OCS Study 

MMS 2003-037 

Monitoring Beaufort Sea Waterfowl and Marine Birds 

Prepared for 

U.S. Department of Interior 

Minerals Management Service 

Alaska OCS Region 

949 East 36th Street 

Anchorage, Alaska 99508 
2

OCS Study 

MMS 2003-037 

Monitoring Beaufort Sea Waterfowl and Marine Birds 

By 

Paul L. Flint1, John A. Reed1, J. Christian Franson2, Tuula E. Hollmén3, 

James B. Grand4, Mark D. Howell4, Richard B. Lanctot5, 

Deborah L. Lacroix1, and Christian P. Dau5 

1

U.S. Geological Survey, Alaska Science Center, Biological Science Office, 

 1011 E. Tudor Road, Anchorage, Alaska 99503 

2

U.S. Geological Survey, National Wildlife Health Center, 6006 Schroeder Road, 

 Madison, Wisconsin 53711 

3

Alaska Sea Life Center, P.O. Box 1329, Seward, Alaska 99664 

4

Alabama Cooperative Fisheries and Wildlife Research Unit, 

 108 M. White Smith Hall, Auburn University, Auburn, Alabama 36849 

5

U.S. Fish and Wildlife Service, Migratory Bird Management, Waterfowl Branch, 

 1011 E. Tudor Road, Anchorage, AK 99503 

Prepared for 

U.S. Department of Interior 

Minerals Management Service 

 Alaska OCS Region 

949 East 36th Street 

Anchorage, Alaska 99508 

July 2003 

This study was funded, in part, by the U.S. Department of the Interior, Minerals Management Service (MMS), through 

Intra-agency Agreement No. 16950 with the U.S. Department of Interior, U.S. Geological Survey, Biological 

Resources Division, as part of the MMS Alaska Environmental Studies Program. 

The opinions, findings, conclusions or recommendations expressed in this report or product are those of the authors 

and do not necessarily reflect the views of the Minerals Management Service, nor does the mention of trade names 

or commercial products constitute endorsement or recommendation for use by the Federal Government. 
3

PROJECT ORGANIZATION PAGE 

Dirk V. Derksen, Project Manager, Chief, Wetlands and Terrestrial Ecology Branch, 

USGS, Alaska Science Center, 1011 East Tudor Road, Anchorage, AK 99503 

Paul L. Flint, Principal Investigator, Wildlife Biologist, USGS, Alaska Science Center, 

1011 East Tudor Road, Anchorage, AK 99503 

John Reed, Crew Leader, Wildlife Biologist, USGS, Alaska Science Center, 1011 East 

Tudor Road, Anchorage, AK 99503 

J. Chris Franson, Co-Principal Investigator for Contaminants, National Wildlife Health 

Center, 6006 Schroeder Road, Madison, Wisconsin 53711 

Tuula E. Hollmen, Co-Principal Investigator for Diseases, Alaska Sea Life Center, P.O. 

Box 1329, Seward, Alaska 99664 

James B. Grand, Co-Principal Investigator for Body Composition of Long-tailed Ducks, 

Alabama Cooperative Fisheries and Wildlife Research Unit, 108 M. White Smith Hall, 

Auburn University, Auburn, Alabama 36849 

Mark D. Howell, Graduate Student studying Body Composition of Long-tailed Ducks, 

Alabama Cooperative Fisheries and Wildlife Research Unit, 108 M. White Smith Hall, 

Auburn University, Auburn, Alabama 36849 

Richard B. Lanctot, Co-Principal investigator for 2 years, Alaska Science Center, 

Biological Science Office, 1011 E. Tudor Road, Anchorage, Alaska 99503 

Deborah L. Lacroix, Crew Leader, Wildlife Biologist, Alaska Science Center, Biological 

Science Office, 1011 E. Tudor Road, Anchorage, Alaska 99503 

Christian P. Dau, Pilot/Wildlife Biologist, U.S. Fish and Wildlife Service, Migratory Bird 

Management, Waterfowl Branch, 1011 E. Tudor Road, Anchorage, AK 99503 

REPORT AVAILABILITY 

U.S. Geological Survey 

Alaska Science Center 

1011 East Tudor Road 

Anchorage, Alaska 99503 

Telephone (907)-786-3512 

SUGGESTED CITATION 

Flint, P. L., J. A. Reed, J. C. Franson, T. E. Hollmén, J. B. Grand, M. D. Howell, R. B. 

Lanctot, D. L. Lacroix, and C. P. Dau. 2003. Monitoring Beaufort Sea Waterfowl and 

Marine Birds. U.S. Geological Survey, Alaska Science Center, Anchorage, Alaska OCS 

Study MMS 2003-037 
i

TABLE OF CONTENTS 

TABLE OF CONTENTS ................................................................................................... I 

LIST OF TABLES...........................................................................................................IV 

LIST OF FIGURES.........................................................................................................VI 

ABSTRACT ..................................................................................................................VIII 

ACKNOWLEDGMENTS..................................................................................................X 

INTRODUCTION............................................................................................................. 1

SECTION I. MOLTING ECOLOGY OF LONG-TAILED DUCKS 

CHAPTER 1 

EFFECTS OF DISTURBANCE ON CONDITION OF MOLTING LONG-TAILED DUCKS .................... 7 

Introduction .............................................................................................................. 7

Methods ................................................................................................................... 7

Results ..................................................................................................................... 9

Discussion.............................................................................................................. 15

Management Implications ...................................................................................... 17

CHAPTER 2 

BODY MOLT OF MALE LONG-TAILED DUCKS .................................................................. 18 

Introduction ............................................................................................................ 18

Methods ................................................................................................................. 18

Results ................................................................................................................... 19

Discussion.............................................................................................................. 22

CHAPTER 3 

MASS DYNAMICS IN RELATION TO FLIGHT PARAMETERS FOLLOWING

REMIGE MOLT OF MALE LONG-TAILED DUCKS ............................................................... 23 

Introduction ............................................................................................................ 23

Methods ................................................................................................................. 24

Results ................................................................................................................... 25

Discussion.............................................................................................................. 31

CHAPTER 4 

SITE TENACITY AND MOVEMENTS OF LONG-TAILED DUCKS .............................................. 33 

Introduction ............................................................................................................ 33

Methods ................................................................................................................. 33

Results ................................................................................................................... 36

Discussion.............................................................................................................. 37

CHAPTER 5 

EFFECT OF UNDERWATER SEISMIC SURVEYS ON MOLTING LONG-TAILED DUCKS.............. 39 

Introduction ............................................................................................................ 39

Methods ................................................................................................................. 40
ii

Results ................................................................................................................... 44

Discussion.............................................................................................................. 55

CHAPTER 6 

HABITAT USE AND FORAGING PATTERNS OF LONG-TAILED DUCKS..................................... 58 

Introduction ............................................................................................................ 58

Methods ................................................................................................................. 58

Results ................................................................................................................... 60

Discussion.............................................................................................................. 69

CHAPTER 7 

AN ADENOVIRUS LINKED TO MORTALITY AND DISEASE IN LONG-TAILED DUCKS................. 71 

Introduction ............................................................................................................ 71

Materials And Methods .......................................................................................... 71

Results ................................................................................................................... 74

Discussion.............................................................................................................. 75

CHAPTER 8 

TRACE ELEMENTS IN LONG-TAILED DUCKS..................................................................... 78 

Introduction ............................................................................................................ 78

Methods ................................................................................................................. 79

Results ................................................................................................................... 80

Discussion.............................................................................................................. 81

CHAPTER 9 

SYNTHESIS OF AERIAL AND GROUND BASED STUDIES ON MOLTING LONG-TAILED DUCKS..... 83

SECTION II. BREEDING ECOLOGY OF COMMON EIDERS 

CHAPTER 10 

AERIAL POPULATION SURVEYS OF COMMON EIDERS ALONG THE COASTLINE AND

BARRIER ISLANDS OF THE ARCTIC COASTAL PLAIN OF ALASKA. ........................................ 86 

Introduction ............................................................................................................ 86

Methods ................................................................................................................. 86

Results ................................................................................................................... 87

Discussion.............................................................................................................. 90

CHAPTER 11 

PRODUCTIVITY OF NESTING COMMON EIDERS ................................................................ 91 

Introduction ............................................................................................................ 91

Methods ................................................................................................................. 91

Results ................................................................................................................... 93

Discussion.............................................................................................................. 97

CHAPTER 12 

ISOLATION AND CHARACTERIZATION OF A REOVIRUS FROM NESTING COMMON EIDERS . 100 

Introduction .......................................................................................................... 100

Methods ............................................................................................................... 100

Results ................................................................................................................. 101

Discussion............................................................................................................ 102

CHAPTER 13 
iii

TRACE ELEMENTS AND PERSISTENT ORGANIC POLLUTANTS IN COMMON EIDERS.............. 103 

Introduction .......................................................................................................... 103

Methods ............................................................................................................... 104

Results ................................................................................................................. 105

Discussion............................................................................................................ 109

CHAPTER 14 

SYNTHESIS OF AERIAL AND GROUND BASED STUDIES OF NESTING COMMON EIDERS......... 113 

LITERATURE CITED .................................................................................................. 115 
iv

LIST OF TABLES 

TABLE 1-1. GENERAL LINEAR MODELS DESCRIBING VARIATION IN BODY CONDITION OF

MALE LONG-TAILED DUCKS DURING MOLT AND FEATHER RE-GROWTH IN 1999.. ......................10 

TABLE 1-2. PARAMETER ESTIMATES FROM GENERAL LINEAR MODELS DESCRIBING

VARIATION IN LONG-TAILED DUCK BODY CONDITION DURING MOLT AND SUBSEQUENT

FEATHER RE-GROWTH IN 1999.. ............................................................................................11 

TABLE 1-3. GENERAL LINEAR MODELS DESCRIBING VARIATION IN BODY CONDITION OF

MALE LONG-TAILED DUCKS DURING MOLT AND FEATHER RE-GROWTH IN 2000. .......................13 

TABLE 1-4. PARAMETER ESTIMATES FROM GENERAL LINEAR MODEL ANALYSES DESCRIBING 

VARIATION IN LONG-TAILED DUCK BODY CONDITION DURING MOLT AND SUBSEQUENT

FEATHER RE-GROWTH IN 2000.. ............................................................................................14 

TABLE 2-1. PARAMETER ESTIMATES, STANDARD ERRORS (SE), AND COEFFICIENTS OF

MULTIPLE DETERMINATION (R2) FOR THREE-PARAMETER GAUSSIAN MODELS OF MOLT

INTENSITY FOR MALE LONG-TAILED DUCKS IN BEAUFORT SEA LAGOONS, 1999-2000..............20 

TABLE 3-1. GENERAL LINEAR MODELS DESCRIBING VARIATION OF MUSCLE MASS OF MALE

 LONG-TAILED DUCKS DURING MOLT AND FEATHER RE-GROWTH..............................................25 

TABLE 3-2. PARAMETER ESTIMATES FROM GENERAL LINEAR MODEL ANALYSES DESCRIBING 

VARIATION IN LONG-TAILED DUCK PECTORAL MUSCLE AND LEG MUSCLE MASS DURING

MOLT AND SUBSEQUENT FEATHER REGROWTH.......................................................................26 

TABLE 4-1. SAMPLING RESULTS FOR RADIO-EQUIPPED LONG-TAILED DUCKS MONITORED

BY DATA COLLECTION COMPUTERS (DCC) ON THE CONTROL AND INDUSTRIAL AREAS, 

 2000-2002...........................................................................................................................34 

TABLE 4-2. PROBABILITY (%) OF A BIRD REMAINING WITHIN THE DETECTION ZONE OF A SINGLE DATA 

COLLECTION COMPUTER (DCC) FOR A 21-DAY SAMPLING PERIOD...........................................37

TABLE 4-3. DAILY PROBABILITY (%) OF MOVEMENT BETWEEN THE DETECTION ZONE OF ONE DCC 

AND ANOTHER IN THE INDUSTRIAL AREA 2002 ........................................................................37 

TABLE 5-1. NUMBER OF LONG-TAILED DUCKS CAPTURED AND RADIO-MARKED AT FIVE

CAPTURE AREAS, 2001 .........................................................................................................47 

TABLE 5-2. DETECTION RESULTS FOR RADIO-EQUIPPED LONG-TAILED DUCKS MONITORED

BY THE DATA COLLECTION COMPUTERS LOCATED ON THE BARRIER ISLANDS AND THE 

MAINLAND, IN 2001 ...............................................................................................................48 

TABLE 5-3. REPEATED MEASURES ANALYSIS OF VARIANCE FOR PROPORTION OF LONGTAILED DUCKS DETECTED BY DATA COLLECTION COMPUTERS LOCATED IN SEISMIC

AND CONTROL AREAS ...........................................................................................................51 

TABLE 5-4. REPEATED MEASURES ANALYSIS OF VARIANCE FOR PROPORTION OF LONGTAILED DUCKS FEEDING WHEN DETECTED BY DATA COLLECTION COMPUTERS LOCATED

IN SEISMIC AND CONTROL AREAS ..........................................................................................52 

TABLE 6-1. SAMPLING EFFORT AND RESULTS OF RADIO-EQUIPPED LONG-TAILED DUCKS

MONITORED BY TRIANGULATION IN THE CONTROL AND INDUSTRIAL AREAS, 2000-2001...........61 

TABLE 8-1. FREQUENCY OF DETECTION, MEDIAN, AND MINIMUM-MAXIMUM CONCENTRATIONS

OF 11 TRACE ELEMENTS IN BLOOD OF LONG-TAILED DUCKS IN THE CONTROL AND

 INDUSTRIAL AEAS. ................................................................................................................80 

TABLE 8-2. MEDIAN AND MINIMUM-MAXIMUM CONCENTRATION OF SIX TRACE ELEMENTS IN

BLOOD OF LONG-TAILED DUCKS IN THE CONTROL AND INDUSTRIAL AREAS..............................81 

TABLE 10-1A. SUMMARY OF COMMON EIDER COUNTS DURING AERIAL SURVEYS OF THE

 ARCTIC COASTAL PLAIN (BARRIER ISLAND SEGMENTS), 1999-2002........................................88 
v

TABLE 10-1B. SUMMARY OF COMMON EIDER COUNTS DURING AERIAL SURVEYS OF THE

 ARCTIC COASTAL PLAIN (SHORELINE SEGMENTS), 1999-2002. ..............................................89 

TABLE 11-1. NUMBER OF NESTS FOUND BY SPECIES AND STUDY AREA IN 2000-2002......................93 

TABLE 11-2. ESTIMATES OF NESTING SUCCESS FOR COMMON EIDERS IN 2000-2002 .....................96 

TABLE 11-3. BANDING EFFORT FOR FEMALE COMMON EIDERS IN 2000-2002. ................................96 

TABLE 13-1. FREQUENCY OF DETECTION, MEDIAN, AND MINIMUM-MAXIMUM

CONCENTRATIONS OF 15 TRACE ELEMENTS IN BLOOD OF COMMON EIDERS IN THE

 CONTROL AND INDUSTRIAL AREAS ......................................................................................106 

TABLE 13-2. MEDIAN AND MINIMUM-MAXIMUM CONCENTRATIONS OF LEAD AND MERCURY

IN BLOOD OF COMMON EIDERS OF THE CONTROL AND INDUSTRIAL AREAS............................106 

TABLE 13-3. FREQUENCY OF DETECTION, MEDIAN, AND MINIMUM-MAXIMUM

CONCENTRATIONS OF 14 TRACE ELEMENTS IN EGGS OF COMMON EIDERS IN THE

 CONTROL AND INDUSTRIAL AREAS.......................................................................................107 

TABLE 13-4. MEDIAN AND MINIMUM-MAXIMUM CONCENTRATIONS OF MANGANESE AND

 NICKEL IN EGGS OF COMMON EIDERS OF THE CONTROL AND INDUSTRIAL AREAS..................107 

TABLE 13-5. FREQUENCY OF DETECTION, MEDIAN, AND MINIMUM-MAXIMUM

CONCENTRATIONSOF 11 CHLORINATED HYDROCARBON PESTICIDES AND TOTAL

POLYCHLORINATED BIPHENYLS (PCBS) IN EGGS OF COMMON EIDERS FROM THE

 CONTROL AND INDUSTRIAL AREAS.......................................................................................108 

TABLE 13-6. FREQUENCY OF DETECTION, MEDIAN, AND MINIMUM-MAXIMUM

CONCENTRATIONS OF NINE POLYCHLORINATED AROMATIC HYDROCARBONS (PAHS) 

IN EGGS OF COMMON EIDERS FROM THE CONTROL AND INDUSTRIAL AREAS .........................108 
vi

LIST OF FIGURES 

Figure 1. Location of Long-tailed Duck and Common Eider study area 

along the Beaufort Sea, Alaska, 1999-2002........................................................6 

Figure 1-1. Dynamics of (A) dry carcass mass, (B) lipid, and (C) protein 

levels during molt compared to pre-molt levels for male Long-tailed 

Ducks collected in 1999.......................................................................................11 

Figure 1-2. Dynamics of dry (A) carcass mass, (B) lipid, and (C) protein 

evels during molt compared to pre-molt levels for male Long-tailed 

Ducks collected from experimentally disturbed and undisturbed 

sites in 2000.........................................................................................................11 

Figure 2-1. Molt intensity in each feather tract through ninth primary growth 

for male Long-tailed Ducks. (A) Head and neck, (B) lesser coverts, 

(C) greater coverts, (D) tail, (E) back and rump, (F) belly, (G) breast, 

and (H) flank and side..........................................................................................21 

Figure 3-1. Dynamics of wing load for molting male Long-tailed Ducks 

1999-2000 (wing loads are based on wet weights)..............................................27 

Figure 3-2. Changes in pectoral and leg muscle mass in relation to molt 

stage for molting male Long-tailed Ducks 1999-2000..........................................28 

Figure 3-3. Wing power load (pectoral muscle mass/wing area) in relation to 

molt stage for molting male Long-tailed Ducks 1999-2000..................................28 

Figure 3-4. Power load (carcass mass/right pectoral muscle mass) in relation 

to molt stage for molting male Long-tailed Ducks 1999-2000..............................29 

