, R squared = 0.807

AE.td

d

a

Y

L

m

yt(ah)

10.553

3.278

2.212

36.553

16.094

15.338

571.781

56.291

181.09

15.061

232.366

232.366

232.366

571.781

56.291

56.291

181.09

15.061

232.366

232.366

1

1

2

1

1

1

4

4

18

18

308

308

308

4

4

4

18

18

308

308

10.553

3.278

1.106

36.553

16.094

15.338

142.945

14.073

10.061

0.837

0.754

0.754

0.754

142.945

14.073

14.073

10.061

0.837

0.754

0.754

13.988

4.345

1.466

0.256

1.144

1.09

14.208

16.819

13.335

1.109

4JMQ

0.232

0.640

0.345

0.355

0.00Q

0.00Q

_QAQQ

0.342

Case C: Covariate = WAVE, R squared = 0.804

wav e

d

a

Y

L

~h(a)

f{ald

yt(ah)

9.151

2.798

78.659

34.520

27.544

538.830

54.179

182.133

14.552

235.813

235.813

538.830

54.179

54.179

182.133

14.552

235.813

235.813

1

2

1

1

1

4

4

18

18

309

309

4

4

4

18

18

309

309

9.151

1.399

78.659

34.520

27.544

134.708

13.545

10.119

0.808

0.763

0.763

134.708

13.545

13.545

10.119

0.808

0.763

0.763

11.991

1.833

0.584

2.549

2.034

13.313

16.754

13.259

1.059

- 0001

0.162

0.487

0.186

0.227

0,00Q

0.000

0.00Q

0.393

Underlined tern-s in boldface italics are statistically signitkant (ps 0.05).
52 Testing of Monitoring Protocol, 1990-1991

1990

n==

3

2

1

!

o~

i

Industrial Control Industrial Control Industrial “Control

Barrier Island Mid-Lagoon Mainland Shore

Habitat Type

7-

6-

5-

4-

3-

2-

o~

1-

Industrial Control Industrial Control Industrial Control

Barrier Island Mid-Lagoon Mainland Shore

Habitat Type

Figure 6. Mean & 95% C.I. (confidence intervals) of transformed densities

[ln(density+l)] of oldsquaws in three barrier island-lagoon habitats (barrier

island-lagoon shoreiine, mid-lagoon, and mainland shoreline) in both the

Industrial and Control study areas in 1990 and 1991.
Tesfing of Monitoring Protocol, 1990-1991

Industrial study area. Level 3 disturbances were recorded on several transects

during survey periods in July and August 1990 (Append. 5) during surveys

that overlapped with practice oil spill clean-up exercises that occurred in the

Industrial study area (R. Metcalfe, ARCO, pers. comm.). The exercises

involved regular boat activity, as well as several low-level (< 500’ altitude)

fixed-wing and helicopter overflights, on transects near the Kuparuk River

delta and West Dock causeway (Fig. 3). Other level 3 disturbances were

assoaated with seismic activity, primarily in the Control study area (Append.

5). Details of disturbances recorded on speafic transects and survey days are

given in Appendix 5.

Graphs showing the relationships between transformed oldsquaw

density on transects in the two study areas (Industrial and Control) in 1990

and 1991 vs. time of day, transformed wave height, disturbance level,

transformed day of season, transect location (W-E), transformed wind

direction, and wind speed are given in Appendix 6.

Appendix 7 presents the individual cell means (ln[oldsquaw density +

I] adjusted for wave height) by transect, habitat, study area, and year (1990 and

1991). In total, there were 15 complete surveys of the 24 transect/habitat/area

combinations in the two years of sampling (7 in 1990 and 8 in 1991, n=360).

Appendix 8 shows the actual mean densities of oldsquaws (adjusted for

wave height) in the Control and Industrial study areas in 1990 and 1991. This

plot represents the year x area interaction term discussed in detail above.

Evaluation of Hmotheses

The main objective of this study was to devise field and analytical

methodology suitable for long-term monitoring of the numbers of molting

oldsquaws in relation to potential regional effects (ml) and local effects (l&2)

of industrial activity. After an initial season of field tests (1989), two seasons

of systematic field data were collected (1990-1991). From a computational

viewpoint, the statistical procedures developed in this study can be applied

with a minimum of two years of systematic data. However, it is premature to

try to evaluate the correctness of the null hypotheses, and particularly ml,

after only two years of systematic surveys. Thus, interpretations of

hypotheses given here are included primarily as an illustration of how such

53
Testing of Monitoring Protocol, 1990-1991

interpretations can be made after more data are collected — not as definitive

tests of the hypotheses.

The two null hypotheses to be tested by the monitoring and analysis

program are as follows:

HOI: There will be no detectable change in relative densities of molting

male oldsquaws in selected Beaufort Sea index areas.

H& Changes in male oldsquaw distribution patterns are not related to

OCS oil and gas development activity.

Re~ional Effects

HOI concerns the possibility of a long-term, i.e., year-to-year, change in

oldsquaw densities in the Industrial area that is not paralleled by a

corresponding change in the Control area. In our analyses of variance and

covariance, the year x area interaction term, a y, provides a test of ml after

allowance for other factors such as habitat, specific transect, local disturbance,

various interaction terms, and (in ANCOVA) covariates such as wind speed

or wave height. Based on two years of systematic sampling there is no

statistically significant evidence of such a change; the ay term was nonsignificant in all ANOVA and ANCOVA models (Table 7). However, various

industrial activities had been going on in the Industrial areas for many years

prior to 1990 as well as in 1990 and 1991. Thus, no significant ay effect would

be expected over such a short interval, regardless of whether or not a change

in relative use of the Industrial vs. Control areas would occur over the longer

term. If systematic surveys are continued in subsequent years when

industrial activities in nearshore areas are consistently greater (or less) than

in 1990-1991, a corresponding statistical test of the ay term can be used to

evaluate whether there is a corresponding long-term change in oldsquaw

densities.

The are a term in the ANOVA and ANCOVA results are also

instructive. The area term provides a test of the hypothesis that oldsquaw

densities were the same in the Industrial and Control regions during the

1990-1991 period as a whole. (This hypothesis should not be confused with

the original ml that “There will be no detectable change in relative densities
Testing of Monitoring Protocol, 1990-1991 55

of molting male oldsquaws in selected Beaufort Sea index areas.”). All

ANOVA and ANCOVA models failed to reject this hypothesis of overall

equal densities in the two study areas. Inspection of Figure 7 suggests that –

in 1990-1991 – there was no consistent difference between the Industrial and

Control regions in oldsquaw densities in barrier island habitats or mid-lagoon

habitats. This is a noteworthy result, given the history of industrial activity

in the Industrial area not only in 1990-1991 but also in prior years. However,

densities along the mainland shore were substantially higher in the control

area, as mentioned previously, and as reflected in the significant h(a)

interaction terms (Table 7). This is believed to be at least partly the result of a

difference in the type of habitat along the mainland shoreline in the

Industrial vs. the Control areas. It should be kept in mind, however, that

there is no a prior reason to expect that densities of oldsquaws should be the

same in the two study areas. As mentioned above, it is a change in the

relative densities in the two study areas that is the true test of &l.

Although neither the area term nor the year x area interaction term

was statistically significant in the three runs of the model using 1990-1991

data, it is somewhat worrisome that the overall mean density of oldsquaws

on barrier island transects in the two study areas (pooled), declined steadily

over the 1989-1991 period (Fig. 6, top). At the same time, the general trend in

oldsquaw densities on non-barrier island habitats steadily increased (Fig. 6,

bottom). The possibility of long-term trends in densities of molting male

oldsquaws in nearshore Beaufort Sea habitats can only be evaluated if data

from additional years become available. However, results of aerial breeding

pair surveys of the Arctic Coastal Plain of Alaska during 1986-1991 (Brackney

and King 1991, King 1991) indicate no downward trends in the estimated

numbers of oldsquaw drakes, oldsquaw pairs, or in oldsquaw population

estimates.
Local Disturbance Effects

Testing of Monitoring Protocol, 1990-1991

H02 concerns the possibility that human activities in particular parts of

the Industrial (or Control) study areas may have localized influences on

oldsquaw densities. In our analyses of variance and covariance, the

disturbance term, d, provides a test of ~2 after allowance for other factors

such as area, year, habitat, specific transect, various interaction terms, and (in

ANCOVA) covariates such as wind speed or wave height. Based on two years

of systematic sampling, there is no statistically significant evidence of such a

change; the d term was non-significant in all ANOVA and ANCOVA models

(Table 7). However, it is interesting that in the Industrial area in 1990 — the

one situation with many cases of potential disturbance — there was a

nonsignificant tendency for lower oldsquaw densities on transects with

human activities (Appendix 6C).

The test of &2 is potentially more meaningful than is the test of HOI

when only a few years of systematic data are available, given the much larger

number of error degrees of freedom for the present test (Table 7).

Nonetheless, great caution is necessary in interpreting the results. There

were relatively few transect /data combinations with known human

disturbance in 1990, and virtually none in 1991 (Appendices 5, 6C). In this

situation, the test has little power to detect a biologically significant

disturbance effect even if a strong effect exists.

Power of Kev Statistical Tests

As mentioned earlier, an important issue in a monitoring program of

this type is the degree to which the sampling and analytical procedures are

able to test critical hypotheses. In this study we considered the degree to

which the current model can be improved, i.e., made more powerful, in order

to detect smaller percentage changes in the adjusted mean density of

oldsquaws for the two terms in the model (disturb ante and year x area

interaction) that relate to the two hypotheses being tested. We assumed that

current conditions would prevail in future years, i.e., only three levels of

disturbance at the same relative frequencies would continue to be recorded,

and residual error within each cell (year, area, habitat and transect

combination) would remain the same.
Testing of Monitoring Protocol, 1990-1991

It is clear that for localized disturbance effects, the current annual level

of sampling (seven or eight surveys/season) is adequate to detect, over a 2-

year or longer period, a 7-8% change (at a 95% confidence level) in adjusted

mean oldsquaw density on disturbed vs. undisturbed transects (Fig. 8). An

increase in sampling effort from 7-8 surveys per season, or an increase in the

number of seasons of surveys, would not appreciably improve the sensitivity

of the model (Fig. 8).

For the year x area interaction term, however, the current level of

sampling is sufficient only to detect a 130-140% change in the adjusted mean

density of oldsquaws over a 2-year period (Fig. 8). Although the performance

of the model is not appreciably improved by increasing the number of

samples within a year, it is markedly improved if the number of years of

sampling is increased beyond two years. With three years of sampling one

could detect a 50’?ZO change in adjusted mean density. The current analysis

indicates that a 12?t0 difference could be detected only after about 11 or 12 years

of surveys. However, if levels of disturbance increase in future years, or if

the effects of disturbance are cumulative over several years, then fewer

survey years would be required to detect such a change in oldsquaw density.