Figure 3-5. Leg-power load (carcass mass/leg muscle) in relation to molt 

stage for molting male Long-tailed Ducks 1999-2000..........................................29 

Figure 3-6. Wing loading based on nutrient composition (dry weight values) 

in relation to molt stage for molting male Long-tailed Ducks 1999-2000.............30 

Figure 3-7. Percent of nutrients contributing to wing load in relation to molt 

stage for molting male Long-tailed Ducks 1999-2000..........................................30 

Figure 4-1. Locations of Data Collection Computers (DCC) and Long-tailed 

Duck capture sites in the Control and Industrial Areas, 2000-2001.....................35 

Figure 5-1. Location of the Seismic, Industrial and Control Areas. ..............................40 

Figure 5-2. Number of Long-tailed Ducks counted from aircraft during the 

pre-seismic, during-seismic, and post-seismic periods along the 

lagoon and ocean sides of barrier islands in 2001. ............................................45 

Figure 5-3. Number of Long-tailed Ducks counted (corrected values) on the 

(A) ocean and lagoon, and (B) lagoon only sides of the barrier 

islands where seismic activity occurred and did not occur .................................46 

Figure 5-4. The proportion of radio-equipped Long-tailed Ducks detected 

by DCCs placed in the Seismic Area...................................................................49 

Figure 5-5. The proportion of radio-equipped Long-tailed Ducks detected 

by DCCs located in the Industrial and Control Areas...........................................50 

Figure 5-6. The proportion of radio-equipped Long-tailed Ducks detected 

feeding by DCCs on barrier islands in the Seismic Area.....................................52 
vii

Figure 5-7. The proportion of radio-equipped Long-tailed Ducks detected 

feeding by DCCs in the Industrial and ................................................................53 

Figure 5-8. The number of radio-equipped Long-tailed Ducks detected by 

DCCs on the barrier islands in the Seisimic, Industrial, and Control 

Areas....................................................................................................................54 

Figure 6-1. Locations of triangulation towers Control and Industrial 

Areas, 2000-2001................................................................................................59 

Figure 6-2. Diurnal foraging activity pattern of Long-tailed Ducks determined 

by radio triangulation in 2000-2001......................................................................62 

Figure 6-3. Diurnal habitat use patterns of Long-tailed Ducks determined 

by radio triangulation in 2000-2001......................................................................63 

Figure 6-4. Pattern of foraging activity by Long-tailed Ducks through the 

molt period determined by radio triangulation......................................................64 

Figure 6-5a. Pattern of foraging activity of Long-tailed Ducks determined by 

Data Collection Computers in 2000.....................................................................65 

Figure 6-5b. Pattern of foraging activity of Long-tailed Ducks determined by 

Data Collection Computers in 2001.....................................................................66 

Figure 6-5c. Pattern of foraging activity of Long-tailed Ducks determined by 

Data Collection Computers in 2002.....................................................................67 

Figure 6-6. Pattern of habitat use by Long-tailed Ducks through the molt 

period determined by radio triangulation..............................................................68 

Figure 7-1. Time line indicating when individual birds were inoculated with 

the adenovirus and necropsied............................................................................74 

Figure 10-1. Location of aerial survey segments along the Arctic Coastal 

Plain (A) shoreline and (B) barrier islands...........................................................87 

Figure 10-2. Aerial survey counts of Common Eiders at known breeding 

areas on the Chukchi and Beaufort Seas............................................................88 

Figure 10-3. Correlation of aerial survey counts (indicated breeding pairs) 

and ground based nest counts (all successful and failed eider nests).................90 

Figure 11-1. Distribution of Common Eider nest initiation dates on the Control 

and Industrial Areas in 2000-2002.......................................................................94 

Figure 11-2. Locations of Common Eider nests on the Control and Industrial 

Areas in 2000-2002..............................................................................................95 
viii

ABSTRACT 

We present the results from four seasons of a multifaceted research program designed 

to assess the breeding ecology of Pacific Common Eiders (Somateria mollissima vnigra) and molting ecology of Long-tailed Ducks (Clangula hyemalis) along the Beaufort 

Sea coast of Alaska. An aerial survey component of this study was completed in 2000 

and presented as a separate report (Fischer et al 2002). Our study area was split into 

an Industrial Area adjacent to current oilfield development to the west of Prudhoe Bay 

and an undeveloped Control Area around Flaxman Island. 

Long-tailed ducks congregate in the lagoon system of the Beaufort Sea for a postbreeding molt period from mid-July through mid-September. During this time the 

lagoons host 10-30,000 flightless Long-tailed Ducks. The combination of their large 

numbers, limited mobility, nutritional demands along with a declining population trend 

has led to concern for this species. In 1999 and 2000, we collected ducks through the 

molt period for a study of body condition, molt timing, and flight parameters. The 

dynamics of body composition during the molt period act to minimize the flightless 

period for Long-tailed Ducks. These ducks meet their nutritional demands by foraging 

during the molt period, but there is no indication that they are resource limited. Body 

condition was not affected by experimental boat disturbances or proximity to industrial 

development. During 2000-2002, we studied aspects of movement, site fidelity, habitat 

use, and foraging using radio telemetry. In general, Long-tailed Ducks forage in the 

lagoons by day and roost along the barrier islands at night. Movement patterns of 

Long-tailed Ducks among years and areas are highly variable with some individuals 

showing a great deal of mobility. We have not seen effects of disturbance (including 

underwater seismic gunning) on movement, habitat use, or foraging; rather weather 

(esp., wind) appears to be the primary influence on these behaviors. To examine the 

role of disease and contaminants on Long-tailed Ducks, we have analyzed blood and 

cloacal samples taken from live ducks and tissue samples from carcasses. Blood levels 

of lead were low and there were no major differences in concentrations of trace 

elements between the Industrial and Control Areas. We identified an adenovirus 

outbreak as the cause of poor body condition and mortality of Long-tailed Ducks in the 

Control Area in 2000. Our data suggest that molting Long-tailed Ducks are more 

influenced by natural phenomena such as wind and disease than human disturbance. 

There is concern for the Common Eiders of the Beaufort Sea due to recent dramatic 

population declines. Along the arctic coast of Alaska, the greatest concentration of 

breeding Common Eiders is in the central Beaufort Sea where they nest almost 

exclusively on barrier islands. We used aerial surveys and ground based nest 

monitoring to assess the breeding ecology of Common Eiders in our study areas. Both 

aerial surveys and ground based nest searches show a continued decline in nesting 

effort since 1999. This decline parallels increasingly late sea ice breakup and we 

believe that it is in part due to eiders forgoing nesting because of poor conditions on 

breeding grounds. All of our measures of productivity (nesting effort, clutch size, hatch 
ix

success, and fledging success) are low and substantially below those of Pacific 

Common Eiders nesting on the Yukon-Kuskokwim Delta. Predation by Arctic Foxes 

and Glaucous Gulls is the greatest contributor to nest failure. Of 52 broods followed in 

2000 and 2001, none were known to survive until fledging. A reovirus, similar to one 

responsible for a major die-off in Finland, was isolated from two duckling carcasses 

collected in 2000. Disease and predation may be responsible for poor duckling survival. 

Concentrations of lead and mercury in blood and eggs were lower than on the YukonKuskokwim Delta. Our data do not show an effect of industrial development on 

Common Eiders, with the possible exception of an increased risk of predation for eiders 

breeding near the oilfields. With the breeding success that we have documented since 

2000, this population will not persist on its own. We see three possible scenarios for 

this population: 1) the population may be declining rapidly, 2) the population is 

maintained by recruitment from other populations, or 3) the population is maintained by 

infrequent years of high recruitment. Regardless, there is cause for significant concern 

about the long-term viability of this population. 
x

ACKNOWEDGMENTS 

Funding for the ground based research was provided by the U.S. Geological Survey 

(USGS), Outer Continental Shelf (OCS) Program. Aerial surveys were funded by the 

Minerals Management Service, U.S. Fish and Wildlife Service (USFWS), and the 

USGS. BP Exploration (Alaska) funded the assessment of seismic surveys on Longtailed Ducks. The USGS, OCS Program, and USGS Science Support Program funded 

the disease and contaminants components of this research. We received extensive 

logistical support from numerous companies and individuals. We thank: Bill Streever, 

Wilson Cullor, Allison Erickson, Dave Trudgen, and Conci Rock from BP Exploration; 

Mike Barker, Larry Harms, and Buddy Hand from ExxonMobil; Bill Griffiths, Bob 

Sechhelm, Bod Rodrigues, and Lynn Noel from LGL Alaska Research Associates; Dick 

Lawrence from Air Logicstics; Bob Glover from ERA Helicopters; Becky Reynolds and 

Kermit Karns from Cape Smythe Air; Nick Glover and Walt Henry from Alaska Clean 

Seas; Lynden Transport, Nana Oilfield Services, and Colville Environmental Services. 

Pilots from the USFWS (John Sarvis and Eric Akola) and from the Office of Aircraft 

Services (Garland Dobson and Bart Stone) were particularly helpful with both logistics 

and research. Margaret Petersen, John Pearce, and Greg Spencer of the USGS, 

Alaska Science Center provided a tremendous range of aid from logistical coordination 

and field work, to project and report review. This project would not have been possible 

without the efforts of our numerous field assistants: Kim Annis, John Benson, Alan 

Brelsford, Nathan Chelgren, Suzanne Conlon, Pat Cook, Jud Easterwood, Tamara 

Egans, Laura Galati, Brook Gamble, Rainy Inman, Sam Iverson, Laura Kirk, Dave 

Koons, Elizabeth Labunski, Leonard Machut, David Miller, Scott Pavey, Tim Pierson, 

Cathy Randall, Jon Runge, Jennifer Sheehan, Martin Stafford, Jacob Storey, Steve 

Stroka, Erica Sussman, Carolyn Van Hemert, and Scott Wallace. We also thank 

Heather Wilson and Sarah Sonsthagen of the University of Alaska-Fairbanks for field 

assistance and sharing results from their studies of Pacific Common Eiders. And finally, 

we especially thank Lyman Thorsteinson of the U.S. Geological Survey for his support 

of this project. Lyman encouraged us to explore avenues of research that have yielded 

an improved understanding of Beaufort Sea marine waterfowl. 
1

INTRODUCTION 

The Alaskan Arctic Coastal Plain is home to the largest oil and natural gas discovery in 

North America (Gilders and Cronin 2000). Located primarily between the Colville and 

Sagavanirktok rivers on the North Slope of Alaska, this development spans an area 

approximately 30 x 100 km in size, with facilities occupying some 8,793 ha (Gilders and 

Cronin 2000). Recent expansion of oil and gas development into the near shore waters 

of the Beaufort Sea has raised concerns that wildlife using these waters (and the nearby 

barrier islands) may be at risk to disturbance and oil spills (US Army Corps of Engineers 

1998). Of particular concern are more than one hundred thousand sea ducks and other 

marine birds that use the Beaufort Sea each summer (Johnson and Herter 1989, 

USFWS 1999). Impacts on sea ducks may be especially important given their recent 

declines in Alaska and along the Arctic Coastal Plain (Goudie et al. 1994, Suydam et al. 

2000, USFWS 1999). 

To address the potential threats to wildlife resources, the Outer Continental Shelf Lands 

Act and its amendments include provisions for post-lease monitoring studies to identify 

environmental changes, establish trends in marine bird populations, and design 

experiments to identify causes of any changes (Johnson and Gazey 1992). In 1999, the 

Minerals Management Service, in collaboration with the U.S. Geological Survey, began 

studies to describe the abundance and distribution of Long-tailed Ducks (Clangula 

hyemalis), eiders (Somateria spp.), and other marine birds in lagoons and offshore 

areas located adjacent to and away from industrial development. This study consisted 

of nearshore and offshore aerial surveys, and intensive ground-based studies focused 

on breeding Common Eiders (Somateria mollissima v-nigra) and molting Long-tailed 

Ducks. These latter studies monitor eiders and Long-tailed Ducks in more detail, and 

evaluate how species utilizing the lagoons during different stages of their breeding cycle 

may be affected differentially by industrialization. 

 The specific objectives of this study were: 

1. Monitor Oldsquaw and other species within and among industrial and control

areas in a manner that will allow comparison with earlier surveys using Johnson 

and Gazeys’ (1992) study design. 

a) Perform replicate aerial surveys of five previously established transects 

based on existing protocol (OCS-MMS 92-0060). 

b) Expand the area from original surveys to include near-shore areas along 

Beaufort Sea coastline between the original “industrial” (Jones-Return 

Islands) and “control” (Stockton-Maguire-Flaxman Islands) areas. 
2

c) Define the range of variation for area waterfowl and marine bird 

populations. Correlate this variation with environmental factors and oil 

and gas exploration, development, and production activities. 

2. Expand aerial monitoring approximately 50 km offshore. Surveys will target 

Spectacled, Common and King eiders. The goal is to sample areas potentially 

impacted by oil spills from the Liberty, Northstar, and/or Sandpiper Units. 

3. Locate and determine the species, number of individuals, density, flock size, 

direction of movement, habitat/water depth, and behavior of eiders along transect 

lines. Record the species, location, numbers, and flock size of other seabirds 

and loons encountered during the surveys. 

4. Determine the timing of staging/migrating flock occurrence including the range of 

dates and peak period dates, the occurrence of eider consistent use areas and 

areas where generally higher densities of eiders would be expected. 

5. Develop a monitoring protocol for birds breeding on barrier islands, particularly 

Common Eiders. These data will be compared to historic data summarized by 

Schamel (1977) and Moitoret (1998). 

6. Examine relationships between life-history parameters (e.g., fidelity, annual 

survival, productivity) and ranges of variation in Oldsquaw and Common Eider 

distribution and abundance to enhance interpretation of cross-seasonal effects of 

disturbance. That is, the combination of aerial and ground based work has the 

potential to both document changes in abundance/distribution and describe those 

changes in terms of movements of marked individuals. Parameters will be 

examined in relation to disturbance using the two-tiered approach (i.e., within and 

among lagoons) developed by Johnson and Gazey (1992). 

7. Recommend cost-effective and feasible options for future monitoring programs to 

evaluate numbers and species of birds potentially impacted by oil spills involving 

ice-free and ice periods in both inshore and offshore waters. 
3

To accomplish objectives 1-4 the USGS-BRD collaborated with the Waterfowl Branch of 

the USFWS Migratory Bird Management Division to conduct near-shore aerial surveys 

using existing MMS protocol (OCS-MMS-92-0060). This protocol was designed to 

measure effects of near shore industrialization on marine bird abundance and 

distribution (Johnson and Gazey 1992). Rather than test for industrial effects on all 

species, the protocol identified the Long-tailed Duck as a focal species because of its 

relative abundance and distribution within the area of interest. We used this protocol to 

collect density and distribution data on Long-tailed Ducks in 1999-2000 to compare 

relative densities between ‘industrial’ and ‘control’ areas. These areas were delineated 

in the early 1990’s at a time when human activity was concentrated in the ‘industrial’ 

area (Johnson and Gazey 1992). 

Although human disturbance may have indirect effects on marine birds, an oil spill 

would likely cause direct mortality of some individuals of certain species (Stehn and 

Platte 2000). The probability and relative severity of oil spill impacts on population 

status depends on the temporal and spatial distribution of marine birds in the region. To 

understand marine bird distribution, we expanded aerial surveys throughout the nearshore environment between Oliktok and Brownlow Point. 

The near-shore aerial survey protocol provides a means to monitor trends and 

distribution patterns of bird populations close to shore, but bird use of offshore waters is 

poorly documented. Previous studies demonstrated that Spectacled Eiders (Somateria 

fischeri), a threatened species, use offshore waters extensively (Petersen et al. 1999). 

Surveys in the Canadian Beaufort Sea revealed that eiders used waters as far as 115 

km from shore (Searing et al. 1975). Thus, we designed an Offshore survey to 

delineate concentrations of eiders and other marine birds that use waters within and 

beyond the barrier island lagoons between Cape Halkett and Brownlow Point. In 

contrast to the near-shore survey that was designed to detect small-scale distribution 

patterns within the barrier island lagoons, the offshore survey covered a much larger 

area. Consequently, inferences drawn from the offshore survey are not limited to smallscale localized patterns of distribution. Results of the aerial survey portion of this study, 

relative to objectives 1-4 and 7, are summarized and reported by Fischer et al. (2002). 

Because molting Long-tailed Ducks and breeding Common Eiders are using the 

Beaufort Sea lagoons during different portions of their respective annual cycles, the 

USGS-BRD developed a multifaceted research program designed to address objective 

6. 

MOLTING ECOLOGY OF LONG-TAILED DUCKS 

Between mid-July and mid-September 10-30,000 Long-tailed Ducks use the lagoons 

along the Beaufort Sea for a post-nuptial molt (Johnson and Richardson 1981, Bartels 

et al. 1983, Wilbor 1999). At this time, Long-tailed Ducks are the most abundant and 

widespread waterbird in the nearshore areas of the Beaufort Sea (Schroger 1947, 

Gollop and Richardson 1974, Johnson and Richardson 1981, Johnson and Herter 
4

1989). During this molt period, birds are flightless for 3-4 weeks, making them 

vulnerable to human disturbance, weather events, and predation (Johnson and 

Richardson 1982). Within the lagoons invertebrates (esp., mysids and amphipods) are 

an abundant food source for sea ducks during an energetically costly time (Johnson 

1984). 

Industrial development associated with oil and gas exploration and extraction has 

resulted in increased industrial activity in the Beaufort Sea lagoons. These activities 

include increased sea and air traffic, and changes in habitat due to development of 

temporary and permanent structures. Offshore industrial development in the Northstar 

Unit of the Beaufort Sea began during the winter of 1999. In addition, underwater 

seismic activity, conducted in 2001, may continue in other areas. The additional air and 

boat traffic may result in increased levels of stress during a time of energetic demand. 

Additionally, there is at least a 15% chance of > 1,000 barrel oil spill during the expected 

15-year lifespan of the industrial developments (U.S. Army Corps of Engineers 1998). 

Long-tailed Ducks may be especially vulnerable to these disturbances during the 

flightless period. 