57
58 Testing of Monitoring Protocol, 1990-1991

Disturbance

r

2 4 6 8 10 1 2

Years of Survey Replication

Year and Area Interaction

150 r

u

g 100

s

01 1 I I 1 I

2 4 6 8 10 12

Years of Survey Replication

Figure 7. Percent change (95% confidence level) in the adjusted mean density

of oldsquaws for tests of disturbance (top) and year x area interaction

(bottom), given different amounts Of sampling” Each line ‘n ‘he ‘“p and

bottom plots represents a different intensity of within-year sampling, i.e.,

the bottom lines represent 10 replicate samples per year, and the top lines

represents 2 replicate samples per year. The abscissa represents the

number of replicate years of sampling, and the ordinate represents the

percent change in the mean density of oldsquaws in the Control study area

(adjusted for wave height).
SUMMARY AND CONCLUSIONS

Summary and Conclusions

In late September 1983, an MMS/NOAA-sponsored workshop was held

in Girdwood, Alaska, to develop a monitoring strategy for the Alaska

Beaufort Sea. The concept of monitoring Beaufort waterbirds is based on the

following conclusions of the 1983 workshop:

Marine birds are abundant and are a biologically and socially important

component of the nearshore Beaufort Sea ecosystem.

Some species of Beaufort Sea marine birds, especially marine waterfowl

such as the oldsquaw duck (Clangula hyemalis), are ubiquitous,

relatively easy to detect and count, and have been well studied prior to

industrial development; therefore they are appropriate candidates for

monitoring.

A monitoring protocol should be designed to insure that industryrelated influences on marine birds are discernible from other natural

influences, i.e., should involve a rigorous design and statistical

approach that includes both experimental (Industrial) and Control

areas and draws on all relevant historical information collected in the

study area.

The 1983 workshop identified several potential waterbird species for

monitoring. The oldsquaw duck was selected over other species because it is

the most abundant and widespread local waterbird in the nearshore Beaufort

Sea, the zone where virtually all exploration and development have occurred

in the Beaufort marine system. Data presented at the workshop confirmed

that during the summer open-water period oldsquaws represent most of the

avian biomass in the nearshore Beaufort environment. Most other species

occur in smaller numbers or are transients in the study area, so none of these

species were thought to be suitable candidates for a monitoring program.

During July and August, when oldsquaws molt their feathers, they are

flightless and they are thought to be particularly vulnerable to water-borne

contaminants and disturbances.

59
Summary and Conclusions

Analyses of nine years

phase of this study indicated

about 93% of all birds of all

of historical aerial survey data in the design

that oldsquaw ducks represented on average

species seen both on- and off-transect in the

central Alaska Beaufort Sea. A very large proportion of oldsquaws recorded

during aerial surveys in this area were near barrier islands. These results,

along with similar results from studies in the Arctic National Wildlife

Refuge (Garner and Reynolds 1986), confirmed the results of the 1983

workshop, i.e., that the oldsquaw was the best candidate for study in a

monitoring program designed to detect and measure the effects of industry

activities (disturbance) on marine birds and waterfowl in the Jones-Return

islands area, Beaufort Sea, Alaska.

A monitoring program that is designed to detect the influences of

industry activities on nearby birds must test specific hypotheses that relate to

(1) the birds chosen to be monitored, and (2) the types of industry activities in

the study area. The following null hypotheses were constructed with such

factors in mind:

HOI: There will be no detectable change in relative densities of molting

male oldsquaws in selected Beaufort Sea index areas.

H02: Changes in male oldsquaw distribution patterns are not related to

OCS oil and gas development activity.

Hypothesis (1) relates to the possibility of a rather large-scale and longterm change in relative densities in Industrial vs. Control study areas.

Hypothesis (2) concerns relationships between oldsquaw densities and shortterm localized variations in human disturbance.

Correlation analyses of nine years of historical aerial survey data

indicated that densities of oldsquaws along barrier island transects best

reflected overall densities of oldsquaws in the study area during the sampling

periods. Other studies indicated that undisturbed oldsquaws showed a strong

diel periodicity in behavior and abundance at barrier island locations near the

Jones-Return islands, and that oldsquaw distribution near barrier islands was

significantly related to wind speed and direction. The results of these and

other studies helped in the selection of potential predictor variables for use in

preliminary analyses of historical data; multivariate statistical analyses were
Summary and Conclusions

designed to isolate the most important determinants of oldsquaw density on

transects in the study area (Johnson 1990).

The relevant predictor variables (independent variables) selected for

use in these preliminary multiple regression analyses of oldsquaw density

(dependent variable = DENSTRAN) on transects in the study areas were as

follows:

1. Year of study (YEAR).

2. Time of the year (day of the season) that sampling occurred (DAY

and DAYTRAN).

3. Time of day that sampling occurred (TIME).

4. Water depth in the sampling area (DEPTH and DEPTRAN).

5. Location of transect along an east-west axis (WESTEAST and

WESTRAN).

6. Proximity of transect to a barrier island (DIST, DISTRAN, and

HABITAT).

7. Wind speed and direction in the sampling area during the sampling

period (WDIR, WSPD, ORDWND, NECOMWND, NCOMWND).

8. Percent ice-cover on-transect in the study area during the sampling

period (ICE and ICETRAN).

9. Wave height on-transect during the sampling period (WAVEHT

and WAVETRAN).

10. Study Area (AREA), i.e., Industrial vs. Control.

Earlier analyses, and analysis of residuals from this multiple regression

analysis, indicated that some variables should be transformed to satisfy

various assumptions of the parametric general linear modeling (glrn)

statistical procedures used in this study.

Two multiple regression analyses of oldsquaw densities were

conducted: (1) for oldsquaws on transects surveyed during the open-water

season (5 June to 23 September), and (2) for those on transects surveyed

during the peak period of molt by male oldsquaws (I5 July to 25 August).

Results from 9 years of study (1977-1984 and 1989) indicated that several

variables and combinations of variables (interaction terms) were highly

significant in predicting oldsquaw density on transects in the study area. In

particular DAY, WAVETRAN, HABITAT, YEAR x AREA, TIME x HABITAT,

61
Summary and Conclusions

HABITAT x ICETRAN and WDIR x WSPD were statistically significant

predictors of oldsquaw density (Table 2) in one or the other of the two

analyses. HABITAT was a particularly important predictor variable,

especially in combination with TIME and ICETRAN, and this factor was

selected to represent the array of other factors describing the proximity of the

transect to the barrier islands in the study area.

The results of the multiple regression analyses helped in the design

and implementation of the full season sampling programs in 1990 and 1991,

and in the formulation of a speafic analysis of covariance (ANCOVA) model

suitable to analyze 1990, 1991, and any subsequent comparable data collected

in the Industrial and Control study areas.

Sampling was conducted in such a way as to obtain oldsquaw density

data and associated environmental data for the following spatial and

temporal categories:

● Two study areas (Industrial and Control).

● Three habitat strata: (1) barrier island habitat, (2) mid-lagoon habitat, (3)

mainland shoreline habitat.

● Four transects within each habitat stratum per area.

s One 4- to 5-week sampling period during the peak of the oldsquaw

molt period (mid-July to late August).

● Six to eight relatively evenly spaced survey dates within the single 4- to

5-week sampling period.

For every transect surveyed, we determined the number and density of

oldsquaw present, presence of human disturbance, wave height, ice cover and

wind.

This sampling approach provides the replicated and structured data

necessary to isolate the effects of the variables known to affect oldsquaw

densities. The experimental design is compatible with the powerful ANOVA

and ANCOVA statistical procedures that we have used to separate the effects

of factors and covariates.
Summary and Conclusions

In order to test the two null hypotheses presented at the start of this

exercise, i.e., to test whether there have been regional or local changes in

densities of molting male oldsquaws that may be attributable to industrial

activities, we recommend continued use of the analysis of covariance

statistical approach. The 5 factors are year, area, habitat, transect, and

disturbance level, and the five covariates considered were WH?D, WDIR,

NCOMWND, WAVETRAN, and ICETRAN on each transect; WAVETRAN

was the single covariate remaining after completion of further analyses. The

replicates are the six to eight days of surveys within the single 4- to 5-week

sampling period.

The ANCOVA model most appropriate and best suited to test for

significant differences in oldsquaw densities over space and time is as follows:

Density= Mean+ WAVE+ D+A+Y+AY+H(A) +YH(A) +T(AH) +YT(AHA) +error

I?arentheses indicate that some factors are nested within others, e.g., H(A) is

interpreted as habitat nested within area. The ANCOVA model is nested

(habitat within study area, transect within habitat) and factor effects are

mixed, i.e., some are fixed and some are random. Year, area, and disturbance

are fixed effects, but habitat and transect are considered random effects since

they could have been defined in a number of different ways. WAVE is the

single covariate included in this final model.

Because of the nested design and mixed (random and fixed) effects, tests

of significance of the various terms and interactions in the analysis model

involve error terms that are specific to the particular test, i.e., terms other

than residual error are sometimes used as the denominator of the F-ratio.

We have followed the appropriate analysis of covariance (ANCOVA)

procedures, as suggested by Bliss (1970), Huitema (1980), and others. The

ANCOVA identifies how much of the variation in densities of oldsquaws is

attributable to each factor, i.e., year, study area, disturbance, habitat, transect,

and to the single covariate, wave height.

The main objective of this study was to devise field and analytical

methodology suitable for long-term monitoring of the numbers of molting

oldsquaws in relation to potential regional effects (ml) and local effects (~2)

of industrial activity. After an initial season of field tests (1989), two seasons

of systematic field data were collected (1990-1991). However, it is premature to

63
54 Summary and Conclusions

try to evaluate the correctness of the null hypotheses, and particularly ml,

after only two years of systematic surveys. Thus, interpretations of

hypotheses given here are included primarily as an illustration of how such

interpretations can be made after more data are collected — not as definitive

tests of the hypotheses.

HOI concerns the possibility of a long-term, i.e., year-to-year, change in

oldsquaw densities in the Industrial area that is not paralleled by a

corresponding change in the Control area. In our analyses of variance and

covariance, the year x area interaction term, ay, provides a test of ml after

allowance for other factors such as habitat, specific transect, local disturbance,

various interaction terms, and (in ANCOVA) covariates such as wind speed

or wave height. Based on two years of systematic sampling there is no

statistically significant evidence of such a change; the ay term was nonsignificant in all ANOVA and ANCOVA models. However, various

industrial activities had been going on in the Industrial areas for many years

prior to 1990 as well as in 1990 and 1991. Thus, no significant ay effect would

be expected over such a short interval, regardless of whether or not a change

in relative use of the Industrial vs. Control areas would occur over the longer

term. If systematic surveys are continued in subsequent years when

industrial activities in nearshore areas are consistently greater (or less) than

in 1990-1991, a corresponding statistical test of the ay term can be used to

evaluate whether there is a corresponding long-term change in oldsquaw

densities.

The area term in the ANOVA and ANCOVA results is also instructive.

The area term provides a test of the hypothesis that oldsquaw densities were

the same in the Industrial and Control regions during the 1990-1991 period as

a whole. (This should not be confused with the original HOI that “There will

be no detectable change in relative densities of molting male oldsquaws in

selected Beaufort Sea index areas.”). All ANOVA and ANCOVA models

failed to reject this hypothesis of overall equal densities in the two study

areas. Inspection of Figure 7 suggests that – in 1990-1991 – there was no

consistent difference between the Industrial and Control regions in oldsquaw

densities in barrier island habitats or mid-lagoon habitats. This is a

noteworthy result, given the history of industrial activity in the Industrial

area not only in 1990-1991 but also in prior years. However, densities along

the mainland shore were substantially higher in the control area, as
Sunnmzry and Conclusions

mentioned previously, and as reflected in the significant h(a) interaction

terms (Table 7). his is believed to be at least partly the result of a difference

in the type of habitat along the mainland shoreline in the Industrial vs. the

Control areas. It should be kept in mind, however, that there is no a prior

reason to expect that oldsquaw densities would be the same in the two study

areas. It is a change in the relative densities in the two study areas that is the

true test of I&l.

Although neither the area term nor the year x area interaction term

was statistically significant in the three runs of the model in 1990-1991 data, it

is somewhat worrisome that the overall mean density of oldsquaws in the

two study areas (pooled), especially on barrier island transects, declined

steadily over the 1989-1991 period. It is also noteworthy that the proportions

of oldsquaws, relative to other species of marine birds recorded during

surveys, appears to have declined in recent years (Append. 9). The possibility

of a long-term downward trend in oldsquaw numbers can only be evaluated

if data from additional years and over a wider area become available.

H02 concerns the possibility that human activities in particular parts of

the Industrial (or Control) study areas may have localized influences on

oldsquaw densities. In our analyses of variance and covariance, the

disturbance term, d, provides a test of ~2 after allowance for other factors

such as area, year, habitat, specific transect, various interaction terms, and (in

ANCOVA) covariates such as wind speed or wave height. Based on two years

of systematic sampling, there is no statistically significant evidence of such a

change; the d term was non-significant in all ANOVA and ANCOVA models.

However, it is interesting that in the Industrial area in 1990 — the one

situation with many cases of potential disturbance — there was a

nonsignificant tendency for lower oldsquaw densities on transects with

human activities.

The test of &2 is potentially more meaningful than is the test of Hol

when only a few years of systematic data are available, given the much larger

number of error degrees of freedom for the present test. Nonetheless, great

caution is necessary in interpreting the results. There were relatively few

transect/data combinations with known human disturbance in 1990, and

virtually none in 1991. In this situation, the test has little power to detect a

biologically significant disturbance effect even if a strong effect exists.

65
Summary and Conclusions

As mentioned earlier, an important issue in a monitoring program of

this type is the degree to which the sampling and analytical procedures are

able to test critical hypotheses. In this study we considered the degree to

which the current model can be improved, i.e., made more powerful, in order

to detect smaller percentage changes in the adjusted mean density of

oldsquaws for the two terms in the model (disturbance and year x area

interaction) that relate to the two hypotheses being tested. We assumed that

current conditions would prevail in future years, i.e., only three levels of

disturbance at the same relative frequencies would continue to be recorded,

and residual error within each cell (year, area, habitat and transect

combination) would remain the same.

It is clear that for localized dis~bance effects, the current annual level

of sampling (seven or eight surveys/season) is adequate to detect, over a 2-

year or longer period, a 7-8% change (at a 95% confidence level) in adjusted

mean oldsquaw density on disturbed vs. undisturbed transects.

For the year x area interaction term, however, the current level of

sampling is sufficient only to detect a 130-14070 change in the adjusted mean

density of oldsquaws over a 2-year period. Although the performance of the

model is not appreciably improved by increasing the number of samples

within a year, it is markedly improved if the number of years of sampling is

increased beyond two years. With three years of sampling one could detect a

50% change in adjusted mean density. The current analysis indicates that a

12% difference could be detected only after about 11 or 12 years of surveys.

We are confident that the monitoring plan presented above is the most

appropriate and statistically defensible approach given the present state of

information. However, as mentioned in our previous report (Johnson 1990),

it is inevitable that, after several years of data collection and subsequent

analyses, it will be necessary to further modify some aspects of the field

procedures or some of the analyses to further improve the study.
Literature Cited 67

LITERATURE CITED

Bliss, C.I. 1970. Statistics in biology, statistical methods for research in the

natural sciences, Vol. 2. McGraw-Hill, New York, NY.

Brackney, A.W. and R.J. King. 1991. Aerial breeding pair surveys of the

Arctic Coastal llain of Alaska: abundance and distribution of waterbirds

1986-1990. U.S. Fish and Wildl. Serv., Migratory Bird Manage., Fairbanks,

AK.

Brackney, A. W., J.W. Morton, J.M. Nell, and M.A. Masteller. 1985.

Movements of molting oldsquaw within the Beaufort Sea coastal lagoons

of the Arctic National Wildlife Refuge, Alaska, 1984. ANWR prog. rep.

no. FY85-12. Vol. 1, pages 350-361 @ U.S. Fish and Wildlife Service, Arctic

Nat. Wildl. Refuge coastal plain resource assessment. 1984 update report,

baseline study of the fish, wildlife, and their habitats. U.S. Fish and Wildl.

Serv., Arctic Nat. Wildl. Refuge, Fairbanks, AK.

Brewer, W.A. Jr., H.W. Searby, J.L. Wise, H.F. Diaz, and A.S. Prechtel. 1977.

Climatic atlas of the outer continental shelf waters and coastal regions of

Alaska, Vol. 3, Chukchi-Beaufort Sea. BLM/NOAA, OCSEAP. Anchorage,

AK.

Burdick, R.K. and F.A. Graybill. 1992. Confidence intervals on variance

components. Marcel Dekker, N.Y.

Craig, P. C., W.B. Griffiths, S.R. Johnson and D.M. Schell. 1984. Trophic

dynamics in an Arctic lagoon. ~. P. Barnes, E. Reimnitz, and D. Schell

(Eds.). Alaskan Beaufort Sea ecosystems and environment. Academic

Press, New York, NY.

Dames and Moore. 1984. Beaufort Sea monitoring program workshop

synthesis and sampling design recommendations. Rep. for U.S. Dept.

Commerce, NOAA. Juneau, AK.

Draper, N. R., and H. Smith. 1981. Applied regression analysis, 2nd ed. John

Wiley & Sons, Inc, New York, NY.
68 Literature Cited

Garner, G.W. and P.E. Reynolds. 1986. Arctic National Wildlife Refuge

coastal plain resource assessment. Final report, baseline study of the fish,

wildlife, and their habitats. U.S. Fish and Wildl. Serv., Arctic Nat. Wildl.

Refuge, Fairbanks, AK.

Green, R.H. 1979. Sampling design and statistical methods for

environmental biologists. John Wiley & Sons Inc., New York, NY.

Griffiths, W.B. and R.E. Dillinger. 1981. Barrier island lagoon ecological

process studies: Final Report, Simpson Lagoon. Part 5, Invertebrates.

Pages 1-198. In: Environ. Assess. Alaskan Contin. Shelf, Final Rep. Prin.

Invest. Vol. 8. BLM/NOAA, OCSEAP, Boulder, CO. NTIS PB82-

195942/AS.

Hochbaum, H.A. 1955. Travels and traditions of waterfowl. Univ.

Minnesota Press, Minneapolis, MN.

Huitema, B.E. 1980. The analysis of covariance and alternatives. John Wiley

and Sons Inc., New York, NY.

Johnson, S.R. 1982a. Continuing investigations of oldsquaws (Ckngu/a

hyemalis L.) during the molt period in the Alaskan Beaufort Sea. Pages

547-563 ~ Envir. Assess. Alaskan Cont. Shelf, Final Rep. Print. Invest.

Vol. 23. BLM/NOAA, OCSEAP. Juneau, AK. NTIS l?B85-212595/AS.

Johnson, S.R. 1982b. The oldsquaw duck. Alaskan OCS species account

series. Rep. by LGL Alaska Research Associates, Inc., for Office of Marine

Pollution Assessment, National Oceanic and Atmospheric

Administration, Juneau, AK.

Johnson, S.R. 1983. Investigations of nesting and molting birds in the

vicinity of Long Island, Alaska, 10 July to 2 August 1982. Rep. by LGL

Ecological Research Assoc. Inc., for Shell Oil Co., Houston, TX.

Johnson, S.R. 1984a. Prey selection by oldsquaws (Chmguh hyemudis L.) in a

Beaufort Sea Lagoon. Pages 12-19. In: Marine birds: their feeding ecology

and commercial fisheries relationships. Can. Wildl. Serv. Spec. Pub.,

Ottawa, Canada.
Literature Cited 69

Johnson, S.R. 1984b. Habitat use and behavior of nesting common eiders and

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Johnson, S.R. 1984c. Chapter 2, Birds and marine mammals. Pages 265-324

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24129-579. NTIS PB85-212603/AS.

Johnson, S.R. 1985. Adaptations of the long-tailed duck (Clarzguh hyemalis

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25 Aug. 1982, MosCOW, USSR. 18 (1):530-540.

Johnson, S.R. 1990. Monitoring Beaufort Sea waterfowl and marine birds.

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Anchorage, Alaska.

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process studies: Final Report, Simpson Lagoon. Part 3, Birds. Pages 109-

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Vol. 7. BLM/NOAA, OCSEAP. Boulder, CO. NTIS PB82-192113/AS.

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BP Exploration (Alaska) Inc. Anchorage, AK.
70 Literature Cited

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marine environmental impacts of West Dock Causeway. Rep. for ARCO

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reproductive habits of mallards, gadwalls, and blue-winged teal. Wildl.

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Milliken, G.A. and D.E. Johnson. 1984. Analysis of messy data, Vol 1:

designed experiments. Van Nostrand, Reinhold. NY.

Troy, D. M. and S. R. Johnson. 1982. Prudhoe Bay waterflood project bird

monitoring program--l98l studies. Rep. by LGL Alaska Research Assoc.

Inc., for U. S. Army Corps of Engineers, Alaska District. Anchorage, AK.

Underwood, A.J. 1991. Beyond BACI: Experimental designs for detecting

human environmental impacts on temporal variations in natural

populations. Aust. J. Mar. Freshwater Res. 42:569-587.