Knowledge of the distribution, movements, and residence times of ducks within the 

lagoons, as well as their behavior, is important to predict potential effects of pollution 

and disturbance from oil exploration on Long-tailed Ducks using the coastal lagoon 

system. Effects of disturbance could be as subtle as changes in behavior, or as major 

as mortality and abandonment of molting sites. Understanding the patterns body 

composition dynamics is essential to interpreting the potential effects of disturbance. 

Birds in poor condition are likely to be more susceptible to alteration of behavior 

patterns or displacement from preferred feeding locations. Disease and exposure to 

contaminants have been shown to have negative effects on populations of sea-ducks. 

Therefore, it is essential to assess the prevalence of these potentially confounding 

factors. 

The specific objectives of our molting Long-tailed Duck studies include: 

1. Examine within season site-fidelity, local movement and feeding patterns of 

Long-tailed Ducks in relation to time of day, weather, disturbance and industrial 

activity. 

2. Compare the dynamics of muscle mass and body composition of molting Longtailed Ducks in relation to the use of exogenous resources and examine the 

effects of disturbance on body composition dynamics. 

3. Determine the prevalence of viruses and concentrations of selected 

contaminants in molting Long-tailed Ducks and evaluate these parameters in 

relation to proximity of oil development and potential for influencing population 

dynamics. 

BREEDING ECOLOGY OF COMMON EIDERS 
5

The Pacific race of Common Eiders (Somateria mollissima v-nigra) is of particular 

interest to resource managers because of their recent population declines (Hodges et 

al. 1996, Suydam et al. 2000). Indices to the number of Common Eiders migrating past 

Barrow, Alaska, have dropped dramatically in recent years (Suydam et al. 2000). 

Further, recent satellite telemetry data suggest that the North Slope breeding population 

is largely isolated from other populations of this subspecies (Petersen and Flint 2002). 

Reasons for this apparent decline are unknown, but may be related to poor productivity, 

low adult survival, or contaminants and disease. 

Along the North Slope of Alaska, Common Eiders nest and raise their broods almost 

exclusively on barrier islands (Johnson 2000). Loss of island habitat, either from natural 

events or industrial development, would likely have negative effects on local segments 

of the Beaufort Sea population. Two of the primary nesting islands, Egg and Stump, 

are bisected by an underwater oil pipeline or lie adjacent to the Northstar oil production 

facility (Schamel 1977, Johnson and Richardson 1980, Johnson 1984, Noel et al 2002). 

Accordingly, an understanding of the distribution of Common Eider nests across islands 

is essential to evaluate the potential impacts of an oil spill and to lessen the effects of 

future offshore oil development on the local nesting population. 

The specific objectives of the Common Eider studies include: 

1. Document nesting activity by birds on barrier islands and compare these data to 

historic data summarized by Noel and Johnson (2000). 

2. Examine life-history parameters (e.g., annual survival, productivity, and site 

fidelity) of Common Eiders in relation to proximity to oil development and in 

comparison with eiders breeding in other locations. 

3. Develop a model of population dynamics, incorporating levels of productivity and 

annual survival. 

4. Determine the prevalence of viruses and concentrations of selected 

contaminants in blood and eggs of nesting Common Eiders, and evaluate these 

parameters in relation to proximity of oil development and potential for influencing 

population dynamics. 
6

STUDY AREA 

This study was conducted at Simpson Lagoon (formed by the barrier island complex 

between Spy and Stump Islands) and an unnamed lagoon to the east (formed by the 

Maguire, and Flaxman Island complex) (Figure 1). Simpson lagoon lies adjacent to 

oilfield production facilities (including Milne Point and Northstar) and is thus considered 

the Industrial Area. The lagoon to the east is considered to be a comparable Control 

Area. The study area includes the barrier islands and the adjacent waters of the 

lagoons and Beaufort Sea. This follows the design proposed by Johnson and Gazey 

(1992) for monitoring waterfowl and marine birds in the Beaufort Sea. Our Control Area 

is not a true control in the sense that some limited development has occurred in the 

area. However, with active coastal and off-shore facilities at F-Pad, Milne Point, West 

Dock, and Northstar the magnitude of development and activity is much greater in the 

Industrial Area. 

Figure 1. Location of Long-tailed Duck and Common Eider study area along the Beaufort Sea, Alaska, 

1999-2002. 
7

Chapter 1 

Effects of Disturbance on Condition of Molting Long-tailed Ducks 

INTRODUCTION 

We hypothesized that disturbance similar to that caused by industrial activity would 

have negative impacts on the body condition of molting Long-tailed Ducks. Feather 

replacement requires large amounts of protein (Hohman et al. 1992, Murphy and King 

1992). These nutritional requirements can be satisfied by increasing foraging time, 

reducing other nutrient costly processes (e.g., shortened periods of activity, roosting 

close to feeding sites, limiting activities to foraging and resting), or catabolizing stored 

nutrients (Ankney 1979). Most waterfowl have the ability to store nutrients that can be 

used to meet the demands of molt if environmental factors prevent them from obtaining 

necessary nutrients from exogenous sources (Ankney 1979, Hohman et al. 1992). 

Thompson and Drobney (1996) found that the nutritional reserves of molting 

Canvasbacks (Aythya valisineria), a species that dives readily, remained stable during 

feather growth after the wing molt. Thus, they concluded the nutritional requirements of 

feather growth were not derived from endogenous stores of nutrients. However, Goudie 

and Ankney (1986) examined diets and activity budgets of co-existing sea ducks and 

found that smaller species fed on higher energy prey items and spent more time 

feeding. They suggested that smaller species, such as Long-tailed Ducks, had 

relatively higher metabolic rates and stored less energy than larger species. Therefore, 

Long-tailed Ducks may have fewer endogenous reserves available to satisfy the 

nutritional demands of molt if foraging time is reduced due to increased disturbance. 

Thus, if disturbance reduces foraging time or increases time spent in energy costly 

behaviors, patterns of nutrient reserve dynamics may be good indicators of the impact 

of disturbance on molting small-bodied waterfowl. Our objectives were to describe the 

dynamics and effects of disturbance on nutrient reserves of Long-tailed Ducks during 

wing molt. 

METHODS 

In 1999 and 2000, we collected Long-tailed Ducks through the molt period in both the 

Industrial and Control Areas. During 1999, we attempted to obtain comparable samples 

of birds from each study area representing all stages of the wing molt from just prior to 

flight feather loss through feather growth and re-attainment of flight. We also collected 

birds prior to wing molt for comparative analysis. In 2000, we further divided each area 

into experimentally disturbed and undisturbed areas to assess the potential effects of 

disturbance on Long-tailed Duck nutritional status. For this study, our experimental 

disturbance consisted of boat traffic through the treatment areas. We chose boat traffic 

because it represented one of the causes of disturbance within the lagoons and has 

been linked to changes in foraging behavior of water birds (Galicia and Baldassarre 

1997). Two transects were established approximately 50 m and 150 m from shore. 

Each transect was driven in an 18 ft skiff with a 40 hp outboard motor once every other 

day after the initial collections were taken at all sites. We planned to collect birds from 
8

each area within each site during three collection periods: initiation of molt, feather 

regrowth half-completed, and as birds began to regain flight. However, adverse 

weather conditions disrupted the experiment and the final collection of birds was not 

completed. Disturbance was applied from 4-11 August after which weather conditions 

interrupted the study. 

All specimens were collected by euthanizing birds captured during banding operations 

or by shooting in accordance with Auburn University animal care and use committee 

guidelines. We recorded culmen width from freshly euthanized specimens. Birds were 

then frozen and transported to Auburn University for laboratory analysis. Laboratory 

analyses were similar to procedures described by Dobush et al. (1985) and Thompson 

and Drobney (1996). We plucked each bird, removed all contents from the digestive 

tract, and weighed the carcass (see Bailey 1985, Thompson and Drobney 1996, and 

Brown and Saunders 1998). We also measured the length of the keel as an index of 

skeletal size. 

Specimens were ground to a homogenized mixture with a Hobart meat grinder. A 

homogenized subsample weighing approximately 100 g was removed and oven-dried 

for 72 hrs at 90°C. The dried sample was further ground up in a food processor; after 

which, an ~8 g subsample of dried homogenate was used to determine lipid, protein, 

and ash content. Subsamples were placed in cellulose thimbles and placed in a 

Soxhlet apparatus with petroleum ether for 8 hr to remove lipids. The remaining lipidfree material was dried and re-weighed to determine lipid content. The dried lipid free 

subsample was placed in an aluminum dish and combusted in a muffle-furnace at 

550°C for 3 hr and the remainder weighed to determine protein and ash content. 

For our analysis, we used nutrient levels based on dry mass to minimize variation in 

moisture content due to storage conditions. We also used dry carcass mass, rather 

than whole bird mass, to minimize variation due to body molt and undigested carcass 

contents. To describe body composition dynamics during wing molt and subsequent 

feather regrowth in 1999, we used 20 linear models with each of the dependent 

variables: carcass mass, protein, lipid, and ash (on a dry weight basis); and the 

independent variables: keel length (KEELEN), bill width (BILLWID), collection date 

(DATE), length of the ninth primary (NINTH) and squared length of the ninth primary 

(SQNINTH). Keel length and bill width were included in all models to control for 

variation due to size. Length of ninth primary was measured from the tip of the feather 

to its insertion on the proximal side of the feather. We examined potential differences 

among sites by including site (SITE) and an interaction between site and length of ninth 

primary (SITENINTH) as independent variables. We selected the models that best fit 

our data based on ∆AIC < 2.0 (Burnham and Anderson 1998). We included KEELEN 

and BILLWID in all models to control for differences in structural size among individuals. 

Thus, for 1999 analyses we report the difference in R2

 for each model in comparison to 

a model containing only keel length and bill width. We assumed that the factors 

influencing condition each year would be similar. Therefore, to examine the effects of 
9

disturbance on body composition in 2000, we used only models from the analysis of 

1999 data with ∆AIC < 2.0, and added the independent variables treatment (TRT), an 

interaction between treatment and ninth primary (TRTNINTH), and a site by treatment 

interaction (SITTRT). Date was included in all lipid and carcass models, and excluded 

in all protein and ash models based on 1999 analyses. Thus, 20 models were 

considered. 

RESULTS 

Patterns of Nutrient Use 

Between 28 July and 14 August 1999, we collected 97 males, of which 4 had not molted 

(length of ninth primary = 144-156mm) and 93 were in the process of molt and feather 

growth (length of ninth primary = 0-136mm). NINTH appeared in all models with ∆AIC <

2.0 for carcass, lipid, and protein mass (Table 1-1). Thus, molt stage was related to 

mass and nutritional status. As indicated by low R2, there was little variation in ash 

content in relation to molt stage (Table 1-1). 

Long-tailed Duck carcass mass and lipid levels declined during molt (Table 1-2, Figure 

1-1). We also found a strong relationship between carcass mass and DATE after 

controlling for molt stage (Table 1-2). Further, the relative importance of the SQNINTH 

parameter suggested moderate support for a decrease in the rate of decline in mass, 

but weak support for a change in rate of decline in lipid use through feather regrowth 

(Table 1-2, Figure 1-1). Mass and lipid levels also differed between sites (Table 1-2). 

We found little support for the interaction term SITENINTH indicating little or no 

difference in the rate of decline in either carcass mass or lipid levels between sites 

(Table 1-2). 

Long-tailed duck protein levels declined during early stages of wing molt (Table 1-2, 

Figure 1-1). However, we found strong evidence for an increase in protein levels to 

levels comparable with pre-molt birds near the end of feather regrowth (Table 1-2), and 

a difference in protein levels or rate of protein use between sites was only weakly 

supported (Table 1-2). DATE also had minimal effect on protein levels (Table 1-2). 
10

Table 1-1. General linear models describing variation in body condition of male Long-tailed Ducks during molt and fea

in 1999. The models best fit by our data, and those with substantial support (i.e., with ∆AIC < 2.0), are presen

length and bill width were included in all models to control for body size. 

Response variable Model Explanatory variablesab

Number of 

parameters ∆AICc

AICc

weight Total 

DRY CARCASS 1 NINTH SQNINTH DATE 7 0.00 0.25 0.56  2 SITE NINTH SQNINTH DATE 8 0.34 0.21 0.57  3 SITE NINTH SITENINTH SQNINTH DATE 9 0.86 0.16 0.57 LIPID 1 SITE NINTH DATE 7 0.00 0.30 0.60  2 SITE NINTH SITENINTH DATE 8 0.45 0.24 0.61  3 NINTH DATE 6 1.29 0.16 0.59 PROTEIN 1 NINTH SQNINTH 6 0.00 0.45 0.33  2 SITE NINTH SQNINTH 7 1.31 0.23 0.34 ASH 1 SITE NINTH SQNINTH 7 0.00 0.11 0.10  2 --d 4 0.06 0.10 0.03 

 3 NINTH SQNINTH 6 0.20 0.10 0.07  4 SITE 5 0.27 0.09 0.05  5 NINTH 5 0.37 0.09 0.05  6 SITE NINTH 6 0.90 0.07 0.07  7 SITE NINTH SITENINTH SQNINTH 8 1.24 0.06 0.11  8 SQNINTH 5 1.27 0.06 0.04  9 SITE SQNINTH 6 1.80 0.04 0.06 

a NINTH - length of ninth primary, SITE - study site, SQNINTH - squared ninth primary, SITENINTH - interaction term b

and length of ninth primary, DATE - collection date (Julian), 

b Culmen width and keel length were included in all models to control for size variation among individuals. c Improvement in R2 with addition of specified explanatory variables. d Model including only culmen width and keel length. 
11

Figure 1-1. Dynamics of (A) dry carcass mass, (B) 

lipid, and (C) protein levels during molt compared 

to pre-molt levels for male Long-tailed Ducks 

collected in Control (East) and Industrial (West) 

Areas in 1999. Curves are based on results of 

weighted model averaging. 

Figure 1-2. Dynamics of dry (A) carcass mass, 

(B) lipid, and (C) protein levels during molt 

compared to pre-molt levels for male Long-tailed 

Ducks collected from experimentally disturbed 

(treatment) and undisturbed (control) sites in 

2000. Ducks were collected from both the 

Control (East) and Industrial (West) Areas. 

Curves are based on results of weighted model 

averaging. 
12

TABLE 1-2. Parameter estimates from general linear models describing variation in Long-tailed 

Duck body condition during molt and subsequent feather regrowth in 1999. Parameter 

likelihoods, i.e. AICc weights summed across all considered models that included a 

given variable, indicate the importance of that variable. Parameter estimates are 

weighted averages (based on model AICc weights) from all candidate models and SE's 

are based on unconditional variances over the suite of models considered. Parameter 

likelihoods are not given for parameters that were included in all models. 

Response 

variable 

Explanatory 

variablea 

Parameter 

likelihood 

Parameter 

estimate SE 

DRY INTERCEP -- 322.5255 275.550 

CARCASS KEELEN -- 2.5275 0.750 

 BILLWID -- 5.8411 3.766 

 NINTH 0.9964 -0.9939 0.401 

 DATE 0.8392 -1.7806 0.828 

 SQNINTH 0.6861 0.0033 0.002 

 SITE 0.6446 -1.7910 7.104 

 SITENINTH 0.2674 -0.0484 0.050 

LIPID INTERCEP -- 334.6773 201.890 

 KEELEN -- 1.3831 0.636 

 BILLWID -- 4.6972 3.187 

 DATE 0.9692 -2.0104 0.670 

 NINTH 0.9429 -0.4533 0.134 

 SITE 0.7667 -2.5415 7.264 

 SITENINTH 0.3229 -0.0508 0.051 

 SQNINTH 0.3008 <0.0001 0.001 

PROTEIN INTERCEP -- 48.2950 38.024 

 KEELEN -- 0.9371 0.208 

 BILLWID -- 0.1587 1.019 

 NINTH 0.9999 -0.4900 0.090 

 SQNINTH 0.9999 0.0033 0.001 

 SITE 0.4069 -0.5898 0.889 

 DATE 0.2462 -0.0139 0.060 

 SITENINTH 0.0965 0.0001 0.004 

a KEELEN - keel length, BILLWID - culmen width, NINTH - length of ninth primary, SITE - study 

site, SQNINTH - squared ninth primary, SITENINTH - interaction term between site and 

length of ninth primary, DATE - collection date (Julian). 
13

TABLE 1-3. General linear models describing variation in body condition of male Long-tailed Ducks during molt 

regrowth in 2000. The models best fit by our data, and those with substantial support (i.e., with ∆AIC < 2

presented. Keel length and bill width were included for all models to control for body size, site and ninth length were included in all models, and date was included in models of lipid and dry carcass mass based results. 

R Response 

variable Model Explanatory variablesa Number of parameters ∆AICc AICc weight Total CARCASS c 1 SITENINTH 8 0.00 0.39 0.41  2 SITENINTH TRT 9 1.82 0.16 0.41 LIPID c 1 SITENINTH 8 0.00 0.36 0.40 

 2 SITENINTH SQNINTH 9 0.91 0.23 0.41 

PROTEIN d 1 TRT 7 0.00 0.15 0.33 

 2 SQNINTH 7 0.43 0.12 0.32  3 SQNINTH TRT 8 0.92 0.09 0.34  4 SITENINTH SQNINTH 8 0.93 0.09 0.34 

 5 SITENINTH TRT 8 1.54 0.07 0.33  6 --e 6 1.53 0.07 0.30 

 7 SITENINTH SQNINTH TRT 9 1.75 0.06 0.35  8 TRT TRTNINTH 8 1.78 0.06 0.33 ASH d 1 --e 6 0.00 0.31 0.08 

 2 SITENINTH 7 1.89 0.12 0.09  3 TRT 7 1.94 0.12 0.09 

a Parameters added to baseline model. SQNINTH - squared ninth primary, SITENINTH - interaction term betw

length of ninth primary, TRT - treatment, TRTNINTH - interaction term between treatment and ninth p

b Improvement in R2 with addition of specified explanatory variables.

c Site, keel length, culmen width, length of ninth primary, and collection date were included in all models. 

d Site, keel length, culmen width, and length of ninth primary were included in all models. e Model included only site, keel length, culmen width, and length of ninth primary. 
14

TABLE 1-4. Parameter estimates from general linear model analyses describing variation in Long-tailed Duck 

body condition during molt and subsequent feather regrowth in 2000. Parameter likelihoods, i.e. AICc 

weights summed across all considered models that included a given variable, indicate the importance of 

that variable. Parameter estimates are weighted averages (based on model AICc weights) from all 

candidate models and SE are based on unconditional variances over the suite of models considered. 