Wilkinson, L. 1987. SYSTAT: the system for statistics. SYSTAT, Inc.

Evanston, IL.

Wirier, B.J. 1971. Statistical principles in experimental design, 2nd Ed.

McGraw-Hill, NY.

Wirier, B.J., D.R. Brown and K.M. Michels. 1991. Statistical principles

in experimental design, 3rd Edition. McGraw Hill, N.Y.
Appendices 71

APPENDICES
‘2 Appendix 1

Appendix 1. Individual on-transect densities of oldsquaws on transects in the

Jones-Return and Stockton-Maguire-Flaxman islands, Alaska Beaufort

Sea, 5 June to 23 September 1977-1984 and 1989-1991.
Appemdix 1. Individual on-transect densities of okkquaws on transects in the Jone+Retum and Stockton-Maguire-Flaxman islands, Alaska Beaufort Sea, 5 June to 23 Sep tmnber 1977-1984 and 1989-1991.”

‘3uwey D.te 0verd2 Siudv Area Barrier Island Trawed No& and kens %mpled (e-q. km)

1%%1991 Tcd 3q. Ian.

Non-barrier Isfand Transect N- and Areas !kmPled (w. km.)

Total U Oldsq Overall L3em. 23 31 201 202 133 13A 133 136 10 11

On-karwx+

12 13 22

On-tralwxl

24 23 3Q 32

Oldsq.(q. h 452 .548 8.48 608 6.0.9 4.84 5.40 &28 9.48 6.04 5s6 3.48 3.64 4.40 4.80 500 5.72

;:.:::::::>*.>.>

5 )um77 .%08 3 0.06

. . . . . . . . . ...> . ..\

O.lm alw ~:,:,:j$i>:~~+:

:,q,~+:..;i

m JuII.77 54.08 34 a63 7.53 230 .y::::>$frl>*

5 JUI.77 54.08 745 1?.78 saw 61.18 W*,::+

28-29 Juf.77 54.08 S3.150 &i3.46

.+?:%’<*.

109a80

15 Au@

345.07

28.32 13002 63s.a3 1894.34 149671 ~:y+: :~~~$+! .<,

30 Auf@7 34.08

.:.:.:.:.:..1;S2<

4237 79.27 443.40 48.68 ~$$+;

22 %.@ 34.08 14937 276.22 75.71 ass *,..+< .

xj+&~

23 Jtm.78 74.4 107 1.44 15.93 1.76

5 JuI.78

224 aoo :fi.++:$

11208

,

:

.

:

.:.: .:3:&, aoo

3X5 29.49 &35 637

1.05

92.22 264.14 1.31 1054 222 080 Xi$%p$

15 JUI.78 74.4 3W72

0.00

440.47 86.95

11.71

1 M9.74 16S5W 1148.63

X.=.:..-.:,.*

25 JU3.78

,::*.A+

111.44 %95 87SM 0.00

045 6.47

465.49 S?Q07 ‘22&34 7.319 0.00 48.15 165.43 ;;fi:+a . . . . . . . ...>.,..;,,;;

3-6 Aug.78 117.72 12141

aoo

103.13 36.5o

3.50

457.C4 375.59 272.35 la53 3.86 464.81

15 AuB.78 74.4 18307

389.17 ;~~~g~ 0.C41

246.06 91.81

8.04

352.82 178597 tO.53 :::::::,f::.+.::<<$

25 AU8.78 111.44 19369

>:,2.:::,y<<* 45.43

17S.81 60619

14.86

346.13 578.89 18.92 1Z99 294.63 10ZS.U3 . .. . . . ., 

. .. . . . .+.- <f.?

36 sep.78 117.72 193s4

2.:.:.:.,...F. 273

169.48 25A5 m .94

3437

39.a3 313.32 113.%3 17.77 244.26

1SS+78 68-68 43s3 6 s %

702..% :;zlii~ 76.32 33.64

s4.07 ax 11.C4

23*

99.m ~~;i$:$<

.78 117.72 21762

74.32

184.86 337.52 ls.tm

.x.x.x.x.:<.>

11.79 7.73 12599 1377.69 364.26

22 Jm.79 74.4

129.62 ~~ji&A

33a

84.55

522 18.81 44.37

. . . . . . . . . . . . . . . . 29.10 . ...

3.S4 a99 :,:,:,:,:::, :$?*.>.

23 Juf.79 117.72 24.W3

. . .. .. ..,.:.,.>,;<. a23

203.45 0.44

z62

7W.18 404.s3 702.30 35a % 2273

31 Au!++ s+79

382.22 ~.n ~;~*~>j

. ...> .. >...:

3A.28 5s&2 8650 5.97

3.64 4.s5

23705 76.77 eA.67 +!w~;<>

22 S+79 74.4 3670 7621 18.36 58.W 28U28 170.23

,y~x.::::;j ::,:::::::;,>*

2 AuF,.3Q 117.72 27826

5.12

226.s7 63.50 12r6.65

21.50

81262 438.65 291.45 20,(4 162.02 5WJ.62 ~~~~},~

18 JuI.81 63.36 1775

0.04

27.36 15.49

71.68

10s88 . . . . . . . . . . . . . . . . ....

<::.2%%:

29 JuI.81 71.44 1W52

1.d5

1S0.49

8523 Llls

98A5 247.44 89293 .,.:.:.:.:Z*.7<V<

:::w$~.::+

2 A@l 9512 13267

O.co aco

160.52

a62

71.02 154.03 23561 275.m 10609 26243 23+3.15 294.11 ~g$;%

12 Au&81 59.% mw

74.5S

18.18 67.92 123a

1 2za3

92.43 *..:.:.:Y.. ..+...:+%..j.~

29 Au@ 71.44 1432

~:.,:.,,.:.:...:~ 0.02 1.36

2a04 2.03

a m

91.51 L?2.3U ;;:j;:,j~y<:j:

11 Sep .81 71.44 19976

34.07 13.86

279.62

mm

1596,24 123X0 117.27 :::::::,:::.: ~.:~~

18 JuI.82 9536 3317

:~~.:&,. 137.22

4a03 44.a’3

1469.09 510.42

124.47 144.34 10Z3U ;: ~,,:~>

31 Jul.62 71.44 9214

6.32 23.43 a21

128.93

240

126s9

.:.:.:.:.,:.:.: .<.+ 73.43

21Z97 38s.s3 . .. .. . . . . . . .W$

14 AL@2 77.12 19216 232.76

IC4.40 42.73 ’292

161.54 1602.46 83a44 1053.62 ;Xiiiii:;

28 AIJ@2 9X46

HXW<.*. . 93.L13 10.23 2a4

w 59.23 141.81 281.51 14269 73.6s +:.:.=$:$ & . ~:>i~::

23 s@2 9534 8867 9298

4.40

3827

42.fXi 7292 Soo 7.69

1s.s3 157.31 29.63 . . . . . . . . . .. . . ...4..

:.:.:.:.:+....,

HJuI.S3 7273

0.03 4.09 5U21 a20

63W s669 84.%

47.90 .:.wz.$$:?;y:: :::,:j,:>.fii..

8 Au&S4 13s88 2839?

10.45

212.12 693.81 263.73 285.73 736.33 347.m 348,26 254.81

6 AL@9 176.% 31334

87293 <~;p.~

176.$0

loam fo.64 aro

84.73

I zoo 70.28

13ffi.90 92.02 13.65 739.62 321.49 7L23.70 1131.21 ]~{;$j a02 2.48 O.oa

8 Aug,89 17696

0.02 O.co O.oa

sxdo 193.12

0.00

23a53 20W.21

10.49

817.02 3n5A3 39Z43 23.76 269.07 , ~2~ :::<;~j<i; a21 0.00 O.ml

9 Au&.89 176%

O.w

44611

0.03 0.00 aoa

252.10 467.92

0.00

2oz5.oll 628.17 370,23 731.25 45X33 435.19 2W0.80 ~~{ IZOO

18 JuI.92

1.82

186.36

0.00

194a2

0.L32 %.15 7.95

104.10 97.79

1.41 0.04

156.87 124.77 55.61 2s4.ss 22235 244.63 3%.83 %:;~:+gij

2D JuI.W 186.36 1%97

15.3s 341

102.47

1.23 1.81

50.88 15L94

24.63

lla33 210.m 35393 155.79 486.67 4s4.S5 ZZ?xs$$

73 JUI.92 1s6s6 16340

12.36 1.14

142.41

1.02

99.78 ;,.,.,.,.,.,. : >;f* 9.68 15.36

394.19 59LU8 397.s3 261.92 239.30 446.11 549.68 fi:e:?:?y

3 Au@2 18U6

6.87

423%

.,.,.,.7..,,.:. ~~ 1.14 a m

228.s

Lloo

3%35 264.08

15.03

61Z92 872m 53zLm 353.61

4 Au8S0 16628

1146.48 141608 &j:#j+

2%14 d

O.ro .318

173.97

1.23 aoo

17.C4 6Z68 188.15

122.%

247.m 236.m 5289 139.44

9 Atia.92 14120 16953

1218.31 $~yfij$ O.co Z73 0.(23

118.S9 36.73

aoo 15693

91.37 111.74 81.41 348.93 S33.47 431.85 ~.m yj~*~$

16 AL16.W 722S L%.?? 9210 +++3? 22.73 O.w

26.11 24920

54s3

671.60 18.03 ~$$$<$g

20 Au&92 184.36 34820

4.W aw

186.34 97.57

0.17

23028 211.74 40.79 %162 1347.11 12S5.19 933.92 ;g@;+%

2 Sep.xl 18636 3655

04-2 31.39

4660 4A2

625

%S2

.. . . . . . . . . . . . . . . . .

3.23

9.15 35.69

14.16

431.48 437.81 129.44 262.26 <~~.~+fi~

4 5+90 143.20 67z9

0.00 am

4k98 0.44

am 0.00

34.15

0.52

29.46 17.76

5*Si2

199.51 67.56 16s.56

14310 18648

1%.18 W$i+i#i a23 Z71

13036 5.09

7.52

19.19 2758 11.18 727.32 244.11 116.67

18 Jul.91

146&40 Xxi;iii$li!