Parameter likelihoods are not given for parameters that were included in all models. 

Response variable Explanatory variablea Parameter 

likelihood Parameter estimate SE 

DRY CARCASS INTERCEP -- -232.6521 325.616 

 NSITE -- -1.1368 16.917 

KEELEN -- 1.0253 1.226 

 BILLWID -- 8.0976 5.064 

 NINTH -- -0.2002 0.310 

 DATE -- 1.2212 7.275 

 SITENINTH 0.91 -0.6006 0.239 

 TRT 0.44 2.3221 5.454 

 SQNINTH 0.28 0.0009 0.001 

 TRTNINTH 0.11 0.0209 0.039 

 SITTRT 0.10 -0.6022 1.715 

LIPID INTERCEP -- -103.6680 268.570 

 SITE -- -3.9026 13.855 

 KEELEN -- 0.4647 1.020 

 BILLWID -- 2.2555 4.195 

 NINTH -- 0.0652 0.330 

 DATE -- 0.6159 0.953 

 SITENINTH 0.93 -0.5359 0.204 

 SQNINTH 0.43 0.0020 0.002 

 TRT 0.38 0.8734 3.903 

 TRTNINTH 0.10 0.01485 0.028 

 SITTRT 0.09 -0.3454 1.216 

PROTEIN INTERCEP -- 15.2135 37.222 

 SITE -- 0.1558 3.169 

 KEELEN -- 0.4080 0.306 

 BILLWID -- 4.2220 1.205 

 NINTH -- -0.1625 0.106 

 TRT 0.69 1.5782 2.094 

 SQNINTH 0.50 0.0008 0.001 

 SITENINTH 0.37 -0.0268 0.030 

 TRTNINTH 0.20 0.0111 0.017 

 SITTRT 0.17 0.1796 0.658 

a SITE - study site, KEELEN - keel length, BILLWID - bill width, NINTH - length of ninth primary, SQNINTH - 

squared ninth primary, SITENINTH - interaction term between site and length of ninth primary, DATE - 

collection date (Julian), TRT - treatment, TRTNINTH - interaction term between treatment and ninth 

primary length, SITTRT - interaction term between site and treatment. 
15

Effects of Disturbance on Nutrient Use 

From 21 July to 10 August 2000, we collected an additional 9 males prior to molt (length 

of ninth primary = 141-161mm) and another 88 males that had molted and were in the 

process of regrowing new feathers (length of ninth primary = 0-117mm). 

Differences in the rate of nutrient use (parameters SITENINTH and TRT) were 

important in explaining the variation in condition of Long-tailed Ducks in 2000 (Table 1-

3). We found some indication of a treatment effect on carcass mass and lipid levels 

(Tables 1-3 and 1-4, Figure 1-2), as well as a change in rate of decline in carcass mass 

through feather regrowth in 2000 (i.e., SQNINTH term; Table 1-4). However, 

improvement in model fit related to treatment effects, as indicated by the partial 

correlation coefficients (Table 1-3) was relatively small, and the rate of decline in 

carcass mass and lipid levels differed between sites (Table 1-4). 

There was marginal support for a treatment effect on protein levels in Long-tailed Ducks 

(Table 1-4, Figure 1-2). Marginal support was also given for a change in the rate of 

protein use through feather regrowth as indicated by the squared ninth term (Table 1-4). 

The best models only explained <10% of the variation in ash levels (Table 1-3). 

DISCUSSION 

Wing molt is a nutritionally costly process for Long-tailed Ducks in the Beaufort Sea 

region, as indicated by declining carcass mass and lipid levels throughout this study. 

Declines in lipid reserves explained a large portion of the declines we saw in body 

mass. Lipids are a readily catabolized source of energy (Blem 1976, Peterson and 

Ellarson 1979). Thus, declines in lipids could be explained by energetic demands, 

reduced foraging, or cost of feather growth (Blem 1976, Peterson and Ellarson 1979). 

Our findings are similar to Peterson and Ellarson's (1979) study of Long-tailed Ducks 

wintering on Lake Michigan, in which most of the decrease in body weight was due to a 

decline in lipid reserves. Cursory observations of fat deposits showed that, similar to 

Musacchia's (1953) findings, during the early molt Long-tailed Ducks had high amounts 

of subcutaneous fat, which are the primary depots for storage (Johnston 1964). 

In contrast, protein levels decreased and then rose to levels similar to those of pre-molt 

birds as feathers were grown. The decrease in protein levels during molt indicated that 

Long-tailed Ducks initially used endogenous reserves to meet increased demands for 

protein. Although feathers are composed primarily of protein and molt increases the 

daily requirement of this nutrient, muscles were a store of labile protein for energy and 

feather growth (Kendall et al. 1973, Swick and Benevenga 1977, Murphy and King 

1982). The rise in protein levels we observed suggested that Long-tailed Ducks were 

able to meet the demands of feather development in the latter stages of molt through 

nutrients obtained from foods. Additionally, the return to protein levels similar to that of 

pre-molt birds resulted from the development of breast muscle necessary to regain 

flight. 
16

Long-tailed Ducks are similar to other waterfowl in not catabolizing minerals stored in 

bone tissue (Ankney 1979, Thompson and Drobney 1996). Our analysis failed to 

explain the small, observed variation in mineral content. Consequently, it would appear 

that Long-tailed Ducks did not experience any shortages in mineral availability and were 

able to meet demands from exogenous sources. 

We suggest that nutrient dynamics provide a clearer picture of the adaptive strategies 

employed by ducks during wing molt. The decline in carcass mass slowed during the 

later stages of wing molt, because protein levels increased even though lipid levels 

declined at a constant rate. Continued mass loss during a period of muscle building 

suggested that the dynamics of condition during molt were related to flight attainment as 

well as a use/disuse of locomotion muscles (Ankney 1979, Thompson and Drobney 

1996, Brown and Saunders 1998). Increases in muscle mass were likely due to 

changes in behavior and wing surface area (e.g., wing flapping and use while diving) 

and the resulting recovery of flight muscle from disuse. The concurrent continual 

decline in lipid levels allowed birds to attain lower body mass while rebuilding muscle 

necessary for flight. This decreased mass effectively lowered the wing load (ratio of 

body mass to wing area) for Long-tailed Ducks, given a reduced wing area during molt, 

thereby shortening the period of flightlessness (see Chapter 3 for further discussion of 

mass and flight dynamics). 

Our results suggest that limited disturbance had little or no effect on the nutritional 

status of molting Long-tailed Ducks. Males molting in disturbed areas were in slightly 

better condition than males in undisturbed areas. Although we found limited support for 

a treatment effect in our models, the difference in condition of birds between disturbed 

and undisturbed areas as reflected by the improvement in model fit was trivial. Thus, 

this result is more likely due to experimental error than any real difference in condition 

between treatments. Additionally, if Long-tailed Ducks experience losses in mass as a 

strategy to shorten the flightless period, additional losses in mass may be negligible. 

However, our inability to detect a disturbance effect may have been due to the 

limitations of our study design. Due to a major storm event in 2000, we were unable to 

subject birds to disturbance for the duration of the molt period and make the final 

planned collection. Additionally, our disturbance experiment was restricted to limited 

boat traffic. Alternate sources, more frequent, or intense disturbance may have 

produced an effect on Long-tailed Duck nutritional dynamics. 

Moreover, we measured changes in carcass and nutrient mass with respect to the 

length of the ninth primary. Our assumption was that changes in behavior related to 

disturbance would affect the rate of nutrient use during molt. Thus, we would have 

been unable to discern an effect if nutrient limitation during molt only influenced feather 

growth rates. Such an effect would be impossible to detect unless repeated measures 

of feather lengths on individuals could be included in the analysis. 

Finally, the observed difference in carcass mass and nutrient composition dynamics 

between sites, in 2000, may be due to an epizootic during this study (see Chapter 7). In 

2000 there was a much higher (44%) incidence of exposure to a previously unknown 
17

adenovirus in the Control Area. This disease caused an undocumented number of 

mortalities among Long-tailed Ducks using the Control study site, and may account for 

nutrient levels being lower in those birds. 

MANAGEMENT IMPLICATIONS 

Use by large numbers of birds and the high nutritional costs associated with molt 

suggest that Beaufort Sea lagoons provide resources that are important to the ecology 

of Long-tailed Ducks. These lagoons offer shelter from winds and provide calmer 

waters in which molt can occur. Long-tailed Ducks are a small sea duck and likely 

spend more time foraging relative to larger sea ducks (Goudie and Ankney 1986). The 

ability of Long-tailed Ducks to meet the demands of molt depends upon their 

maintenance requirement, nutrient supply, and ability to compensate through metabolic 

and behavioral plasticity (King and Murphy 1985). It appears that during the early 

stages of molt Long-tailed Ducks rely heavily upon lipid and protein stores. Although 

lipid reserves decreased throughout the study period, during the latter stages of molt, 

muscle mass increased presumably using protein attained from food resources. This 

strategy allowed Long-tailed Ducks to lose mass quickly, but regain muscle (protein) in 

order to regain flight. Protein deficiencies during this period could have resulted in the 

prolonged duration of molt (i.e. slower feather growth) or the malformation of growing 

feathers (Murphy and King 1987). Thus, an abundant protein source that was readily 

available during late July and early August appeared vital to rapid re-attainment of flight 

and feather regrowth. 

Similar rates of nutrient use during 1999, suggested that individuals were able to meet 

nutritional demands of molt equally well at both sites. Additionally, natural variation in 

condition due to stage of molt, site, and disease appears greater than any effects of 

disturbance for birds molting in near-shore waters. However, differences in body 

condition between Control and Industrial Areas in 2000 suggested that these 

populations are somewhat segregated. Molting and wintering sea ducks have been 

shown to exhibit high site fidelity (Flint et. al. 2000 and Robertson et. al. 2000), which 

may be advantageous to locating food resources and areas with low predation pressure 

(Robertson et al. 2000). Therefore, we suggest that it is important to maintain the 

integrity of the entire complex of lagoons, and that further investigations of molt site 

fidelity and population structure of Long-tailed Ducks are warranted. 
18

Chapter 2 

Body Molt Of Male Long-Tailed Ducks 

INTRODUCTION 

Whereas the molting regimes of most species of waterfowl consist of one or two molting 

cycles per year (i.e., pre-alternate and pre-basic), Long-tailed Ducks are unique in that 

they carry out an additional third, supplemental molt (Salomonsen 1949, Humphrey and 

Parks 1959, and Palmer 1976). The supplemental molt occurs from approximately 

September through mid-October, between the pre-basic and pre-alternate molts, and 

involves replacement of the head, neck, and scapular regions (Palmer 1976). 

Subsequently, pre-alternate molt occurs gradually from some time in September 

through November or December. This molt involves the replacement of all feathering 

except the tail, wing, and posterior under-parts of the bird (Palmer 1976). Pre-basic 

molt occurs in two periods. In the spring (commonly April; Palmer 1976) areas including 

the head down to the breast and much of the mantle are shed. Pre-basic molt resumes 

in late summer (July) and continues into fall when Long-tailed Ducks molt the remaining 

body regions as well as remiges. During this molt Long-tailed Ducks are flightless for 

approximately three to four weeks (Palmer 1976). 

Molt can be a useful indicator of the physiological status, habitat requirements, and 

probable fitness of migratory waterfowl (Lovvorn and Barzen 1988). Understanding the 

timing of pre-basic molt is important since it may occur simultaneous with the wing molt, 

a period that leaves waterfowl vulnerable to disturbance and predation. During this 

period, birds may lose as much as 25% of their total body mass (Chilgren 1977, King 

1980). Nutrient demands increase for the synthesis of new feathers, increased amino 

acid metabolism, increased cardiovascular activity to supply feather regrowth, daily 

changes in protein levels, and an increased need for iron to produce red blood cells and 

for calcium associated with bone formation (Murphy and King 1992). Birds can cope 

with the added costs of molt by increasing foraging effort, reducing other nutrient costly 

activities, catabolizing stored nutrients, or a combination of the three (Ankney 1979, 

Murphy and King 1982). Catabolized reserves often play an important role in supplying 

the necessary nutrients to carry out feather replacement (Ankney 1979). Therefore, 

characterizing the body molt as it relates to remige growth provides a measure of the 

intensity of molting pressures and, when compared to historical information on condition 

and timing of molt, indicates whether the ability to meet the demands of molt have been 

threatened. Our objective was to describe the timing and intensity of body molt in 

relation to the stage of remige molt as it occurs in male Long-tailed Ducks. 

METHODS 

We collected molting Long-tailed Ducks through the molt period in 1999 and 2000 (see 

Chapter 1). Stage of wing molt was determined by measuring the length of the ninth 

primary from the tip of the feather to its insertion on the proximal side of the feather. To 

measure the intensity of body molt, we assigned feather tracts into eight regions: 1) 

head and neck (crown, facial, chin/throat, and neck), 2) back and rump (upper and 
19

lower back, scapulars, rump), 3) breast (chest), 4) belly, 5) greater coverts, 6) lesser 

coverts, 7) side (flank and side), and 8) tail (retrices), based on Taylor (1995). Each 

region was sampled four times by estimating the proportion of blood quills and missing 

feathers along a 1 inch dissecting probe inserted between feathers. We defined a blood 

quill as any growing feather containing blue color within the shaft or any feather with 

feather sheath present at or above the skin surface. We categorized molt intensity into 

five levels based on the estimated portion of blood quills and missing feathers in each 

sample: 0 (0%), 1 (1-24%), 2 (25-49%), 3 (50-74%), and 4 (>75%). Samples were 

averaged and the corresponding score was recorded for that region. 

We were interested in estimating the intensity and duration of molt for each feather tract 

with respect to the stage of wing molt. Therefore, we used nonlinear regression 

(Sigmaplot 2001 v7.0, SPSS, Inc.) to fit the Gaussian three-parameter model 2 0 0.5 













 − −

= b

x x

y ae

where the estimated parameter a corresponds to the function maxima, b determines the 

width of the bell curve, x is the dependent variable, x0 is the estimated value of the 

independent variable at the corresponding function maxima, and y is the independent 

variable. Thus, for a given feather tract and individual, y and x are the observed molt 

score and length of the ninth primary (i.e., molt stage). It follows then, that the 

population parameters estimated for each feather tract are, a – peak molt intensity, b – 

variation in molt intensity with respect to molt stage, and x0 – length of ninth primary at 

which molt intensity peaks. A high degree of synchrony in molt among individuals 

resulted in good model fit as indicated by a large R2. Large variability in timing of peak 

molt intensity with respect to molt stage was indicated by a relatively large b. Thus, a 

small R2 in concert with a large value for b suggested a low degree of synchrony in 

feather molt at the population level. Conversely, a large R2 indicated a high degree of 

synchrony among individuals. 

RESULTS 

Specimens were collected (n = 187) from 28 July to 14 August 1999, and from 21 July 

to 10 August in 2000. Long-tailed Ducks molted each body region during our study 

except head and neck (Table 2-1, Figure 2-1). Although we found evidence of feather 

regrowth, only six males (3%) showed any degree of molt in the head and neck (Figure 

2-1A). 

During our study, molt intensity achieved the highest levels in the lesser coverts, greater 

coverts, tail, and back and rump regions. Non-linear models explained over 60% of the 

variation in molt scores for lesser and greater coverts. Lesser coverts appeared to 

reach the highest molt intensity slightly earlier than greater coverts (Figures 2-1B and 2-

1C). Retrices were the next feathers to reach peak molt intensity (Figure 2-1D). The 

back and rump region also reached highest molt intensity during mid-molt of the 

remiges (Figure 2-1E). Both the lesser and greater coverts appeared to complete the 

process of molt before remige regrowth was complete, while the back and rump regions 
20

were nearly completed during the same time period. Molt of retrices appeared to have 

continued into the latter stages of remige regrowth. 

Our models explained nearly 60% of the variation in molt scores for the breast region 

and over 50% for the belly region (Table 2-1). We found that the breast and belly 

regions started molt later than the greater and lesser coverts, retrices, and back and 

rump regions and attained maximum molt intensity during the latter half of molt. The 

breast region attained maximum intensity slightly ahead of the belly region (Figures 2-

1F&G). However, the figures indicate that the latter stages of feather growth for the 

breast and belly regions did not occur within our study period and probably continued 

until and perhaps beyond full remige regrowth. 

Molt in the side and flank region occurred throughout our study (Figure 2-1H). Values 

for molt intensity in this region were also highly variable as indicated by the high value 

of b for this feather tract (Table 2-1). 

Table 2-1. Parameter estimates, standard errors (SE), and coefficients of multiple 

determination (R2) for three-parameter Gaussian models of molt intensity for male 

Long-tailed Ducks in Beaufort Sea lagoons, 1999-2000. 