72.42 3739

4.32 3.75

51.20

O.w

17.70 104.05 17a75 233.93

<: ,:::,:~:.:,,,:.,

;, ~::m ;~i~>:

19 Jul.91 11560 7253 61.88

Z05 aoo

75.00 74s26

1.22

174.29 162.49 78.13 20.43 9204 1 %.22 W%%%*2

20 JIJ191 11.560 6963

9.55

6a23

aoo

60.23

. . . . . . . .,w :,:.:.: 15.03

1 E.62 31.26 21Z23 29.n 15.92 21.67 1 ml.12 ~~g~ ~

22 JuL92 115.60 %i3 836-3

9.5s 0.21

67.o4 112.68

23.LM

25236 ss.n 8a43 3678 149.81

4 Au@ 11s60 SS70

1s5.19 Fgxzi.z ;..........,.,.,,:.:* 341 O.w

751m 90.27 91.73

33.22

98.23 136.6s 97.37 11164 115.W

10 Au@

63.54 $jj::::jj+i<,j

113.60 f#23 57.34

7.03 Zml

23.44 28.3S

41.% g

10991 88.82 131..58 3616 7333 33.02 j:mi.j<

14 Au8.91 139.00 63s3 49.52

1.36 1.46

21.90 144.19 l&9s

BY...........+.+; 144.58

162.s3 87.17 4669 33.19 30.al jj~+~ 3

16 Au8.92 138.76 626s 40.72

9.32 313 55.%

6.19 3&33 86.79 Z5.w 190.30 IZ81 4%33 %.52 +, . . . . . . . . . . . . ..i. >..:.::<.:.:.:.,.,* %

21 AuK,91 158.76 5248

2.n Z27 375

?a26

0.60

12.S3 57.64 3S.61

36.89

63.82 154.61 3079 2556 7,32 =$; -.

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

5.22 63-3 a42 17.20 17.48 R

:;~::::~

n . 58 S8 58 32 44 )-2

Mean =

57 56 31 31 31 ~ ~:~jjt$ 3 3

103.32

3

13%6.79

3 23

133.s3

30 31 16

12a40 35932

41

317.m 247.02 296.11 23,s72 314.46 334.09 xwi~j aov 1.43

ad. = 44.62

0.00 O.co

n515.11

27.m 43.39

131.35

21.51 MS

Z5A.56 528.90

33.76

429.82 324.n 2Z5.39 364.32 3W.$V ,“’:+’+33 515.93 $;:,;$>$$

OA 1 0.83

a12 Km 0.02 O.w

Cv. = a 98

44.3s 205.93

2.11

9Z04

1.47 136

5.29

1.31

40.63

0.93 1s3 1.04 a97 $..~g$ 1.73 O.su 0.00 O.w 1.63 4s6 4.28 1.52 1.20

$.: :.*.,:,:: ,,.>$

● S?e Figures 3 and 4 for tranu!ci katfms

4

u
Appen dix 1. Continued.’

%rvey Date Non-bank Island ‘ftamect No& and Areas 3ampkd (q. km)

1%’7-19913330515233 60616263101 Kr2301 302402402 180 181 182 183 190 191 192

7.52 4.n 4.24 4.28 3.64 .576 4.44 4.32 5.36 8.44 6.00

193

6.92 5.24 7.28 5.64 5.64 4.24 3.16 5.64 6.26 4.48 7 9.84

5 Jun.77 O.al 0.C42 O.co o.m am 213

‘m JUII.77 o.m 0.c4

5 JuU7

o.m o.m am z34

0.12 5.011 4.92 o.3a 119.23 44.68

23-29 JuH7 0.12 6.67 7223 1W3.73 15W.53 2741.41

15 Au&77 1124.73 3546

30 AuK.77 5.43 0.17 143.3s 244.08 42857 71.63

22 %m.77 5.21 0.83 6X45 M38.7.9 27653 1334,04

23 Ium7& O.(K3 O.ca o.m o.m a41 aoo

5JUI.7B 0.24 0.00 3.s0 7.44 343 a33

15 JuJ.78

24.47 107.78 aOO O.IX)

Z% 1.67 14.74 72.14 29.69 111.03

25 JuI.7B 18.26 2.17 6_L90 6Z02 28.43 7.09 4.40 11:9 mm o.m

.5.6 Au&78 0.36 0.33 =.76 75.76 11.40 aoo mmo 117.62 am o.cn

15 Au&78 o.m 0.00 4.77

25 Aug.78

Io.88 am am

5Z37 43.33 24.86 43.42 aW 1.77 1.07 aOO aoo o.m

5-6 3ep.78 2370 14..54 11.13 14.12 a14 aoo 3+79.15 n54.m 206.39 18.97

153ep.7Ll 16315229.30 X4.24 25..9 2C6

23*

13.63

.78 2.61 O.Cml 269.24 56.30 19.69 114.54 438.03 79.0

1 aoo mz.w

22 JUII.79 o.m o.cm o.m

28 JU2.79

o.m am a33

0.00 9.43 131.21 136.45 41.48 17.73 1.9.34 nw,m 23.26 o.m

31 Au&-l S-+79 6.37 43.33 sa.70 640.29 am am

Z2%p.79 19.79 9.3

4 5.76 0.9

2 AU$..8O

7610 16Z39

18 JuI.81

O.W 0.00 25Lw37 61.33 134.71 213.43 aOO 16.98 0.00 O.(M

IOZ94 0.00 53.23 2639

29 JUI.81

9.75 &76

O.W 0.17 129.48 177.67 alll a%

2 Aug.81 O.fHl 0.c4 52.16 23359 37.23 W.44 3222 20.05 0.00 O.W

12 Au&81 o.m 0.c4 7.37 6.87 az7 4.79

29 Au8.81 8.03 033 15.32 8.02 am am

116ep.81 71.36 18.W 194..97 20.42 a69 1.24

18 JuI.83 Z& 0.12 0.00

31 Jul.32

1Z36 5S.34 5.!3 9.04

0.12 0.00 33699 193.31 27A0

14 Aug.82

11.70

1.18 o.c4 28.I2 0.38 am am

28 Au8.82 19.95 1031 15.o 58.09 *.o3 aoo aoo

23 E4p.82 419.f4 8.S4 0.00 234.77 197.90 17.17 7334

29 Jul.83

8 AuR.84 aoa 7.94 0.00 209.54 387.60 O.CO

6 AuE,89

aCK7 102.76 2W.19 113.37 7.8o

3.18 aoo o.m am am 000 am acd o.m o.oa 7xoI 318.70 aoo am 322.29 o.m

8 Aug.89 o.m aoo 0.00 aw aoo 0.00 aco 0.00 0.00 0.00 79.48 0.37 4.40 2358 25.43 0,02

9 Auu.89 o.m 7.08 5.79 am am o.m am 0.c4 o.m 033 14.43 O.w 0.00 al!u 211.24 39.0

1

18 JuI.92 aca 3&89 41.32 a@2 aCXl 8.29 0.00 227.75 26.72 7.28 am

20 JUL92 1.20

62.68 145.32 34.88 o.m 49a03 3n32 K.5.m 6362

a35 IS az2 a36 mm 0.00 147.40 33.21 7.m a02 144.72 66.75 34.30 21.28 20332 24263 160.14 77.%

23 jd.w aoo am 223 aoo 0.00 039 0.00 39.39 17.75 3 Au3..93z47 a33 221

B 236 a19 2.46 387.64 45246 216.37 3125

1.06 am 0.00 aoo am H-63 230 13a64 26.72 am 4.’26 67.61 34.25 4.84 0.03 1.97.03 802.23 133.00 maw

4 AuE.90 aoo o.m 0.00 11Z86 34.35 a% 0.M2 1320 29.25 26.74 2.66 3193 277.o3 150.37 3ZOl

9 Aug.90 1.20 39.97 .%30

16 AU8.SO

a14 a18 31.16 41.04 14.15 O.CO 3L124 L39.06 5600 2M2

a27 7.23 439 261 am

20 mlg.xl 2.93 0.00 O.W Z21 0.00 4.27 0.00 19.32 222.14 4.% 1.06 0.35 aoo 27.32 o.m 43.09 406.s72 234.43 2,13

2 sep.’xl 14.10 aoo 0.22 am aoo o.m 0.00 15.46 1.34 69.(0 0.18 44.34 13.21 a97 1.93 18.20 40.83 3.S00 4.27

4 Sep.%7 598 19.M 6.11 am 0.33

5*.40

37.32 8.02 237.21 3.n 24.21 72.65 31.00 5.1s

14.63

18 JuLY

1.54 3.24 1.10 a33 0.00 0.47 62.o2 192,73 3.01 140.40 48.86 20.33

0.13 12.43 5.7

3 5.22 Z34

19 Jul.91 4.63 22.40 12.60 7.42 a18

20 Ju191 14.36

2306 7.53 678 0.03 43.7o 109.04 Z129 43

7

?0.35 15.46 11.40

22 JuL91

4.% 5.11 7.78 4.84 0.35 11.78 70.76 3300 8,84

abb W.89 10.69 19.29 ala 64,90 63.44 116.67 0.(!0 67.m 101.34 30.71 .%.81

4 Aug.91 13.% 76.39 86.26 1648 1348 3644 34.67 61.24 0.3S 49.32 78.13 83.14 44.31

10 Au8.91 10.24 69.94 10.31 13.32 1.77 W2.11 16.04 67.44 0.53 39.65 94.64 38.43 17.28

14 AU8.n aoo 430 a

m 0.39 0.00 41.47 16.41 2?4 14.18 0.00 2830 75,39 0.01 101.43 10.71 6571 5,89

16 Au8.91 1.33 2544 65,94 23.23 1.31 0.47 1.67 138.87 14752 1.92 O.GU 35.21 2333 33.72 0.18 19.93 9.60 9.71 8,94

21 AuR.91 1.73 9.m 430 11.C6 30.22 0.47 10.33 217.77 9.16 39 a18 10.21 30.66 46.32 0.02 la19 33.26 Z36 2A4

n. 2433331112 121146463456 34 W31313131 1818181

s

Mean = 2211 1.03 2.36 1.93 alxl 625 10.04 306 Z92 1226 8.s3 91.24 123.85 78.82 93.63 36,39 86.60 38.24 1434 114.23 194S6 83.43 29.3.5

ad.= 34.90 1.84 4.09 334 0,00 1243 21.14 7.10 9.06 3&39 ?4.69 119.34 288,24 263.10 410.59 9623 241.7f 81.83 4334 143.19 204.70 7382 2R41

Cv. . 384 1.23 1.73 1.73 am 1.% ZIO z32 aII 2.48 3.93 1.30 233 3.34 4.38 1.71 279 1.41 3.02 1.27 1.05 0.E6 100

● *e Figures 3 and 4 far trane-set katicuw.

i-i
Appendix 2 75

Appendix 2. SYSTAT multiple regression analysis output for the complete

season (5 September to 23 September 1977-1984. and 1989) in the central Alaska

Beaufort Sea.
76 Appendix 2

USE ‘HD84:f3ppl ications:Statistics:Systat Modules:863txt’

‘!?RIRBLES IN S$%TfiT FILE RRE:

DENS DENSTRRN YERR DRY TIME

DEPTH DEPTRRN WESTERST WESTRRN DIST

D 1 STRRN WSPD WDIR ORDWND ORDUTRRN

NCOMWND NECOMWND ICE I CETRRN WRUEHT

DRYTRRN WSPDTRRN WRUETRRN HRB 1 TRT RRER

CRTEGORV HRBITflT=5

CRTEGORV RRER=2

MODEL DENSTRRN=CONSTRNT+VERR+DRV+DRYTRRN+TlME+WESTERST+WSPD+WD IR+USPD*WDIR+,

ICETRRN+WRUETRRN+HRB lTRT+RRER+VERR*fIRER+HRB ITRT*DRVTRRN+HRB ITRT*T]HE+,

HRBITRT*USPD+HRB ITRT*WDIR+HRBI TRT*WRUETRRN+HRB ITRT*ICETRRN

PRINT LONG

ESTIMRTE
Appendix 2 77

YfSTflT UERSION 3. 2 COPYRIGHT, 1988 S’fSTRT, INC.