Region 

Maximum 

Intensity (a) 

Variance in intensity 

with respect to stage 

of wing molt 

(b) 

Wing molt 

stage 

 at peak 

intensity 

(x0) R2 

Head and Neck Estimate 0.43 0.38 15.40 0.30

 SE 1.32 0.85 0.47 

Back and Rump Estimate 3.30 45.53 81.59 0.53

 SE 0.10 2.35 1.89 

Breast Estimate 2.49 35.73 101.70 0.52

 SE 0.13 3.40 3.35 

Flank Estimate 8.37 306.61 -550.31 0.06

 SE 119.14 1374.08 5218.59 

Greater Coverts Estimate 4.25 36.44 59.81 0.63

 SE 0.12 1.43 1.30 

Lesser Coverts Estimate 4.25 30.74 53.35 0.65

 SE 0.15 1.36 1.30 

Tail Estimate 4.06 42.84 101.94 0.60

 SE 0.15 3.42 3.49 

Belly Estimate 2.49 37.63 108.52 0.59

 SE 0.11 3.55 3.96 
21

0 20 40 60 80 100 120 140

0

1

2

3

4

Ninth primary length (mm)

0 20 40 60 80 100 120 140

0

1

2

3

4

Ninth primary length (mm)

0 20 40 60 80 100 120 140

Molt Intensity

0

1

2

3

4

0 20 40 60 80 100 120 140

Molt Intensity

0

1

2

3

4

0 20 40 60 80 100 120 140

Molt Intensity

0

1

2

3

4

0 20 40 60 80 100 120 140

0

1

2

3

4

0 20 40 60 80 100 120 140

0

1

2

3

4

0 20 40 60 80 100 120 140

Molt Intensity

0

1

2

3

4

A

B

C

D

E

F

G

H

Figure 2-1. Molt intensity in each feather tract through ninth primary growth for male Long-tailed 

Ducks. (A) Head and neck, (B) lesser coverts, (C) greater coverts, (D) tail, (E) back and 

rump, (F) belly, (G) breast, and (H) flank and side. Dotted lines indicate the estimated 

molt stage of wing molt (i.e., ninth primary length) corresponding to peak molt intensity.
22

DISCUSSION 

All feather tracts, except head and neck and flank and side, increased in molt intensity 

as wing molt progressed. Our research substantiated Palmer's (1976) characterization 

that molt occurs in the head and neck region prior to all other feather tracts during the 

pre-basic molt. Molt in the flank and side region appear to be more variable throughout 

the wing molt and the lack of a discernable "peak" is probably indicative of a slower rate 

of molt in these areas. Both the greater and lesser coverts, and back and rump regions 

began molting concurrent with the loss of remiges and were re-grown by the time 

remige regrowth was complete. 

Long-tailed Ducks may reduce the nutritional demands of molt by staggering the molt of 

various feather regions over time or by undergoing molt during periods when molt does 

not overlap with other nutrient costly activities such as migration (Heitmeyer 1987). Our 

data indicate that greater and lesser coverts and back and rump regions likely 

completed molt prior to additional post-breeding movements, which may reduce costs 

associated with the initiation of the supplemental molt as it begins in September (Palmer 

1976). However, King and Murphy (1985) advise that nutritional status should be 

considered in the context of the life history of a species with regard to nutritional 

requirements and accessibility, as well as the repertoire of adjustments available to 

avert or minimize discrepancies between the two. Additionally, Lovvorn and Barzen 

(1988) caution the interpretation of molt patterns in terms of staggered costs and found 

that timing of molt was associated with a photoperiodic response for Canvasbacks. 

Leafloor and Ankney (1991) also suggest that among unsuccessful nesting female 

Mallards (Anas platyrhynchos) social environment appears to influence the timing of 

molt. For Long-tailed Ducks molting in the cold waters of the Beaufort Sea, a less 

synchronous pattern of body molt may also lessen the thermal costs associated with 

reduced insulatory qualities. 

Additionally, completion of the covert molt prior to full remige regrowth may be indicative 

of an adaptive strategy to minimize the flightless period. Many species of waterfowl 

regain flight before remige regrowth is complete (Hohman et al. 1992). Brown and 

Saunders (1998), showed that body mass dynamics and wing surface area played a 

significant role in enabling Blue-winged Teal (Anas discors) to re-attain flight capability 

before remige growth was complete. However, smoothness and shape of the wing are 

also important in reducing wing drag and maximizing lift (Pennycuick 1989). We 

suggest that while the overall timing of molt in Long-tailed Ducks may be controlled by 

ultimate factors such as photoperiod, the order of molt among specific feather tracts, 

specifically the molt of wing coverts, is timed to reduce the period of flightlessness. 

Further, the staggered timing of molt among feather tracts may serve to spread the 

nutritional demands of feather growth and reduce the energetic costs associated with 

thermoregulation in cold-water environments. 
23

Chapter 3 

Mass Dynamics in Relation to Flight Parameters Following Remige Molt of 

Male Long-tailed Ducks 

INTRODUCTION 

Unlike most bird species, waterfowl undergo synchronous wing molt and lose the ability 

to fly for approximately three weeks. During this flightless period, waterfowl experience 

physiological changes as well as changes in behavior. Physiological changes include 

shifts in body mass and the mass of muscle groups associated with locomotion during 

wing molt and feather re-growth (Ankney 1979, Thompson and Drobney 1996, Brown 

and Saunders 1998), which have been attributed to energetic costs of molt and usedisuse (i.e., behavioral) adaptations to the loss of flight capabilities (Brown and 

Saunders 1998). Swimming and diving become the primary modes of travel, as well as 

means of predator avoidance. These behavioral changes result in changes in the use 

of muscle groups and may cause shifts in the mass associated with each group of 

muscles. 

Physiological changes associated with wing molt can also have a direct influence on 

flight capability. Body mass, wing area, wingspan, and flight muscle and leg muscle 

mass can all be affected during wing molt and influence the ability to regain flight 

capability. Whether changes in mass are the result of stressful conditions in which 

nutrient demands exceed nutrient ingestion and cause a catabolization of endogenous 

reserves is debatable (Ankney 1979). Brown and Saunders (1998) hypothesized that 

changes in mass during molt are regulated to facilitate a quicker return to flight rather, 

than the result of nutritional and energetic costs associated with molt. These regulated 

changes may be an innately programmed process in which endogenous reserves are 

drawn on even under optimal conditions (King and Murphy 1985). Changes in body 

composition have been studied in other species and may be normal adaptative changes 

rather than changes caused by nutritional stress (Freed 1981, Baldassarre et. al. 1986). 

Swaddle and Witter (1997) found that artificial loss of wing feathers alone was enough 

to cause European Starlings (Sturnus vulgaris) to lose body mass. This loss indicated 

that a loss of flight performance alone could cause an adaptive response of loss in body 

mass. In the case of molting Long-tailed Ducks, changes in body condition and muscle 

mass may also be normal regulated changes to facilitate a shorter flightless period and 

early return to flight. 

We examined four factors related to the physiological changes that occur subsequent to 

the wing molt that may affect Long-tailed Duck flight and swimming abilities. Wing 

loading (body mass/wing area) is a measure of the load supported by the wings and 

influences the amount of power necessary to attain flight. Power loading (body 

mass/pectoral muscle mass) is a measure of the ratio of flight muscle power given the 

amount of body mass. Wing-power loading (pectoral muscle mass/wing area) is a 

measure of the power available to generate lift. All three of these factors directly 

influence dynamics of flight and consequently affect the ability to attain flight. Legpower loading (body mass/leg muscle mass) is a measure of power available for 
24

swimming given the amount of body mass and may provide flightless birds with the 

power necessary to reach higher swimming and diving speeds necessary for survival 

during the flightless period (Brown and Saunders 1998). Hence, we examined changes 

in nutrient levels, body mass, flight muscle mass, leg muscle mass, and changes in 

wing area that Long-tailed Ducks undergo during molt and how those changes affect 

flight and swimming capabilities. Our objective was to describe changes in body mass, 

muscle mass, and nutrient composition as they relate to the ability of Long-tailed Ducks 

to regain flight after the wing molt. 

METHODS 

We collected molting Long-tailed ducks through the molt period in 1999 and 2000 (see 

Chapter 1). We recorded culmen length and width, tarsus length, and total weight from 

freshly collected specimens. Keel length was recorded as an additional index to size 

during later laboratory analysis. Birds were then frozen and transported to Auburn 

University for laboratory analysis. Prior to dissection, we measured morphological 

features pertaining to flight dynamics. Each bird was placed ventrally on a flat surface 

and the right wing was traced (Pennycuick 1989). We then scanned each tracing and 

determined the area of each wing (+ 0.01cm) using image analysis in MATLAB 

(Mathworks, Inc). 

We plucked each bird and removed and weighed (+ 0.01g) the right breast muscles (M. 

pectoralis and M. supracoracoideus), right leg muscles (Gastrocnemius, Iliotibialis 

cranalis, Peroneus longus, and Tibialis cranialis), heart, empty gizzard, and empty 

digestive tract (Bailey 1985, Thompson and Drobney 1996, and Brown and Saunders 

1998). After weighing, organs were returned to the carcass and the carcass was 

weighed again to determine the plucked/digesta free wet carcass mass (+ 1g) (CARC). 

A small portion of the heart muscle (< 1g) was removed and preserved for DNA 

analysis. 

We examined the dynamics of flight parameters (wing load, power loading, etc.) using 

simple linear and polynomial regressions versus wing area. Wing load (g/cm2

) was 

CARC divided by area of the right wing. Power load (g/g) was CARC divided by the 

mass of the right pectoral muscle. Wing power (g/cm2

) was calculated by dividing the 

right wing area by the right pectoral muscle. Leg power (g/g) was calculated by dividing 

CARC by the total mass of right leg muscles. 

We examined changes in pectoral muscle and leg muscle masses with respect to stage 

of molt using general linear models. We used 26 linear models with the dependent 

variables: breast muscle mass, and right leg muscle mass; and independent variables: 

keel length (KEELEN), bill width (BILLWID), date (DATE), length of the ninth primary 

(NINTH), and squared length of the ninth primary (SQNINTH). Keel length and bill 

width were included as independent variables in all models to control for variation due to 

size. We also examined potential differences among sites (SITE), and an interaction 

between site and length of ninth primary as (SITENINTH). Potential differences among 

years (YEAR) and a year/site interaction (YRSITE) were also analyzed. We selected 
25

the models that best fit our data based on ∆AIC < 2.0 (Burnham and Anderson 1998). 

For comparative purposes, we included mean values for birds collected prior to wing 

molt, and birds collected in 1999 that were capable of flight during the latter stage of 

feather re-growth. 

For wing loading analysis involving nutrient composition, we used dry weights obtained 

from compositional analysis. Contributions of each nutrient to wing loading were 

calculated using linear regression. Dry weights were used for the nutritional analysis to 

avoid biases resulting from dessication during laboratory procedures. 

RESULTS 

We used data from 186 birds collected during the summers of 1999 and 2000, including 

12 premolting males and 12 males in the latter stage of feather re-growth, capable of 

flight, as a reference to flight dynamic conditions prior to flightlessness. As expected, 

wing load was negatively correlated with wing area (Figure 3-1), and the rate of decline 

in wing load decreased until premolt levels were reached. 

Fifty-two percent of the variation in pectoral muscle mass was explained by best-fit 

models versus molt stage (i.e., ninth primary length, Table 3-1). There was unequivocal 

support in our data for a change in the relationship between pectoral mass and ninth 

primary length during feather re-growth (i.e., inclusion of SQNINTH, Table 3-2). There 

also was strong support for a difference in pectoral mass between sites. Pectoral mass 

declined initially and then increased during feather re-growth (Figure 3-2). The 

relationship between wing-power load and wing area was similar to the relation between 

wing load and wing area (Figure 3-3). Pectoral mass only explained a small portion of 

the variation in body mass (R2

 = 0.16). Power load increased initially and then 

decreased, but only 30% of the variation was explained by molt stage (Figure 3-4). 

TABLE 3-1. General linear models describing variation muscle mass of male Long-tailed Ducks during 

molt and feather regrowth. The models best fit by my data, and those with substantial support (i.e., 

with ∆AIC < 2.0), are presented. Keel length and bill width were included for all models to control 

for body size. Excludes treatment birds from 2000.

Response 

variable Model Explanatory variablesa Number of 

parameters AICc 

AICc weight R2 

Pectoralis Muscle 1 SITE NINTH SQNINTH 6 0 0.293 0.522 

2 SITENINTH SQNINTH 5 0.26 0.257 0.515 

3 SITE NINTH SQNINTH YEAR 7 1.62 0.130 0.523 

4 NINTH SQNINTH YEAR 6 1.94 0.111 0.516 

Leg Muscles 1 NINTH SQNINTH 5 0.000 0.236 0.278 

 2 SITE NINTH SQNINTH 6 0.697 0.167 0.285 

3 SITE NINTH SITENINTH 

SQNINTH 7 1.357 0.120 0.291 

a

NINTH - length of ninth primary, SITE - study site, SQNINTH - squared ninth primary, SITENINTH - 

interaction term between site and length of ninth primary, YEAR - collection year. 
26

Nearly thirty percent (range = 0.28-0.29) of the variation in leg mass was also explained 

by best-fit models versus molt stage (i.e., ninth primary length, Table 3-1). There was 

unequivocal support for a change in the relationship between leg mass and ninth 

primary length during feather regrowth (i.e., inclusion of SQNINTH, Table 3-2). There 

was moderate support in the data for differences between sites and years; however, 

neither parameter was present in the best models. Leg mass increased slightly and 

then decreased during feather re-growth (Figure 3-2). There was little variation in leg 

power load through feather re-growth (R2

 < 0.01, Figure 3-5). Leg mass explained 

relatively little variation in body mass (R2 = 0.30) and pectoral mass (R2 = 0.01). 

TABLE 3-2. Parameter estimates from general linear model analyses describing variation in 

Long-tailed Duck pectoral muscle and leg muscle mass during molt and subsequent 

feather re-growth. Parameter likelihoods, i.e. AICc weights summed across all considered 

models that included a given variable, indicate the importance of that variable. Parameter 

estimates are weighted averages (based on model AICc weights) from all candidate 

models and SE are based on unconditional variances over the suite of models 

considered. Parameter likelihoods are not given for parameters that were included in all 

models or summed to 1. Excludes treatment birds from 2000.

Response variable 

Explanatory 

variablea 

Parameter 

likelihood 

Parameter 

estimate SE 

Pectoralis Muscle INTERCEP -- -10.9480 10.845 

 KEELEN -- 0.3356 0.082 

 BILLWID -- 1.1828 0.375 

 NINTH -- -0.2695 0.027 

 SQNINTH -- 0.0023 0.000 

 SITE 0.63 0.5321 0.518 

 YEAR 0.35 -0.1642 0.282 

 SITENINTH 0.16 0.0007 0.003 

 YRSITE 0.06 -0.0183 0.076 

Leg Muscles INTERCEP -- -12.4725 6.565 

 KEELEN -- 0.2640 0.050 

 BILLWID -- 0.7796 0.228 

 SQNINTH 0.92 0.0266 0.016 

 NINTH 0.81 0.3692 0.334 

 SITE 0.57 -0.0020 0.002 

 YEAR 0.31 -0.0573 0.153 

 SITENINTH 0.19 -0.0003 0.000 

 YRSITE 0.06 0.0312 0.054 a

KEELEN - keel length, BILLWID - bill width, NINTH - length of ninth primary, SITE - study site, 

SQNINTH - squared ninth primary, SITENINTH - interaction term between site and length 

of ninth primary, YEAR - collection year, YRSITE - interaction term between year and 

study site. 
27

The dynamics of nutrient composition and wing load indicated that protein was the 

greatest factor contributing to wing load through molt and feather re-growth (Figure 3-6). 

Contributions of protein to wing load decreased initially and then increased largely due 

to changes in pectoral mass. Contributions of lipid levels to wing load declined 

constantly through feather re-growth, while contributions of mineral levels remained 

constant. Wing load due to each nutrient was relatively constant until the latter stage of 

feather re-growth when protein levels increased (Figure 3-7). Once Long-tailed Ducks 

were capable of flying, the relative contribution of lipid to wing load decreased rapidly. 

Wing area (cm2)

50 100 150 200 250 300

Wing Load (g/cm

2)

0

2

4

6

8

10

12

y = 0.0002x2 - 0.1065x + 15.739

R2 = 0.9397

Flying Birds

Pre-molt

Trendline

Body mass/Wing area

Figure 3-1. Dynamics of wing load for molting male Long-tailed Ducks 1999-2000 (wing loads are based 

on wet weights). Reference lines for pre-molting and individuals that had re-attained flight included for 

comparison. 
28

Length of Ninth Primary (mm)

0 20 40 60 80 100 120 140

Muscle Mass (g)

30

40

45

50

55

Pectoral

Muscle*

Leg

Muscle*

*BOD 2000 obscured by BOD 1999

 FLX 2000 obscured by FLX 1999

BOD 1999 

FLX 1999 

BOD 2000 

FLX 2000 

Figure 3-2. Changes in pectoral and leg muscle mass in relation to molt stage for molting male Longtailed Ducks 1999-2000. 

Wing area (cm2)

50 100 150 200 250 300

Wing Power-Load (g/cm2

) 0.1

0.2

0.3

0.4

0.5

0.6

0.7

y = 0.00002x2 - 0.0071x + 1.0032

R2 = 0.9056

Pre-molt

Flying

Pectoral Mass/Wing Area

Trendline

Figure 3-3. Wing power load (pectoral muscle mass/wing area) in relation to molt stage for molting 

male Long-tailed Ducks 1999-2000. Reference lines for pre-molting and individuals that had reattained flight included for comparison. 
29

Wing area (cm2)

50 100 150 200 250 300

Wing Power-Load (g/cm2

)

0

5

10

15

20

25

y = 0.0002x2 + 0.0365x + 14.339

R2 = 0.3058

Body Mass/Pectoral Mass

Trendline

Premolt & Flying

Figure 3-4. Power load (carcass mass/right pectoral muscle mass) in relation to molt stage for 

molting male Long-tailed Ducks 1999-2000. Reference lines for pre-molting and individuals 

that had re-attained flight included for comparison. 