IOU f3RE IN MGLH MODULE

)EP URR:DENSTRRN N: 474 MULTIPLE R: .758 SQUf3RED MULTIPLE R: .574

-1

ISTlflRTES OF EFFECTS B = (X9X) X’Y)

DENSTRflN

CONSTRNT

YERR

DR’f

DRYTRRN

TIME

WESTERST

USPD

WDIR

USPD

WDIR

I CETRRN

WfIUETRRN

fiflB I T(3T

HRB I TflT

HRB I TflT

HftB I TRT

f3RER

VERR

RREf3

HRB I TRT

DRVTRRN

HRB I TRT

DRVTRflN

HRB I TRT

DRVTRRN

HRB I TflT

DRVTRRN

HRB I TRT

TIME

HflB I TRT

TIME

Hf3B I TRT

TIME

1

2

3

4

1

1

1

2

3

4

1

2

3

-2.179

-0.038

0.097

-0.001

0.001

-0.054

0.003

-0.003

0.000

-0.047

-0.376

-0.229

3.911

-4.105

1.513

-0.010

0.016

-0.000

0.000

0.000

0.000

0.002

-0.003

0.002
78

HRB I TflT

TIME

H(3B I TfiT

WSPD

HRB I TRT

WSPD

HflB I TRT

USPD

HflB I TRT

WSPD

HRB I TflT

WDIR

HflB 1 TflT

UDIR

HflB I TflT

UDIR

H17B 1 TRT

UDIR

HRB I T(3T

URUETRRN

HflB I TfiT

WflUETRfiN

HflB I TRT

WR.JETRflN

HRB I TRT

UflUETRflN

H(3B I T13T

I CETRflN

HfiB 1 TRT

I CETRRN

HRB I TRT

1 CETRRN

HfiB I TfiT

I CETRRN

SOURCE

VEflR

DfiV

DRVTRRN

TIME

JESTERST

IJSPD

WDIR

lKPD*

l.lDIR

Appendix 2

4

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

-0.002

0.004

-0.006

0.062

-0.044

0.001

0.000

-0.002

0.004

0.227

0.329

-0.754

0.080

-0.357

0.001

-0.051

0.119

SUM-OF-SQURRES

3.632

55.006

0.412

2.849

1.442

0.005

2.330

4.695

RNRLVSIS OF URRIRNCE

DF MERN-SQURRE F-RRT 10

1 3.632 1.296

1 55.006 19.622

1 0.412 0.147

1 2.849 1.016

1 1.442 0.515

1 0.005 0.002

1 2.330 0.831

1 4.695 1.675

P

0.256

0.000

0.702

0.314

0.474

0.965

0.362

0.196
Appendix 2 79

0.298

19.075

11.818

0.000

0.106

6.805

4.216

0.000

0.745

0.009

0.002

0.989

I CETRRN

URUETRflN

HRB I Tf3T

RREfl

‘f Ef3R*

f3RER

HflBlTf3T*

DRVTRRN

HRBITRT*

TIME

H17BiTflT*

USPD

HRBIT13T*

UDIR

HfIBITflT*

UFIUETRi7N

HRBITRT* i CETRfIN

0.298

19.075

47.270

0.000

0.328

1 0.328 0.117 0.73

2

26.627

4 6.657 2.375 0.051

152.752

4 38.188 13.623 0.000

22.485

4 5.621 2.005 0.093

10.221

4 2.555 0.912 0.457

26.491

4 6.623 2.362 0.052

25.971

4 6.493 2.316 0.057

ERROR 1213.824 433 2.803
Appendix 3

Appendix 3. SYSTAT multiple regressicm analysis output for the oldsquaw

moIt period (15 July to 21 August 1977-1984 and 1989) in the central Alaska

Beaufort Sea.
Appendix 3 81

>(.JSE ‘HD84:Rpp lications:Statist ics:Systat Ilodules:moltxt’

JflRlftBLES IN SVSTRT FILE RRE:

DENS DENSTRf3N VEflR DRY TIME

DEPTH DEPTRRN WESTERST IJESTRRN DIST

D 1 STRRN USPD UDIR ORDWND ORDUTRRN

NCOtl14ND NECOHWND ICE I CETRflN WRUEHT

DRVTRRN USPDTRfIN URUETRRN HfiB I TflT RRER

ECRTEGORV HRBITRT=5

~CflTEGORV RRER=2

‘MODEL DENSTRRN=CONSTflNT+YERR+DRV+DRYTRRN+TlHE+UESTERST+USPD+UDlR+USPD*UDlR+,

IICETRflN+WfKJETRflN+HfIB ITRT+RRER+VERR*RRE17+HRB ITRT*DRYTRRN+HRB lTRT*TlflE+,

IHRBITRT*WSPD+HRB lTf?T*UDIR+HfIB ITRT*WRUETRFIN+HRB lTflT*lCETRflN

‘PRINT LONG

‘ESTIMRTE
82 Appendix 3

S’fSTRT UERSION 3. 2 COPYRIGHT, 1988 SVSTRT, INC.

VOU RRE IN HGLH MODULE

DEP URR:DENSTRRN N: 275 MULTIPLE R: .825 SQURRED MULTIPLE R: .681

-1

ESTIMRTES OF EFFECTS B = <X’X) XIV)

DENSTRflN

CONSTfINT

VEflR

DRY

Df3VTRflN

TIME

UESTEfiST

USPD

WDIR

WSPD

WDIR

I CETRf3N

WRUETRRN

HflB I TfiT

HfiB I TRT

HRB I TfiT

HflB 1 TflT

flREfl

YERR

RRER

HRB I TRT

DRYTR17N

HRB 1 TRT

DRVTRRN

HRB I TRT

DRVTRRN

HRB 1 TRT

DRYTRRN

HRB I TRT

TIME

HfIB I TflT

TIME

HfIB I TRT

TIME

1

2

3

4

1

1

1

2

3

4

1

2

3

-1.269

-0.076

0.099

-0.001

0.001

-0.128

-0.025

-0.007

0.000

0.374

-0.457

-1.645

3.244

2.415

3.258

-1.121

0.153

0.000

0.000

-0.000

-0.001

0.002

-0.003

0.001
Appendix 3 83

HRB I T13T

TIME

HflB I TfIT

WSPD

HRB i Tf9T

USPD

HfIB I TRT

USPD

HRB I TflT

WSPD

HRB I Tf3T

UDIR

HRB I Tf3T

WDIR

Hf#B I TRT

UDIR

HRB I TflT

WDIR

HflB I TRT

WIUETRflN

HRB 1 TfIT

Uf3UETRf)N

HfIB I TflT

UfRJETRRN

Hf3B I TflT

URUETRRN

HRB I TfIT

I CETRRN

H13B I T13T

I CETRRN

HRB I TRT

I CETRflN

HRB 1 TRT

I CETRRN

SOURCE

VE13R

DRY

DfPfTRRN

TIME

JESTE(%ST

WSPD

UDIR

WSPD*

UDIR

4

1

2

3

4

1

2

3

4

1

2

3

4

1

2

3

4

-0.000

0.003

0.004

0.034

-0.077

0.003

0.003

-0.002

0.000

0.359

0.198

-1.030

0.210

-0.322

1.181

0.195

-0.892

RNRLVSIS OF URRIRNCE

SUM-OF-SQUf?RES

8.389

0.438

0.209

3.410

4.807

0.434

7.266

DF tlERN-SQUflRE F-R(3T 10

1 8.389 3.303

1 0.438 0.172

1 0.209 0.082

1 3.410 1.343

1 4.807 1.893

1 0.434 0.171

1 7.266 2.861

18.892 1 18.892 7.439

P

0.070

0.678

0.774

0.248

0.170

0.680

0.092

0.007
Appendix 3

3.844

16.841

16.045

3.511

18.685

12.228

74.523

14.635

6.766

11.790

37.145

594.237

I CETRRN

Wf%JETRflN

H17B I TRT

flRER

YEfiR*

FIRER

HflB 1 Tf3T*

DflVTRRN

HRBITRT*

TIME

HRBITflT*

WSPD

HRBITfiT*

WDIR

HflBITfiT*

WRUETRRN

HRBITRT*

I CETRRN

11413.844

16.841

4.011

3.511

1.514

6.632

1.580

1.383

0.220

0.011

0.180

0.241

1 18.685 7.358 0.007 4 3.057 1.204 0.310 4 18.631 7.336 0.000 4 3.659 1.441 0.221 4 1.692 0.666 0.616 4 2.947 1.161 0.329 4 9.286 3.657 0.007

ERROR 234 2.539
Appendix 4 85

Appendix 4. Plots of multiple regression residuals VS. the various

independent variables used in the complete season (5 June to 23 September

1977-1984 and 1989) and molt period (15 July to 21 August 1977-1984 and 1989)

multiple regression analyses.
Residuals-Total Season

I a)+ooaflI

i

N

o- A

0“ I t

&t

oaii!kP (#O

o UmDo 00 

0

0

&30 00 0030 

0

8 I o ma)ooouf)o

@cmo dam) an o

C@ o-

%

@ QQ,)

Ooocmo 00%0 0 0

‘b 00 

g o moor)o

o

0 (mUD0

Residuals-Total Season I o altmuuDqmDoaNlmDal I)o

I

to I

mo I o

IA
Residuals-Total Season

cmo0

i

00I (?0

}

–iIt

:0

IGo0

c—r.

3$0

(3o0

0300No00

h) No0go

0;

-.

!2SN—

y)

G’

nl

—.

Residuals-Total Season

o 1
i t

4-

3-

2.

c

0

; 1-

fn

z o- -

z

k-l.

76

z-

(JJ 2“

.-

m -3.