Wing area (cm2)

50 100 150 200 250 300

Leg-Power Load (g/g)

10

12

14

16

18

20

22

24

26

28

30

y = - 0.0019x + 22.41

R2 = 0.005

Flying Pre-molt

Body Mass/Leg Mass

Trendline

Figure 3-5. Leg-power load (carcass mass/leg muscle) in relation to molt stage for molting male Longtailed Ducks 1999-2000. Reference lines for pre-molting and individuals that had re-attained flight 

included for comparison. 
30

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

6080

100120

140

160

180

200

220

240260

Wing area (cm2)

Wing loading (g/cm 2)

Ash

Lipid

Protein

Figure 3-7. Percent of nutrients contributing to wing load in relation to molt stage for molting 

male Long-tailed Ducks 1999-2000 

Wing area (cm2)

50 100 150 200 250 300

Wing Load (g/cm2

) 0

1

2

3

4

5

6

Dry Carcass

Dry Lipid

Dry Protein

Dry Ash

Dry Carcass/Wing Area

y = 9E-05x2 - 0.0448x + 6.5209

R2

 = 0.8953

Dry Lipid

y = 5E-05x2

 - 0.0224x + 3.1813

R2

 = 0.9505

Dry Protein/Wing Area

y = -0.0065x + 1.8208

R2

 = 0.6954

Dry Ash/Wing Area

y = -0.0014x + 0.4591

R2

 = 0.7341

Figure 3-6. Wing loading based on nutrient composition (dry weight values) in relation to molt stage for 

molting male Long-tailed Ducks 1999-2000. 
31

DISCUSSION 

Male Long-tailed Ducks experienced changes in parameters affecting flight performance 

similar to those found by Brown and Saunders (1998) for Blue-winged Teal. Wingpower loading increased and wing load more than tripled over pre-molt levels with the 

loss of flight feathers. These results were expected given the sudden drop in wing area 

experienced during synchronous wing molt. Power loading, the ratio of body mass to 

pectoral mass, also increased initially and subsequently returned to pre-molt levels 

indicating a return to levels necessary for flight. 

In contrast to flight parameters, we found that leg-power loading remained relatively 

constant throughout molt. This result differed from that of Brown and Saunders (1998). 

This was expected given the different ecological adaptations of Long-tailed Ducks and 

Blue-winged Teal. Long-tailed Ducks have been reported to depend on wing power for 

diving and therefore should not experience an increased dependence on leg power for 

foraging or escape (Snell 1985). Similar studies on diving ducks have found little or no 

change in leg muscle mass with regard to stage of molt. Bailey (1985) found that while 

male Redheads (Aythya americana) experienced losses in mass for breast muscle, 

these losses were not compensated by increases in leg muscle mass and could not 

simply be attributed to a use/disuse cause alone, but rather were related to protein 

reserves, foraging time, and anticipated seasonal use. Similarly, Hohman (1993) found 

that leg muscle mass was not related to stage of molt in Ruddy Ducks (Oxyura 

jamiacensis). While Thompson and Drobney's study (1996) of molting Canvasbacks 

indicated a change in leg muscle mass concurrent with an atrophy of breast muscle, the 

increase was only 12% compared to a greater than 40% increase in leg muscle mass 

for Blue-winged Teal (Brown and Saunders 1998). Although leg-power loading levels 

were higher during pre-molt, this was likely due to small sample size. 

We suggest that changes in pectoral muscle and nominal changes in leg muscle mass 

experienced by Long-tailed Ducks are the result of behavioral adaptations (i.e., loss of 

flight and increased foraging activity to offset costs of molt) and anticipated use (i.e., 

increased reliance on diving to escape predators). We found little support for the 

hypothesis that breast muscle mass for Long-tailed Ducks was offset by hypertrophy of 

leg muscle mass (R 2 = 0.01). While flightlessness likely resulted in increased reliance 

of dabbling ducks on swimming for predator evasion and feeding, Long-tailed Ducks 

and similar species normally demonstrate more diving and swimming behavior 

(Hohman 1993). Furthermore, diving ducks have been shown to allocate a greater 

amount of time to resting during molt than dabbling ducks (Bailey 1985, Thompson and 

Baldassarre 1991, and Adams et al. 2000). To better clarify whether Long-tailed Ducks 

experience changes in muscle mass that are normal regulated changes made in 

anticipation of use, as Bailey (1985) suggested, then studies of mass dynamics should 

include behavioral observations prior to molt as well as during the flightless period. 

Reductions in mass of male Long-tailed Ducks effectively shortened the period during 

which wing loading levels were too high for flight. Throughout the period of 

flightlessness Long-tailed Ducks experienced a decrease in carcass mass. Wing area 
32

increased as body mass decreased and stabilized as pre-molt levels of wing loading 

were reached. Thus, pre-molt wing loads were attained before remiges were 

completely re-grown allowing Long-tailed Ducks to resume flight with less than a 

complete wing. 

We found that mass loss during the initial stages of molt, when wing loading was at its 

highest, was the result of decreases in the levels of protein, lipid, and ash. As the level 

of wing loading approached pre-molt levels, the rate of decline in carcass mass 

decreased and eventually began to stabilize. Protein and lipid levels were greatest and 

made the largest contribution to overall wing loading. Changes in the levels of these 

two nutrients also resulted in the greatest fluctuations in wing load. Protein levels also 

stabilized and ultimately increased as wing loading levels approached those similar to 

pre-molt. However, lipid levels continued to decrease, offsetting the gain in protein, and 

thereby allowed wing loading levels to remain at a level similar to that of pre-molt birds. 

We suggest that mass dynamics experienced by Long-tailed Ducks cannot be explained 

by a singular causative factor, but rather a combination of factors. Changes in mass 

could result from regulated changes, costs associated with molt, and anticipated use. 

Protein levels may diminish initially due to increased demands for protein during feather 

development (Murphy and King 1992), as well as a lack of use of flight muscles. Lipid 

declines may be due to increased energetic demands resulting from a diet higher in 

protein than lipid content (Murphy and King 1987) or increased energetic demands 

resulting from a reduction in insulation due to feather loss (Blem 1976). Additionally, if 

Long-tailed Ducks make frequent stops to feed, lipid reserves may not be necessary 

during migration (Johnston 1964). While the ultimate cause of mass dynamics are 

debatable, decreases in mass resulting from decreases in levels of protein and lipid 

effectively shorten the period of flightlessness. 
33

Chapter 4 

Site Tenacity and Movements of Long-Tailed ducks 

INTRODUCTION 

Flightless Long-tailed Ducks appear to establish a relatively regular diurnal pattern of 

movement and habitat use (Johnson 1984). Flocks of birds are regularly observed 

roosting along the barrier islands late in the evening and early in the morning. During 

mid-day, birds are dispersed throughout the central portions of the lagoons. While 

flocks of birds are regularly observed in the same locations, it is unknown to what 

degree this represents consistent use by the same individuals. It has been suggested 

that patterns of habitat use by flightless Long-tailed Ducks are influenced by wind 

direction and speed. That is, birds appear to select habitats providing protection from 

wind-generated waves. Thus, individuals may not actually show site fidelity, but birds 

are observed in consistent locations simply because of overall patterns of habitat 

selection. Obviously, flightless birds have limited opportunity to move if conditions 

change following the onset of molt. Thus, understanding the degree of site fidelity and 

range of movement by flightless birds is essential to predicting the effect of potential 

displacement caused by disturbance or habitat loss. Our goal in this study was to 

measure site fidelity and movement probabilities of radio-equipped Long-tailed Ducks. 

METHODS 

From 1999 to 2002, we live-captured flocks of flightless Long-tailed Ducks using boats 

and corral traps set at known roosting locations throughout the Industrial and Control 

Areas. In 2000 and 2001, a sub-sample of males was marked with subcutaneously 

anchored radio transmitters (Peitz et al. 1995). Birds were released at the capture site 

immediately following marking. Presence/absence of marked individuals was recorded 

using Data Collection Computers (DCC) connected to Yagi antennas on fixed towers. 

The DCCs are automated data loggers that record the pulse rate of transmitters within 

the detection range of the antenna tower. Locations for DCC towers were based on 

logistics, such that data could be retrieved without disturbing birds (Figure 4-1). DCCs 

were programmed to listen for each radio transmitter for 45 seconds, then switch to the 

next frequency, and to repeat the cycle once all transmitters were scanned (Table 4-1). 

In this way, transmitters were monitored 2-3 times per hour continuously through the 

study period. Our transmitters emitted 60 pluses per minute. Because saltwater 

attenuates VHF radio signals, we considered records of 40 pulses or less (during a 45 

second scan) to indicate a bird that was diving (i.e., feeding); records with 41-50 pulses 

were considered to represent a roosting duck, and records with more than 50 pulses 

were discarded as erroneous. High pulse rates may have been caused by competing 

radio frequencies from two or more ducks close to the radio towers, interference from 

other radio sources (i.e., marine radio) or low battery voltage effects on the receiver or 

DCC unit. 
34

Table 4-1. Sampling results for radio-equipped Long-tailed Ducks monitored by Data Collection Computers (DCC) on the 

Areas, 2000-2002. 

 Industrial Area 

Year DCC sites: Bodfish Island Cottle Island E. West Long Island W. West Long Island F-Pad Oliktok Point Pingok Island E. Spy Island W. Spy Island 

Sampling (days) 12 

2000 

Sampling (hours) 272.0 

 No. radios monitored 20  No. radios detected 19  Fixes/radio (± SE) 735.9 ± 107.4  Range of fixes/radio 23 - 1518  Total Fixes 13982 

Sampling (days) 29 14 4 1 35 31 28 34 32 

2001 

Sampling (hours) 649.7 229.5 61.0 4.5 690.7 659.2 623.6 762.2 654.4 

 No. radios monitored 40 40 40 40 40 40 40 40 40  No. radios detected 18 14 1 1 6 3 28 30 15  Fixes/radio (± SE) 132.2 ± 55.1 26.5 ± 11.6 48 6 5.2 ± 1.7 3.3 ± 1.3 215.3 ± 48.2 135.5 ± 34.0 26.1 ± 6.3  Range of fixes/radio 4 - 737 2 - 170 2 – 11 2 - 6 5 – 995 2-699 2-100  Total fixes 2380 371 48 6 31 10 6027 4066 391 

Sampling (days) 37 37 34 30 37 6 

2002 

Sampling (hours) 876.7 877.5 796.4 734.7 874.6 114.5 

 No. radios monitored 59 59 59 59 59 59  No. radios detected 39 31 41 21 17 2  Fixes/radio (± SE) 62.1 ± 17.5 76.2 ± 16.6 73.5 ± 20.8 21.5 ± 7.6 48.9 ± 25.2 50 ± 24  Range of fixes/radio 2 – 645 2 - 430 2 – 755 2 - 130 2-389 26-74  Total fixes 2423 2363 3015 451 831 100 
35

Because DCC antennas were stationary and could only detect the presence of ducks 

within 2.5 km, we used DCC records to indicate the presence or absence of radioequipped ducks near the towers. Thus, we defined the area around each tower as a 

unique area and describe the fidelity of individuals to these areas. We used two 

approaches to analyze these data. First, we used standard Cormak-Jolly-Seber models 

to estimate the overall rate of fidelity. These models examine the ‘capture history’ of 

individuals and typically estimate two parameters: survival rate and recapture rate. The 

recapture parameter accounts for the fact that not all marked individuals are typically 

encountered at every ‘observation’ and is defined as the probability of recapturing or 

observing a marked individual given that it is present on the study area. However, in 

our case, our areas were defined by the coverage of antennas. Thus, the recapture 

rates account for individuals on the fringe of the antenna range or individuals that left 

the areas for a period of time then returned (i.e., temporary emigration). The survival 

parameter is related to the proportion of individuals that disappear from the area and 

Figure 4-1. Locations of Data Collection Computers (DCC) and Long-tailed Duck capture sites in 

the Control and Industrial Areas, 2000-2001. 
36

are never re-encountered. These individuals either died or permanently emigrated from 

the area. Because our study was of a short duration, we assumed that survival was 

100% and that the apparent ‘mortality’ from a mark-recapture model was actually 

permanent emigration. Hereafter, we refer to this parameter as the emigration rate. We 

examined models that estimated emigration and recapture probability on a daily basis 

(i.e., individual days were the period of observation). Unfortunately, we lacked sufficient 

data to examine if this daily rate varied through time. We fit models that allowed the 

emigration and recapture rates to differ among areas. This analysis estimates the daily 

rate of emigration. The probability of emigration for the flightless period was estimated 

by raising this daily rate to the 21st power (i.e., flightless interval). This represents the 

probability of leaving a particular monitoring area and moving to any other area. 

Accordingly, the fidelity rate is one minus the probability of permanent emigration during 

the flightless period. 

The second analysis used multi-state mark-recapture approaches to estimate the 

transition or movement probabilities among specific areas. Statistically, this approach is 

similar to the one described above, however it incorporates additional parameters that 

estimate the probability of individuals changing ‘states’. In this case, ‘states’ were 

defined as specific monitoring areas. Thus, this model has the ability to estimate the 

specific probability of individuals moving from one area to another. For this analysis, we 

lacked sufficient sample size to estimate the emigration and detection probabilities for 

each area separately. Therefore, we estimated an overall emigration rate and detection 

probability and fixed these values in subsequent analyses. We then applied multi-state 

models to estimate the transition probabilities among the different areas. It is important 

to realize that a single individual can contribute to more than one transition probability. 

That is, once an individual changes areas, it is subsequently assumed to be a member 

of the ‘new’ population and any further movements will contribute to subsequent 

transition probabilities. Thus, if an individual moves from area A to area B then back to 

area A, it will contribute to both the probability of movement from A to B and from B to 

A. Accordingly, the sum of all possible transition probabilities can be greater than one. 

RESULTS 

Estimates of site fidelity ranged from 1-96%, with considerable variation among 

locations and within locations among years (Table 4-2). In 2000, radio-marked birds 

had a very high probability (>85%) of remaining at one site through the flightless period. 

In 2001, we were only able to estimate probabilities for the Control Area and they were 

much lower (<50%). In 2002, Control Area estimates were high (72-96%) again, 

whereas Industrial Area estimates were particularly low (0.5-32%). 

Using multi-state analyses, site-specific movement probabilities suggest a general trend 

of movement to the east. For all but one transition probability, the probability of 

eastward movement (estimates below the diagonal in table 4-3) exceeded comparable 

estimates for westward movement (above the diagonal). The probability of movements 

was negatively related to distance between DCCs. The transitions with the highest 
37

probabilities (i.e., Bodfish to Cottle, Cottle to Bodfish, and Cottle to West Long) are 

between adjacent DCCs (Table 4-3). 

DISCUSSION 

Our results demonstrate that Long-tailed Ducks move considerably during the molt 

period in spite of their flightless state. While flocks of birds are consistently observed in 

the same locations on a daily basis, our results suggest that there is turnover within 

these aggregations. Unfortunately, we lack sufficient data to assess daily variation in 

fidelity rate, thus we cannot directly address the effect of wind on site fidelity. The 

general weather pattern along the Beaufort Sea coast in August is dominated by winds 

from the northeast. Thus, under normal conditions the barrier islands provide protection 

from wind generated waves. This likely explains the preference for use of the barrier 

islands for roosting. 

The strong annual variation in site fidelity is likely explained by annual variation in 

weather conditions. The low site fidelity in 2001 is associated with a period of strong 

southwesterly winds and likely represents birds moving in search of protected water. 

Interestingly, the year with the highest probability of ducks remaining at one DCC for the 

entire flightless period (i.e., 2000) was the year when we experimentally disturbed Longtailed Ducks with boats at two of the three DCC locations (Cottle and Flaxman Islands – 

see Chapter 1). In 2002, we collected our largest and most consistent set of DCC data, 

thus we were able to examine 2002 data in greater detail. Long-tailed Ducks in the 

Industrial Area appear to move more readily among DCC sites. This lack of site fidelity 

Table 4-2. Probability (%) of a bird 

remaining within the detection zone of a 

single Data Collection Computer (DCC) for 

a 21-day sampling period. 

 2000 20011

 2002 

Industrial Area 

West Long 29 

Cottle 100 17 

Bodfish 22 

Pingok 1 

East Spy 44 22 

Control Area 

Maguire 87 67 <1 

Flaxman 64 17 43 

1

Because of sampling gaps caused by 

equipment malfunction, we could not 

estimate probabilities for most of the 

Industrial Area in 2001. 

Table 4-3. Daily probability (%) of movement 

between the detection zone of one DCC and another 

in the Industrial Area 2002. 

To 

From West 

Long Cottle Bodfish Pingok East 

Spy 

West 

Long 94.71

 5.02 0.07 0 0 

Cottle 13.1 91.9 18.0 0 0 

Bodfish 10.7 18.3 93.0 1.9 0 

Pingok 7.7 0 0 77.8 0 

East 

Spy 0 0 0 0 93.0 

1

 Values along the diagonal are the daily probabilities 

of not permanently emigrating from a site. Because 

some ducks move to other sites and then return (i.e., 

non-permanent emigration) rows can sum to >100%. 

Estimates based on Cormak-Jolly-Seber model. 

2

Values off the diagonal were estimated with a multistate transition model. 
38

in the Industrial Area is likely associated with the more contiguous arrangement of 

islands (Figure 4-1). The islands that form Simpson Lagoon essentially create a 

uniform strip of habitat. Conversely, the Control Area represents a more fragmented 

habitat and movements between areas would require crossing a relatively large open 

channel. Apparently, flightless Long-tailed Ducks are reluctant to make movements 

across these large channels. Movements between DCC sites were most likely when 

distances were short. However, this may be an artifact of our sampling design. 

Because the islands essentially create a linear strip of habitat, our DCC sampling 

stations are arranged in a linear fashion. Thus, in order to move from one station to a 

more distant station, birds would likely pass though intermediate stations. The 

multistate analysis approach, assigns individuals to a ‘population or site’ as soon as 

they are detected at that site. Consequently, an individual moving from Bodfish to West 

Long would likely travel through the Cottle DCC area. If this individual were detected in 

the Cottle area its movement would contribute to both the transition probability from 

Bodfish to Cottle and from Cottle to West Long. Nonetheless, most individuals only 

moved to the next nearest sampling location. 

Our data demonstrate that even flightless Long-tailed Ducks have the ability to move 

considerable distances in response to environmental conditions. We found little 

evidence for displacement of individuals associated with disturbance. We suggest that 

patterns of site fidelity are predominantly influenced by weather conditions, particularly 

wind direction. The observations of birds feeding and roosting in consistent locations 

are supported by our data, however, a considerable proportion of individuals change 

locations during the flightless period in each year. This movement within years could be 

interpreted to suggest that foraging conditions are relatively uniformly distributed 

throughout the lagoon systems and site selection is based on other habitat 

characteristics. 
39

Chapter 5

Effect of Underwater Seismic Surveys on Molting Long-Tailed Ducks 

INTRODUCTION 

Environmental disturbances, such as those caused by mineral exploration and 

development, may compromise the ability of Long-tailed Ducks to access and fully use 

their molting habitats, and thus successfully complete their molt. An example of such a 

disturbance is near shore underwater seismic surveys that are commonly used to 

explore and map mineral resources located below the surface (Richardson et al. 1995). 