-4-

0

0

0

0

0

-5- Qo

-6

0 2 4 6 8

westeast

10 12 14

0 Residuals-Total ,.,

G

L

3

2

1

0

~ -1

(u

:-2

$-3

-4

-5

-6

—

—

o 0

-

0

0 0

0

0 Residuals-Total .,,

-5 0 5 10 15 20 25 30 35 40 45 50

wst)d
Residuals-Total Season

o 0 0

%

amm

o mmommo

o ?!00 0

cm o Omxmocm

o

0 00° 0°0Pco Ommo

I o

Oboo

o

ax) I o

o

Residuals-Total Season

o-

‘~

(00 O-o CDomoam o

o[-a o- -

0 0 00°

0 CD %m

+

8° 0 Oooafo 0

0 &*:$ JfpgBqofbo

0
5—

4.

3.

2-

1-

0. —

~ -1-

m

~ -2.

(n

$-3.

-4-

-5-

-6~

00 0

0 go

O Residuals-Total .,,

-.5 0 .5 1 1.5 2 2.5 3 3.5 4

wavetran
Residuals-Molt Period

~(i J!. & o N A 0

OA

i.ji- 0 0 0

(n

oul

UI

{

Ocn ICxm Oazfmm }

!3Cn I

‘T%ad30 0

8 0

<0 1

10 00 00 oil)~o t

o

Residuals-Molt Period
-6 ! o 0 I

1500 2000 2500 3000 3500 4000 4500 5000 5500 6000

daytran

o Residuals-Molt P,.,

- 4

I

o 0 “ o

0

6

4

2

0

.,=

z -2 (x

-4

-6

0

0

u

o

0 $ 08CP

o

0

0

o

0 0

0 Residuals-Molt P.,

600 800 1000 1200 1400 1600 1800 2000 2200 2400

time
Residuals-Molt Period

A A & o

Od

M .A m

alxmc)om @Om

u-l.

& 0 0

+

%39° 00

0

000 ’ ammoooI

o G“ 0 0

0

0

s

N

8 0-

Q 0 0

1

m

cd

0 0

I

-0

“ o mama)

z - 0 al m 000

G- 0 0 000 CD oamocmo o “

c

o
I

ino

inN

inA

Residuals-Molt Period

CA

k r!)

o A) ‘A

m I o }

00 co

Oclo Ocm CQ 0 0

0

0

0.O

O O 0

=

o0

0

0

0

1’0I o

Residuals-Molt Period Io 0 0 o\ (mocBDOo I I

go

o

%0

0

Go 00

t

0 I

00 0

Ocm I

co I 0

0

-w
Appendix 4 95

6

4

2

0

-2

- 4

- 6

0

v

o 0

00 0$ 0

1

0 8

$ 0 0

0

-.5 0 .5 1 1.5 2 2.5 3 3.5

wavetran

O Residuals-Molt P..,
6 Appendix 5

Appendix 5. Disturbance codes assigned to transects in the Industrial and Control study areas in 1990 and 1991.

Code 1 cases (no known disturbance) are not listed.

Potential

Disturbance Date Study Transect Activity

Level ——&ea* Number Recorded

3 20Jul 1990

3 20 Jul 1990

3 23Jul 1990

3 23 Jul 1990

3 02 Aug 1990

3 04 Aug 1990

3 04 Aug 1990

3 09 Aug 1990

3 16 Aug 1990

3 16 Aug 1990

2 20 Jul 1990

2 20 Jul 1990

2 23JuI 1990

2 03 Aug 1990

2 03 Aug 1990

2 04 Aug 1990

2 09 Aug 1990

2 16 Aug 1990

2 20 Aug 1990

2 20 Aug 1990

2 20 Aug 1990

2 20 Aug 1990

2 04 Aug 1991

2 21 Aug 1991

2 21 Aug 1991

I

c

I

c

I

I

I

c

I

I

I

c

I

I

I

c

c

I

I

I

I

r

c

I

I

33

183

25

192

32

2s

24

133

25

23

32

193

24

33

301

193

182

24

401

25

23

22

192

22

30

Five boats and $10 workers on or adjacent to transect

Two seismic sh@s and three support vessels on transect

Five boats and 8-10 workers on or ad~cent to transect

Two seismic ships and three support vessels on transect

Six boats on transect, several aircraft overi%ghts

Six boats and 10-12 workers on or adjacent to transect

Six boats on transect, several aircraft overflights

Two seismic ships and three support vessels on transect

Six boats and 10-12 workers on or adjacent to transect

Six boats and 10-12 workers on or adpcent to transect

Two boats on transect

One boat on transect

Two boats on transect

One boat on transect

Two boats on transect

One boat on transect

One seismic ship on transect

Two boats on transect

Two boats on transect

One boat at net on transect

Two ships on transect

One ship on transect

One seismic ship on transect

Tug with two barges on transect

One boat at net on transect

“ I = Industrial area, C = Control area
Appendix 6

Appendix 6. Relationships between transformed oldsquaw density,

Ln(Oldsquaw Density+ l), on transects in the Industrial and Control study

areas in 1990 and 1991 vs. time of day, transformed wave height, disturbance,

transformed day of season, transect location (W-E), transformed wind

direction, and wind speed (Appends. 6A-6G).

Except for ‘time of day’ in 1991 (Append. 6A), all relationships between

oldsquaw density in Industrial and Control study areas and factors such as

wave height, day of season, wind speed and wind direction were consistent

with results of earlier studies (summarized earlier in this report and in

Johnson 1990). The relationship between oldsquaw density and time of day in

1990 was also similar to past results (positive correlation, Johnson 1990), but

oldsquaw density vs. time of day in 1991 showed an unusual negative

correlation in the Industrial area, suggesting either a spurious correlation or

unusual behavior by the birds over the relatively short time-period during

which surveys were conducted in 1991. Past results have shown positive

correlations between time of day and oldsquaw density, especially when

surveys were conducted without regard for time of day, i.e., over a relatively

broad range of times throughout the day. In this study we recommended that

all oldsquaw surveys be conducted as expeditiously as possible, and as late in

the day as feasible, in order to obtain peak counts of birds and to reduce

possible temporal influences on survey results. All surveys in this study

were conducted well after mid-day, and most surveys, especially in the

Industrial study area in 1991, were conducted two to seven hours after midday (150-400 minutes after 12:00 ADT)(Append. 6A). Given these

circumstances, the results of analyses of oldsquaw density vs. time of day

from surveys in the Industrial area in 1991 are not readily explainable.

97
Appendix 6

Appendix 6A. Relationship between oldsquaw density and time o~day in the

Industrial and Control study areas in 1990 and 1991.

.-

8

6S

4

2

y = .005x+ 1.718

12= .042

Industrial Area

1990 n=84

0° 0

m

0 uf%-’

*

/9--””-” 0 0 Q)o

o 0 @ (jaPOo

o 100 200 300 400 500 600

Time of Day

(Minutes After 1200 AJY13

10” b

y= 4.W-E4X + 4.315 Control Area

?= 4.441E-4 1990

n=lkl

~ &

~

W-1 ~ %%

g

D6

8 ~0~ #’8

@@a

3m

-~ * --------- --------+a-~--’

%4 -------=% -&...

4 :-:$& --”----

5

o:~o

0 0

Z2. IA 0

+c9

0

t

0 100 200 300 400 500 600

Time of Day

(Minutes After 1200 ADT)
Appendix 6 99

10+

Industrial Area

1

1991

8

i

6

4

2-

Y = -.027x+ 12,181

?= .157

n=9 6

o CP --

D w“. m

\

““---

‘e “. -0 @ ‘..

I

I

100 150 200 250 300 350 400 450 500

Time of Day

(Minutes After 1200 ADT)

y = .Ollx + .645

#= .063 Control Area

1991 n=96

N ‘-

0“ 0 Oa

100 150 200 %0 300 350 400 450 500

Time of Day

(Minutes After 1200 ADT)
00 Appendix 6

Appendix 6B. Relationship between okisquaw density and wave height in

the Industrial and Control study areas in 1990 and 1991. .

y = -1.M2X + 5.029

3. .lOs

Industrial Area

1990 n.~

o

%.. o 0

-.* o 0 -..

-..

● .

00 Ooomo

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

--------

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

0 0

0

0

0 00 0

o .5 ~. 1.5 2 2.5 3

Ln(Wave Height+l)

10

8

6

4

2

y.-.Wx4.917l7 Control Area

?=.0% 1990

nxw

8 0

0 0

0

0

0

II o

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

-------- 0

8

00 

-------------------- ---------- --------- ------ 8 00:

0

-------------------- . . . . . . . ---- u

*-------- 00

0

------------ 0

0

0 &-*- . . . ..--y

o 0

0

00 Oo

1 0

0

0

0 .5 1 1.5 2 2.5 3 3.5

Ln(Wave Height+l)
Appendix 6 101

I I

y= -.372x+ 3.431

?= .032 Industrial Area

n=9 6 1991

8

6

0

e

o

4 -------- e 8°

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

U w

--------- ------ .---.--- CI -Q ---------- . ------ % e

2 0

0 0

0 : 0

0 .5 1 1.5 2 2.5 3

Ln(Wave Height+l)

y - -.457x+ 3.91 Control Area

$= .091 1991 n=gG

o

8

“--------- -- 8 8

. . . . . . . ..- --------- -.. . . . . . ------

ii :

n 0 0

0 .5 1 1.5 2 2.5 3

Ln(Wave Height+l)
Appendix 6C. Relationship between oldsquaw density and disturbance

in the Industrial and Control study areas in 1990 and 1991.

y=-.s53x+4.os6

3=.052 Industrial Area

ns~ 1990

~

. . . . . . . . . . . . . . . . . . . . . . . . . . . .

0 0

--------

-.. 0

. . .

-----

1 2 3

.

Disturbance Level

y..O32x+4A47

? =4.71SE-5 Control Area

n=a 1990

~

o

-----

-----”

-----

----- . ..--”

------.----”

-. .-. . 0

.--- . ...-----”

II

----------------- 8

-----------0’------------

-------- 0

0 0 ---------

0

e

Q

1 2 3

Disturbance Level
Appendix 6 103

10

8

6

4

2

o“,

n=9 6 Industrial Area

1991

i

o 1 2 3

Disturbance Level

lot

Control Area

n=9 6

1991

*

6

4

2 1

e

o“ ‘

o

0 1 2 3

Disturbance Level
104 Appendix 6

Appendix 6D. Relationship between oldsquazu density and day of season in

the Industrial and Control study areas in 1990 and 1991. Day 1 is 1 June; the

absassa is scaled to transformed DAY values (X2), e.g., 15 July is day 452 = 2025.

10+

y = 7.516E-5x + Z.716

3= .003 Industrial Area

n=S4 1990

a

o

0 0

6. 0

4

2

00 00

8

8

0

0

00 00

0 8 e

00 0 ----”-

e------------------*~------;---------- :

u o

--------- --------- . . . . . . ..- ------- 0

..- m5----- n

0 0 8 0

7 00 0 So o 0

0’ o .“

a

1000 2000 3000 4000 5000 6000 7000 8000

Day of Season

y= -1.6ZIE-4X + 5.117 Control hea

9= .014 1990

n=S 4

80 :

0

88 i

o

8

;

o

gp.