Open water marine seismic surveys use a process by which very intense sound pulses 

are emitted at regular intervals by an array of underwater airguns (Greene and 

Richardson 1988). The sound returning from the seabed is processed to locate 

geological formations that may contain producible quantities of hydrocarbons. In 

addition to the sound that travels downwards into the seabed, the sound from the 

airguns also travels sideways through the water, and this noise has been cited as 

causing environmental effects. The peak noise levels of the seismic blasting frequently 

exceed those of other industrial activities such as aircraft, ships, dredging, and 

construction (Richardson et al. 1995). These noise pulses have been found to cause 

general avoidance reaction, changes in behavior (e.g., dive cycles, respiration), and 

displacement of marine mammals such as the Common Dolphin (Delphinus delphis), 

Gray Whale (Eschrichtius robustus), Sperm Whale (Physeter macrocephalus), and 

Bowhead Whale (Balaena mysticetus) (Richardson et al. 1995, Goold 1996). 

Richardson (2001) indicated that some bowheads may remain as far as 24 km from ongoing seismic surveys. Even at 25 km, the sound pulses seem strong and obtrusive to 

humans listening via hydrophones and sonobuoys (Richardson et al. 1995). Airguns 

can be fatal to some species at very close range (Warren 1989). This noise may have 

the potential to disrupt the behavior of Long-tailed Ducks, particularly while diving to 

feed (Goudie and Ankney 1986). 

In August 2001, BP Exploration (Alaska) Inc. (BPXA) contracted Western Geophysical 

to conduct a 3-dimensional geophysical survey in the Simpson Lagoon of the Beaufort 

Sea (Richardson 2001). We used this opportunity to evaluate the effects of underwater 

seismic activity on molting Long-tailed Ducks. Aerial surveys were used to detect 

changes in the distribution and abundance of molting Long-tailed Ducks before, during, 

and after seismic activities in a “Seismic Area” and in the adjacent portion on the 

Industrial Area that had similar habitat features. We also compared changes in the 

abundance and feeding propensity of individual radio-equipped Long-tailed Ducks 

located in the Seismic, Industrial, and Control Areas through time. Radio-equipped 

ducks were monitored with automated Data Collection Computers (DCC) that were 

capable of detecting the presence and feeding behavior of ducks within 2.5 km distance 

of each system. We predicted that Long-tailed Ducks would leave the Seismic Area if 

the activity and associated noises disrupted their normal activities. We did not predict 

whether Long-tailed Ducks would feed more or less during seismic activities because 

both options were plausible. We believe that this is the first study to evaluate the effects 

of underwater seismic surveys on sea ducks. 
40

METHODS 

The study area was divided into three areas: 1) a Seismic Area located near Spy and 

Pingok Islands in the western portion of Simpson Lagoon, 2) the remainder of the 

Industrial Area near Bodfish and Cottle islands in the central portion of Simpson 

Lagoon, and 3) the Control Area located along the Maguire and Flaxman islands (Figure 

5-1). To clarify, the Seismic Area is a sub-sample of the Industrial Area (as referenced 

through this report). Because the magnitude of the human activity associated with the 

seismic survey exceeded the activity in the rest of the Industrial Area and the Control 

Area, we pooled these areas and treated them as controls to the Seismic Area. 

Seismic Survey Protocol 

From 4-26 August 2001, Western Geophysical conducted a 3-dimensional reflection 

survey for hydrocarbon deposits in the nearshore waters of the western portion of the 

Simpson Lagoon, approximately 48 km from Prudhoe Bay (Richardson 2001). Five 

vessels, ranging in length from 23-41 m, were used including two source vessels with 

airguns, two cable deployment vessels, and one multi-use boat. Seismic activities were 

Figure 5-1. Location of the Seismic, Industrial and Control Areas. The location and 2.5 km-radius 

detection area of nine data collection computer systems (dots with circles) and capture 

locations (denoted with stars) are presented. 
41

conducted systematically in discrete sections until the entire area was sampled. 

Activities included surveying each section to obtain water depth, laying receiving cables 

(2.54 cm in diameter) along uniformly spaced transect lines on the ocean floor, towing 

and discharging airgun arrays perpendicular to the cables, and retrieving cables from 

the ocean floor. Airguns were towed by one of two source vessels depending upon the 

bathymetry of the area. In shallow water (i.e. within the lagoon), a smaller vessel was 

used, which deployed two identical clusters of four sleeve-type airguns over the port 

and starboard sides of the vessel. Each airgun was 1.3 L in size for a total of 10.5 L. 

After initial calibration only 8 airguns were operated for a total of 5.2 L. The airguns 

released high pressure underwater air blasts every 8 to 10 sec. On the ocean side of 

the islands where the water was deeper, a larger vessel was used to tow 12 sleeve-type 

airguns of various individual volumes (1.3 to 2.5 L for a total of 19.8 L), which 

discharged every 12 to 24 sec. Clusters of geophones and hydrophones, which were 

attached to the receiving cables at 50 m intervals, registered the subsurface signals and 

sound vibrations and transmitted the information along the cable to the 

recording/telemetry vessel. 

The location of all vessels and the time of day when seismic activity occurred were 

recorded on digital hydrographic charts. We used these charts to classify daily 

disturbance at each DCC site. 

Aerial Surveys 

To examine changes in the abundance and distribution of ducks, aerial counts of Longtailed Ducks were conducted before, during and after seismic activity along the inside 

and outside edges of barrier islands of the Seismic and Industrial Areas (Thetis to 

Stump islands). No surveys were conducted in the Control Area. One “before” seismic 

survey occurred on 24 July 2001, two “during” seismic surveys were conducted on 6 

and 15 August 2001, and one “after” seismic survey took place on 7 September 2001. 

Thetis Island and the ocean side of most of the islands were not surveyed on 15 

August, and Thetis Island was not surveyed on 7 September 2001. To standardize 

surveys we excluded counts from Thetis Island and eliminated the incomplete survey 

conducted on 15 August 2001 for comparisons of the ocean and lagoon sides of the 

islands. Surveys were conducted in the mid-afternoon to evening (start times ranged 

from 12:45 to 20:50). Restricting surveys in this way maximized our ability to count 

ducks and likely reduced the amount of variation in survey counts (Johnson and Gazey 

1992; Fischer et al. 2002). 

Long-tailed Ducks were counted from a Cessna 185 airplane, 90 m above ground level 

at 140 km/hr. For consistency, a single observer counted all ducks within a 400 m strip 

between the plane and the barrier islands. The data was transcribed into a tape 

recorder and the locations of all ducks were marked on 1:50,000 maps of the Simpson 

Lagoon area by a separate observer. Additionally, the second observer photographed 

flocks when feasible. Later, estimates of flock size were compared to counts of ducks 

taken from enlarged photographs. We used a 34.7% correction factor for all counts. 
42

We also tallied the number of Long-tailed Ducks located on the lagoon and ocean side 

of each individual barrier island for each survey. 

Capture and Detection of Radio equipped Long-tailed Ducks 

We captured molting Long-tailed Ducks with corral traps set at roosting sites on the 

barrier islands. A sub-sample of males was equipped with 15 g subcutaneously 

anchored radio transmitters (see Chapter 4). Transmitters were programmed to emit 60 

pulses per minute. 

Data collection computers (DCCs) connected to fixed antennas were placed near each 

of the Long-tailed Duck capture sites to maximize detection of the radio marked birds. 

Five DCC towers were erected within the Seismic Area (three on barrier islands and two 

on the mainland) and two DCC towers were erected in both the Industrial Area and the 

Control Area (all on barrier islands, Figure 5-1). DCCs could detect ducks with 

transmitters within approximately 2.5 km of the towers. 

The DCCs recorded the presence and number of pulses of each radio transmitter for a 

45 sec period, before switching to the next transmitter frequency. In this way, radio 

transmitters were monitored 1-3 times per hour, depending upon the number of radioequipped birds located near a site. We also monitored a reference transmitter at each 

DCC tower that enabled us to determine if and when the tower was functioning. 

Discharged batteries and downloading of data resulted in days with no data or less than 

24 hours of data. To determine how the hours of operation per day affected the number 

of radio-equipped ducks detected by DCCs, we plotted the cumulative proportion of 

radios detected by hour for each DCC in each day. This analysis was limited to days 

when DCCs operated a full 24 hours and included only radio-equipped ducks originally 

captured near a given DCC. We then averaged these proportions across days and 

DCCs. We found that most radios ( x=84.6% ± 2.5 [SE]) were detected after six hours 

of DCC operation. Consequently, we eliminated 14 DCC days when less than 6 hours 

of data were available. The days removed from our analyses were evenly distributed 

throughout the study period. The remaining DCC data allowed us to determine the 

proportion of radios in a given DCC area that were present each day. The physical 

arrangement of the capture sites allowed us to compare the movement of ducks 

between the Seismic Area and the Industrial Area, and between the western (Maguire 

Island) and eastern portion (Flaxman Island) of the Control Area (Figure 5-1). 

We also investigated the feeding propensity of ducks by classifying them as feeding or 

not feeding depending upon the number of transmitter pulses recorded during each 45 

sec scan. Radio signals are attenuated by saltwater when Long-tailed Ducks dive. 

Thus, pulse rates below that expected within a 45 sec scan interval can be interpreted 

as coming from diving (i.e., feeding) birds. We considered radio frequencies with 40 

pulses or less to represent ducks feeding, frequencies with 41-50 pulses to be ducks 

present but not feeding, and frequencies with more than 50 pulses to be erroneous 
43

readings (i.e., caused by transmitter interference from other radio sources or when 

battery power was low). 

Statistical Analysis 

To examine changes in the proportion of ducks detected near DCCs within the Seismic, 

Industrial, and Control Areas, we first computed the proportion of ducks present on a 

given day at each DCC site (observations were summarized for each 24 hr period). We 

limited these calculations to ducks captured near a given DCC site. Because previous 

seismic studies indicated the explosion of airguns was the most disturbing to wildlife 

(Richardson et al. 1995, Goold 1996), we considered 6-26 August to be the “duringseismic” period. This period encompassed all dates when airguns were used 

somewhere in the Seismic Area. The days in which radio-transmitters were monitored 

prior to 6 August and after 26 August were considered the “pre-seismic” and “postseismic” periods, respectively. Although we recognize that each DCC had different 

patterns of seismic activity exposure, the statistical analyses required us to standardize 

the “during-seismic” period to investigate changes occurring through the season at each 

DCC site. This allowed DCC sites to be used as replicates and contrasts could be 

generated between DCCs located within and outside of Seismic Area. This 

standardization only applied to the five DCCs located in the Seismic Area, all of which 

were within 7 km of each other. Prior studies have showed that the noise from airgun 

explosions typically travels much farther than 7 km (especially in the water column). 

Finally, we used a modified BACI (before-after-control-impact, Green 1979, McDonald 

et al. 2000) repeated measures analysis of variance (SAS procedure PROC MIXED, 

SAS Institute 1996) to compare the difference in mean proportion of ducks in the 

Seismic Area with the Industrial and Control Areas, during the pre-seismic and duringseismic periods. This comparison was carried out as a single degree of freedom 

contrast (i.e., the BACI contrast) within the interaction effect of the repeated measures 

analysis of variance. Even though a few cells were missing data in the repeated 

measures analysis, the BACI contrast was always estimable. The null hypothesis of the 

BACI contrast was, 

H0: μpc - μdc - μps + μds = 0 

where μpc was the mean proportion of ducks in the pre-seismic period on the Industrial 

and Control Areas, μdc was the mean proportion of ducks in the during-seismic period 

on the Industrial and Control Areas, μps was the mean proportion of ducks in the preseismic period on the Seismic Area, and μds was the mean proportion of ducks in the 

during-seismic period on the Seismic Area. The alternative hypothesis was that the 

difference in proportions pre- and during-seismic on the Industrial and Control Areas 

(i.e., μpc - μdc) was not equal to the difference in proportions pre- and during-seismic in 

the Seismic Area (i.e., μps - μds). Prior to analyses, data were arcsin square root 

transformed (i.e., xi = ( ) arcsin pi where pi was the proportion of ducks in an area on 

day i) and plotted to ensure data met normality assumptions. We also accounted for 

autocorrelation in the daily transformed values by modeling the variance-covariance 

matrix with a power function that estimated correlations between observations from the 

same DCC. Modeling of the variance-covariance structure used the restricted 
44

maximum likelihood method (REML; PROC MIXED documentation, SAS Institute 1996). 

We assumed observations from different DCCs were independent. Finally, from the 

repeated measures analysis and estimated variance-covariance matrix, we generated 

estimates of the proportion of ducks detected for each treatment (Seismic Area and the 

pooled Industrial and Control Areas, and pre- and during-seismic periods). 

Unfortunately, comparisons to the post-seismic period proved impossible. Ducks were 

beginning to fly by the time seismic activities ended and several of our DCCs failed to 

work properly in the Industrial and Control Areas at this time. 

A similar analysis was conducted to investigate changes in the proportion of ducks 

feeding on a daily basis near each DCC within the Seismic Area and the Industrial and 

Control Areas. We developed an index to feeding activity that controlled for diurnal 

variation in feeding activity. We determined the proportion of observations indicative of 

feeding during each hour of each day for each DCC, and then averaged these 

proportions across a 24 hr period for each DCC. Thus, we removed any weighting 

based on sample size that would have influenced a daily index. Although it would have 

been preferable to statistically investigate feeding behavior during each hour of each 

day, this proved impossible because of compounded autocorrelation problems and 

large numbers of missing or sparsely populated cells. As above, data were arcsin 

square root transformed, autocorrelation was accounted for, and estimates of feeding 

proportions were determined. We used two-tailed tests and an alpha-level of 0.05 in all 

cases. 

RESULTS 

Aerial Surveys 

Long-tailed Ducks were observed near most of the islands during each of our surveys 

(Figure 5-2). Particularly large concentrations of ducks occurred near Thetis, Spy, 

Cottle, and West Long islands. Most ducks were observed on the lagoon side of the 

barrier islands, although large numbers of ducks occurred on the ocean side of Spy, 

Cottle, and West Long islands during one or more of the surveys. 

The total number of ducks detected (after standardizing data sets) during aerial surveys 

decreased from a high of 5499 ducks during the first survey to 1981 ducks during the 

second survey (Figure 5-3A). The number of ducks later increased to 3538 ducks 

during the last survey (Figure 5-3A). Although this pattern was consistent across 

islands within the seismic (Spy, Leavitt, West Pingok) and non-seismic areas (East 

Pingok to Stump islands), the magnitude of the change was greater in the Seismic Area 

(Figure 5-3A). When the aerial survey data was restricted to only the lagoon side of the 

barrier islands, a similar pattern was found (Figure 5-3B). The lowest number of ducks 

occurred during the period with seismic activity (gray shaded areas in Figure 5-3). 

Whereas the decline in duck numbers in the Seismic Area may be partially due to 

seismic activities, the concurrent decline in the Industrial Area suggests other factors 

were affecting duck numbers and distribution. 
45

Thetis Spy Leavitt West Pingok East Pingok Bert/Bodfish Cottle/W. Long East Long Egg/Stump

0

200

400

600

800

1000

1200

Pre-seismic Activity - July 24, 2001

During-seismic Activity - August 6, 2001

During-seismic Activity - August 15, 2001

Post-seismic Activity - September 7, 2001

Thetis Spy Leavitt West Pingok East Pingok Bert/Bodfish Cottle/W. Long East Long Egg/Stump

Number of Ducks Counted

0

200

400

600

800

1000

1200

Survey Location

Thetis Spy Leavitt West Pingok East Pingok Bert/Bodfish Cottle/W. Long East Long Egg/Stump

0

200

400

600

800

1000

1200

*

*

* * * * * *

Thetis Spy Leavitt West Pingok East Pingok Bert/Bodfish Cottle/W. Long East Long Egg/Stump

0

200

400

600

800

1000

1200

1600

1800

Figure 5-2. Number of Long-tailed Ducks counted from aircraft during the pre-seismic, during-seismic, and 

post-seismic periods along the lagoon and ocean sides of barrier islands in the Beaufort Sea, 

Alaska, in 2001. Stacked bars represent observer counts (bottom portion of column) and corrected 

counts (top portion, see text for methods), and diagonal and open bars represent lagoon and ocean 

counts, respectively. An “asterisks” (*) indicates where surveys were not conducted on a given 

island due to weather. Islands where seismic activity occurred nearby are shaded gray. 
46

Capture and Location of Radio equipped Long-tailed Ducks 

We trapped a total of 246 Long-tailed Ducks at five different sites; ducks were captured 

at one site within the Seismic Area and two sites within both the Industrial and Control 

Areas (Figure 5-1, Table 5-1). All ducks, except seven at Bertoncini, were caught prior 

to the start of seismic activities and most were males (94.7%, n=246). A total of 102 

male Long-tailed Ducks were equipped with radios throughout the five capture areas 

(Table 5-1). Ducks captured at Bertoncini and Cottle islands were combined to 

represent ducks from the Industrial Area because these capture locations were within 4 

km each other and only one duck was captured at Cottle Island. We did not combine 

information from the two capture areas in the Control Area because the distance 

between the two capture sites was large (> 12.7 km). 

Survey Date

24 July 6 August 15 August 7 September

Number of ducks

0

500

1000

1500

2000

2500

3000

Seismic Area

Industrial and Control Areas

A. Ocean and Lagoon Counts Combined

B. Lagoon Count Only

Survey Date

24 July 6 August 7 September

Number of ducks

0

500

1000

1500

2000

2500

3000

Figure 5-3. Number of Long-tailed Ducks counted (corrected values) on the (A) ocean and lagoon, 

and (B) lagoon only sides of the barrier islands where seismic activity occurred (i.e., Spy, 

Leavitt, and West Pingok) and did not occur (i.e., East Pingok, Bertonicini, Bodfish, Cottle, 

West and East Long, Egg and Stump islands). We excluded data from Thetis Island during 

all survey dates, and did not include data from the 15 August survey in panel A because 

information from the ocean side of the islands was not available. Dates with seismic activity 

are shaded gray. 
47

Table 5-1. Number of Long-tailed Ducks captured and radio-marked at five capture areas 

on the barrier islands of the Beaufort Sea, Alaska, during 2001. 