-....8* 0

0

- --------

--------- . . . . . . . . . ----

Q.8..o------------~ +.....o

00 !

------- 0

s

-------- 0

0 0

0 0

0

0 0 0

0

0 . 0

1000 2000 3000 4000 5000 6000 7000 8(

Day of Season
Appendix 6 105

1

10

8

6

4

2

0

y = -5.760E-5X + 3.173

4=.003 Industrial Area

n = % 1991

0

el” efi) 0

0: ii iiiio8

o ~-8- “-------- . . . . . . . . . . . . . 0 00 ~

a-----c.J-*----”--

8“-o---------------~-------8 -----& -..._& 0

m

0

0

0 8 8: ~

o

8

0 2000 3000 4000 5000 6000 7000 8000

Day of Season

Y = -7.52SE-5X + 3.624

?..IJOS Control Area

n=9 6 1991

0 0 0 R! [

!3 $ 0

0-. . . -------

-- . . . . ------ :---- o-~----”--; $..-. -... ---.. ----... --... *..-. ?-.q.-

1# 0 8 -----g

@ o

e

o 0

0

0 .

1000 200 0 3000 4000 5000 6000 7000 8000

Day of Season
06 Appendix 6

Appendix 6E. Relationship between oldsquaw density and fransecf kxafion

in the Industrial and Control study areas in 1990 and 1991.

10’ L

Y - -.471x+ 4.187

?= ,067

Industrial Area

n=S4 1990

38

&’

o

; o

n6 o 0

s

B

9 El

2

8

----

%’4 o

~

o e-------- .-. .. Q--- J

0----------

2 0

0 0

0

0 8

0 0 e 0

0“; Q “ “ u

10

8

6

4

2

0

1 2 3 4

Transect Location (W-E)

y= Si07x-.s37

F..OS3

Control Area

n=S 4

1990

0

0

@

o

fl

o 0

Iii 8 ! ‘--1 ---- ----------

~

0 ------- .-.. -----0 0

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

3

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

8

-------- ---- ------ iY”---- 0 !0

0 0

0

8 0

9 10

0

11

~

12

Transect Location (W-E)
Appendix 6 107

Y = -A09x + 3.944

?=.0s7

Industrial Area

n=% 1991

B ii 8 8 E B

-.. .

-------- !Il --------- .- ---------

l t

-

. . ..-. --------- . . . . . . . . .0

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

-. 0

0

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

----- 0 0

i 0 8 !

0

a -$

1 2 3 4

Transect Location (W-E)

y= 377x -.662 Control Area

?= .101 1991

n=9 6

8.

6-

8

B

t3

8

0

I

0

4 8

. ----------

-----

.

0 -. . . ...-----”-”--

. . . . . . . . . ------- . . . . . . . ---------

0

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

--------- -------- 0

2

i

0 0

0

0 :

9 10 11 12

Transect Location (W-E)
108 Appendix 6

Appendix 6F. Relationship between oldsquazu density and wind speed in the

Industrial and Control study areas in 1990 and 1991.

10-m P

Y= -.047x +3.614 Industrial Area $= .016 1990 n=S4

*

o

0 0

6. 0 0

4“

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

. . . . . . . . . . . . . . . . . ...-.0 0 u --------

-------

2

0 -----

8 0 : 0 0 ‘-----~

Oe 9 00 0

0

0

“ “

. .

0 5 10 15 20 25 30 35

Wind Speed (krnlhr)

10-1 “ 4.

Y = -.035X+ 4.79

3= .o17

Control Area

n=S4 1990

8-

0 Og 0

00 0

0

6 8

‘~ 8 0

---~ 8 $

e

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

--------- . . . . . . . . ------e---------- ------ 0

4

. . . . . . ---------

0

0----~ -. Q.._” u

0

:0 ----* -.* 0

-------

0

--------0 0

2 0

00 0

0

0“ e

* . n 0 0

.

0 5 10 15 20 25 30 35

Wind Speed (km/h)
Appendix 6 109

y= -.o19x + 2.947

3= .o13 Industrial Area

~.~ 1991

8.

6

0

8:8 O@

~fj: @ :0

4

--e

0°

e~

o

. . . . . . . . . .-~

w %--”-a

-0 Q. e

- --------- . ---

2“

------

00 e

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

0 0

0

0

0 II

0

!? -0 Q O.a e

10

8

6

0 5 10 15 20 25 30 35

Wind Speed (lcndhr)

= -.059X+ 42s Control Area

:= .115 1991

n=9 6

4“

--------- --------- --,

~o

2

0

e

o“ o Q $

“

*

o 5 10 15 20 25 30 35

Wind Speed (lcndhr)
110 Appendix 6

Appendix 6G. Relationship between oldsquazu density and wind direction in

the Industrial and Control study areas in 1990 and 1991.

10”

y= 1.212x+ 2.S67 Industrial Area

, $= .106 1990

0

0

41 !,. . . ...-..-*I

-p -- , ----

2

-“7---- o

.. G?----- 0

00 e 0 e

Q. 0

0“ I

. A

-1 -.75 -.5 -.25 0 .25 .5 .75 1

Wind Direction (COS degrees)

10

y 5 ax + 4.465,

I Control Area (

I go o

i $

II

8 “

J -----------

. . . . . . . . ------------- -. ---------- ---------- - 0 II

o

4 A--------- --------- --------- --- *---- . . . . . 8

!

---0------

0 9

0

0 0

0 0

2 0

0 e 0

0

0“ 0 w “ i

. T

-1 -.75 -.5 -.25 0 .25 .5 .75 1

Wind Direction (COS degrees)
Appendix 6 111

10+

Y = -.044( i- 2.92

?= 3.655E-4 Industrial Area

n=9 6 1991

8

6I

$: 0

II :

4“ 8 #

o

---- e -------- . . . . . . . . . . . . . . . . . . --- -------- - Q @

-.. . u

-------- . . . . . . . . . . . . . . . . . . . . . 8

2

0 0

0 %

I ;

o

o“ , .

-1 -.75 -.5 -.25 0 .25 .5 .75 1

Wind Direction (COS degrees)

8

6

4

y= -363x+ 3.267

~= .029 n=96

.-.I-------

--------- --a

I

2

go

o

-1 -.75 -.5 -.25

Control Area

1991

8

0

. . . . . . ..- . . . . . . . . . . . . . . . . . . . . .

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

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

-----

8

13

.is .5 .75 i 1.25

Wind Direction (COS degrees)
112 Appendix 7

Appendix 7. Computed ANCOVA cell means (ln[oldsquaw density + 1],

adjusted for wave height) for individual transects in different habitats

and study areas sampled in 1990 and 1991.

%mulimz Au Stratum Year of Studv

1990 (n=7) 19h (n=8)

Area 1 (Industrial)

Habitat 1 (Barrier Island)

Transect 1 5.350

Transect 2 5.232

Transect 3 5.007

Transect 4 3.882

Habitat 2 (Mid-lagoon)

Transect 1 4.550

Transect 2 3.944

Transect 3 3.712

Transect 4 1.908

Habitat 3 (Mainland)

Transect 1 1.227

Transect 2 0.495

Transect 3 0.607

Transect 4 0.686

Area 2 (Control)

Habitat 1 (Barrier Island)

Transect 1 6.046

Transect 2 5.602

Transect 3 6.161

Transect 4 6.527

Habitat 2 (Mid-lagoon)

Transect 1 0.867

Transect 2 2.672

Transect 3 3.153

Transect 4 3.267

Habitat 3 (Mainland)

Transect 1 3.761

Transect 2 5.077

Transect 3 5.897

4.304

4.397

4.335

3.632

4.096

3.134

3.590

1.806

2.127

1.067

1.451

0.629

4.468

3.380

3.960

3.937

0.165

3.713

3.131

3.215

2.555

3.290

3.853

Transect 4 5.187 3.534
Appendix 9

Appendix 8. Computed mean densities of oldsquaws (adjusted for wave

height) in Control and Industrial study areas in 1990 and 1991

The figure on this page shows the different relationship between the

adjusted mean density of oldsquaws in the two study areas in 1990 and 1991.

The 95% error bars for these means (not shown) are well beyond the scale of

the ordinate, and therefore show no statistically significant difference between

the two sets of means. Nevertheless, the trends in the means for the two

areas are subjectively suspicious. Speculation about possible causes of

observed differences in oldsquaw density in the two study areas, with only

two years of sampling, is a rather tenuous exercise. Nevertheless, the trends

suggest that more years of sampling over a wider area would be useful in

helping to explain some observed differences in oldsquaw distribution and

abundance.

Year-Area Interaction

5

4

3

2

v 199 1

I I A 1990

Control Industrial

113

Area
114 Appendix 9

Appendix 9. Numbers and percentages of oldsquaws counted during aerial surveys in

nearshore waters of the central Alaska Beaufort Sea, 1977-1984 and 1989-1991.

Category

Numbers

1

2

3

4

. . m.trvev rear

1977 1978 1979 1980 1981 ‘ 1982 1983 1984 1989 1990 1991 AU Years

20695 111594 28598 22777 30597 31927 - 21998 102968 163915 31316 566385

58310 141801 36157 27826 48711 46964 6144 28399 110975 220766 61441 787494

94461 215199 49456 37549 65768 66794 - 33987 138729 277327120397 1099667

104318 231307 54049 38364 71104 69775 - 34972 149408 312073138408 1203778

Percentages

5

6

7

8

9

10 35.49 78.70 79.09 81.86 62.81 67.98 - 77.46 92.78 74.25 50.97 71.92

90.55 93.04 91.50 97.88 92.50 9.5.73 - 97.18 92.85 88.87 86.99 91.35

55.90 61.30 66.90 72.53 68.51 67.31 - 81.20 74.28 70.74 44.39 65.42

21.91 51.86 57.83 60.66 46.52 47.80 - 64.72 74.22 59.11 26.01 51.51

19.84 48.24 52.91 59.37 43.03 45.76 - 62.90 68.92 52.52 22.63 47.05

61.73 65.89 73.11 74.11 74.06 70.31 - 83.56 79.99 79.60 51.03 71.61

1 = No. of oldsquaws on-transect on barrier island transects only

2 = No. of oldsquaws on-transect on all transects

3 = No. of oldsquaws on+off transect on all transects

4 = No. of all binds of all species on+off transect on all transects

5 = Cat. 3/Cat. 4

6 = Cat. 2/Cat. 4

7= Cat. l/Cat. 3

8= Cat. l/Cat. 4

9 = cat. 2/cat. 3

10= Cat.1/Cat. 2