Seismic 

Area Industrial Area1

 Control Area 

Capture Area Leavitt 

Island 

Bertoncini 

Island 

Cottle 

Island 

Maguire 

Island 

Flaxman 

Island 

Number 

Captured 30 9 1 124 82 

Number (%) 

Males 29 (96.8) 9 (100) 1 (100) 124 (100) 70 (85.4) 

Number 

Radioed 29 9 1 31 32 

Capture Dates Jul 30 Jul 29; 

Aug 3,9 Aug 1 Jul 28 Jul 30 

1

Ducks captured at Bertoncini and Cottle islands were combined to represent ducks from the 

Industrial Area. 

The DCCs began monitoring radio transmitters at some sites by 31 July, and all sites 

were active by 4 August (gray areas within Figures 5-4 and 5-5). The first seismic boat 

moved west past Pingok Island on the 4 August and cables were laid on the same day 

near the eastern half of Spy Island (see seismic activity symbols, Figure 5-4). Airguns 

were fired for the first time on the 6 August. Within the Seismic Area, radio-transmitters 

were monitored for 5 days prior to the start of seismic activities, whereas in the 

Industrial and Control Areas, radio-transmitters were monitored for 2-6 days prior to the 

start of airgun activities in the Seismic Area. The during-seismic period lasted was 6-26 

August (i.e., 21 consecutive days). Post-seismic monitoring lasted 3-8 days depending 

upon the DCC. 

Each DCC collected data for 13 to 35 days ( x=26.1±8.3[SE]) for a total of 4869 hrs of 

detection time (Table 5-2). Of the initial sample of radio-equipped ducks, nine (8.8%) 

radios were not heard by any DCC after capture (all from the Control Area). The radio 

transmitters on these ducks may have failed or the ducks may have moved out of 

antenna range. The high number of fixes per radio at DCCs located at Pingok, East 

Spy, Bodfish, Maguire, and Flaxman islands suggested these sites were used heavily 

by our radio-equipped Long-tailed Ducks. In contrast, DCCs located at F Pad, Oliktok 

Point, West Spy Island, and Cottle Island had low fix averages, indicating the radioequipped ducks seldom used these areas (although a few ducks were heard frequently 

even at these sites, see “range of fixes/radio” Table 5-2). The total number of fixes at 

each site also indicated ducks used areas near particular DCCs more (Table 5-2). 
48

Table 5-2. Detection results for radio-equipped Long-tailed Ducks monitored by Data Collection Computers located on 

the barrier islands and the mainland in 2001. “Local” radios refer to those placed on ducks at the capture site 

adjacent to the DCC and “foreign” radios refer to those placed on ducks at capture sites away from the DCC. 

 Seismic Area Industrial Area Control Area 

Parameter F Pad Oliktok 

Point 

Pingok 

island 

East 

Spy 

Island 

West 

Spy 

Island 

Bodfish 

island 

Cottle 

Island 

Maguire 

Island 

Flaxman 

Island 

Sampling (days) 35 31 28 34 32 28 13 16 18 

Sampling (hours) 659.2 659.2 621.3 730.81 654.4 649.7 228.7 343.7 321.8 

No. of local 

radios monitored 29 29 29 29 29 10 10 32 31 

Total no. of 

radios monitored 39 39 39 39 39 39 39 63 63 

No. of local 

radios detected 6 3 28 28 15 10 5 27 27 

No. of foreign 

radios detected 0 0 0 2 0 8 9 0 9 

Fixes/radio 

(±SE) 

5.0 ± 

1.8 

3.3 ± 

1.3 

214.6 

± 48.2 

135.5 ± 

34.0 

25.7 ± 

6.2 

132.2 ± 

55.1 

25.8 ± 

11.0 

118.6 ± 

21.9 

144.1 ± 

20.8 

Range of 

fixes/radio 

1 – 

11 2 - 6 4 – 995 2-699 1-99 4 – 737 2 - 160 2-395 2-446 

Total fixes 30 10 6009 4066 386 2380 361 3202 5186 

Proportion of Ducks Detected and Seismic Activity Effects 

Figures 5-4 and 5-5 show the proportion of ducks detected during each day in the 

Seismic, Industrial, and Control Areas. Of the five DCCs located in the Seismic Area, 

the highest proportion of ducks were detected at Pingok, followed by East Spy, West 

Spy, F Pad and Oliktok (Figure 5-4). Only three and six radios were detected at the 

Oliktok and F Pad DCC sites, respectively, indicating few radio-equipped Long-tailed 

Ducks moved to the mainland from the barrier islands within the Seismic Area. These 

two DCCs were excluded from subsequent statistical analyses due to their low detection 

rate. In the Industrial Area, the Bodfish DCC detected most of the radios, while the 

Cottle DCC detected less than 40% of the ten radios placed on birds in the area. This is 

likely due to proximity of DCCs to capture areas in the Industrial Area. In the Control 

Area, generally over 40% of the radio-equipped ducks were detected each day, and the 

detection rate remained relatively constant through time. 
49

East Spy

Oliktok F Pad

Pingok

Legend

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

Proportions of radio-equipped ducks

0.0

0.2

0.4

0.6

0.8

1.0

Date (31 July - 3 September)

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

0.0

0.2

0.4

0.6

0.8

1.0

Date (31 July - 3 September)

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

Proportion of radio-equipped ducks

0.0

0.2

0.4

0.6

0.8

1.0

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

0.0

0.2

0.4

0.6

0.8

1.0

West Spy

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

Proportion of radio-equipped ducks

0.0

0.2

0.4

0.6

0.8

1.0

Seismic Area 

(N = 29; same ducks for all DCCs)

Boat Travel

Research Activities

Laying or Picking up Cables

Seismic Gunning

Figure 5-4. The proportion of radio-equipped Long-tailed Ducks detected by DCCs placed in the 

Seismic Area. The proportion of ducks included only those captured in the Seismic Area. 

The gray shading indicates when the DCC were recording data. The solid vertical lines 

represent the first and last days when seismic activity occurred in the general area. The 

five symbols represent the types of activities occurring within a 2.5 km radius of each DCC.
50

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

Proportion of radio-equipped ducks

0.0

0.2

0.4

0.6

0.8

1.0

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

0.0

0.2

0.4

0.6

0.8

1.0

Date (31 July - 3 September)

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

Proportion of radio-equipped ducks

0.0

0.2

0.4

0.6

0.8

1.0

Date (31 July - 3 September)

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

0.0

0.2

0.4

0.6

0.8

1.0

Bodfish Cottle

Maguire Flaxman

Industrial Area

 (N = 10; same ducks at Bodfish and Cottle DCCs)

Control Area 

(Maguire N = 32; Flaxman: N = 31)

Legend

 Research Activities

Figure 5-5. The proportion of radio-equipped Long-tailed Ducks detected by the four DCCs located 

in the Industrial and Control Areas. In the Industrial Area, the number of ducks detected 

included all those captured near both the Bodfish and Cottle island DCCs. In the Control 

Area, the number of ducks detected included only those captured near their corresponding 

DCC site (Table 5-1). The gray shading indicates when the DCCs were recording data. The 

solid vertical lines represent the first and last days when seismic activity occurred in the 

seismic area. The “●” symbol represents when researchers conducting this study were 

boating within a 2.5 km radius of each DCC. 
51

A repeated measures analysis of variance indicated there were no significant 

differences in the proportion of Long-tailed Ducks detected by DCCs located in the 

Seismic, and the Industrial and Control Areas (Seismic main effect, Table 5-3). There 

was a significant date effect, suggesting the proportion of ducks declined through the 

molting season (Date main effect, Table 5-3). However, the primary effect of interest 

was not significant (P = 0.94, BACI contrast, Table 5-3), indicating that the difference in 

proportion of ducks staying near each DCC for the pre- and during-seismic periods was 

nearly identical in the Industrial and Control Areas and the Seismic Area. 

Table 5-3. Repeated measures analysis of variance for proportion 

of Long-tailed Ducks detected by three and four data 

collection computers located in the Seismic Area, and the 

Industrial and Control Areas, respectively. 

Source Numerator DF Type III F Pr > F 

Seismic 1 3.07 0.140 

Date 25 1.92 0.017 

Seismic x Date 23 1.23 0.249 

 

Contrast 

 BACI-seismic x period 1 0.01 0.939 

Seismic Activity Effects on Feeding Activity 

Our index to feeding activity varied both among and within areas (Figures 5-6 and 5-7). 

Generally, 40% or more of our records indicated ‘feeding’ on any given day, although 

there was noticeable variation among DCC sites. DCCs located at East Spy, Pingok 

and Maguire had exceptionally high indices of feeding (60-80%), whereas the West Spy 

DCC was low (20%). Feeding indices were not calculated for the F Pad and Oliktok 

DCC sites because of the low number of ducks detected. Interestingly, some of the 

highest feeding indices were detected on days when airguns were fired (note especially 

East Spy and Pingok, Figure 5-6). 

The repeated measures analysis of variance for feeding also failed to find significant 

differences between the Seismic Area and the Industrial and Control Areas (Seismic 

main effect, Table 5-4), through the season (Date main effect, Table 5-4), or in the slope 

through time on the Seismic Area and the Industrial and Control Areas (Seismic x Date 

effect, Table 5-4). The BACI contrast was also not significant (P = 0.83, BACI contrast, 

Table 5-4), indicating that the difference in feeding indices for each DCC pre- and 

during-seismic was nearly identical in the Industrial and Control Areas and the Seismic 

Area. Estimates of feeding suggest that the highest indices were in the during-seismic 

period in the Industrial and Control Areas (mean and 95% C.I. across days = 0.50; 0.31 

– 0.70), followed by the pre-seismic period in the Industrial and Control Areas (0.46; 

0.17 – 0.76), followed by the during-seismic period in the Seismic Area (0.42; 0.22 – 

0.63), and finally, by the pre-seismic period in the Seismic Area (0.32; 0.09 – 0.61). 
52

Table 5-4. Repeated measures analysis of variance for proportion 

of Long-tailed Ducks feeding when detected by three and 

four data collection computers located in the Seismic Area 

and the Industrial and Control Areas, respectively. 

Source Numerator DF Type III F Pr > F 

Seismic 1 0.62 0.468 

Date 25 0.84 0.677 

Seismic x Date 23 1.25 0.233 

 

Contrast 

 BACI-seismic x period 1 0.05 0.829 

Date (July 31 - Sept 3)

Proportion of radio-equipped ducks feeding

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

0.0

0.2

0.4

0.6

0.8

1.0

Date (July 31 - Sept 3)

Proportion of radio-equipped ducks feeding

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

0.0

0.2

0.4

0.6

0.8

1.0

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

0.0

0.2

0.4

0.6

0.8

1.0

West Spy East Spy

Pingok

Seismic Area 

(N = 29; same ducks for all DCCs)

Legend

***** ** *

* No Ducks Detected

Boat Travel

Research Activities

Laying or Picking up Cables

Seismic Gunning

Figure 5-6. The proportion of radio-equipped Long-tailed Ducks detected feeding by DCCs on barrier 

islands in the Seismic Area. The proportion of ducks included only those captured in the 

Seismic Area. The gray shading indicates when the DCC were recording data. The solid 

vertical lines represent the first and last days when seismic activity occurred in the general 

area. The five symbols represent when and the types of activities occurring within a 2.5 km 

radius of each DCC. 
53

Movement of Ducks 

The East Spy, Bodfish, Cottle, and Flaxman DCCs detected 2-9 radio-equipped ducks 

captured at adjacent capture sites (Figure 5-8). In all but the East Spy DCC, this 

represented ducks moving east from their original capture site. This easterly movement 

of radio-equipped ducks occurred during the middle and end of seismic activities (Figure 

5-8). Easterly movement of this sort, if restricted to the Seismic Area, would support the 

hypothesis that ducks were moving away from seismic activities. However, this 

movement was observed in the Seismic, Industrial, and Control Areas. Further, we 

documented local ducks returning to their initial capture areas before seismic activities 

ended (e.g., West Spy, East Spy, and Pingok DCCs, Figure 5-8). 

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

0.0

0.2

0.4

0.6

0.8

1.0

Date (31 July - 3 September)

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

Proportion of radio-equipped ducks feeding

0.0

0.2

0.4

0.6

0.8

1.0

Date (31 July - 3 September)

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

0.0

0.2

0.4

0.6

0.8

1.0

Cottle

31 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 1 3

Proportion of radio-equipped ducks feeding 0.0

0.2

0.4

0.6

0.8

1.0

Bodfish

Maguire Flaxman

Industrial Area 

(N = 10; same ducks for Bodfish and Cottle)

Control Area 

( Maguire: N = 32; Flaxman: N = 31)

Legend

 Research Activities

Figure 5-7. The proportion of radio-equipped Long-tailed Ducks detected feeding by DCCs in 

the Industrial and Control Areas. In the Industrial Area, the number of ducks detected 

included all those captured near both the Bodfish and Cottle island DCC. In the Control 

Area, the number of ducks detected included those captured near the corresponding 

DCC (Table 5-1). The gray shading indicates when the DCCs were recording data. 

The solid vertical lines represent the first and last days when seismic activity occurred in

the seismic area. The “●” symbol represents when researchers conducting this study 

were boating within a 2.5 km radius around each of the DCC. 
54

Seismic Area

(N = 29; same ducks for all DCCs)

Legend

Ducks from capture area

Ducks from another capture area

Ducks return to original capture area

31 2 4 6 8 101214161820222426 2830 1 3

Wind Direction (degrees)

0

100

200

300

Number of Ducks

0

5

10

15

20

25

30

West Spy

Date (31 July - 3 September)

31 2 4 6 8 1012141618202224262830 1 3

Wind Direction (degrees)

0

100

200

300

0

5

10

15

20

25

30

East Spy

Number of Ducks

Pingok

Date (31 July - 3 September)

31 2 4 6 8 1012141618202224262830 1 3

Wind Direction (degrees)

0

100

200

300

Number of Ducks

0

5

10

15

20

25

30

31 2 4 6 8 1012141618202224262830 1 3

Wind Direction (degrees)

0

100

200

300

Number of Ducks

0

5

10

15

20

25

30

Bodfish Cottle

Flaxman Maguire Eastern Control Area

( Maguire: N = 32; Flaxman: N = 31)

Western Control Area

(N = 10 ducks; same ducks for Bodfish and Cottle)

31 2 4 6 8 1012141618202224262830 1 3

Wind Direction (degrees)

0

100

200

300

Number of Ducks

0

5

10

15

20

25

30

Date (31 July - 3 September)

31 2 4 6 8 1012141618202224262830 1 3

Wind Direction (degrees)

0

100

200

300

Number of Ducks

0

5

10

15

20

25

30

Date (31 July - 3 September)

31 2 4 6 8 1012141618202224262830 1 3

Wind Direction (degrees)

0

100

200

300

Number of Ducks

0

5

10

15

20

25

30

Legend

Ducks from the capture area

Ducks from another capture area

Ducks return to original capture area

Figure 5-8. The number of radio-equipped Long-tailed Ducks detected by DCCs on the barrier islands in the 

Seisimic, Industrial, and Control Areas. Black bars represent detections of ducks originally equipped 

with radio transmitters near the DCCs, light gray bars represent those ducks detected that were 

captured away from the DCCs, and dark gray bars represents ducks that returned after having departed 

the area. The gray background shading indicates when the DCCs were recording data. The solid 

vertical lines represent the first and last days when seismic activity occurred in the seismic area. Wind 

direction is represented in a line and scatter plot.
55

DISCUSSION 

Both aerial surveys and DCC data show declines in duck numbers in the Seismic Area 

following the start of seismic activity. However, we detected no concurrent decline in 

feeding behavior during the seismic period. In fact, high feeding indices were recorded 

from DCCs on days when seismic activity was near by. An analysis based on a 

modified Before-After Control-Impact approach (McDonald et al. 2000) found that the 

difference in proportion of ducks staying near each DCC in the pre- and during-seismic 

periods was nearly identical in the Industrial and Control Areas and the Seismic Area. 

Unfortunately, we could not conduct a similar analysis on aerial survey counts and interlagoon movements because replicate data were not available. Nonetheless, changes in 

aerial survey counts and lagoon movements exhibit similar patterns in the Seismic Area 

and the Industrial and Control Areas. 

Although evidence from this study suggests that underwater seismic activity did not 

cause Long-tailed Ducks to leave the Seismic Area, these results should be viewed 

cautiously given several statistical and ecological factors that may have reduced our 

ability to detect changes. First, our study may have lacked statistical power to detect 

differences between the Seismic Area and the Industrial and Control Areas. We would 

need additional seismic areas and associated controls to compensate for site variation. 

Unfortunately, the cost of conducting underwater seismic surveys and monitoring radioequipped ducks prohibits such a design. Second, our statistical analysis suffered from 

having treatment periods of unequal duration. The pre-seismic period was rather short 

and the during-seismic period was relatively long. The long during-seismic activity 

period precluded comparisons to the post-seismic period because many ducks were 

completing their molt and beginning to fly. Other researchers have reported Long-tailed 

Ducks flying in large numbers during late August and leaving the lagoons for the ocean 

environment (Bartels et al. 1983, Brackney et al. 1985, Johnson and Richardson 1982, 

Fischer et al. 2002). Third, the parameters measured during our study were rather 

coarse and unlikely to detect subtle changes in duck distribution. DCCs, while enabling 

us to collect data continuously over a very large area, are only capable of recording the 

presence of ducks within a 2.5 km radius. Short distance movements by ducks, as 

would occur in response to a passing vessel, would not be detected. Further, we did 

not directly observe the behavior of Long-tailed Ducks in or outside of the Seismic Area. 

Johnson (1982) documented numerous occasions of Long-tailed Ducks moving from 

one habitat to another in response to aircraft, boat, and human disturbances. We know 

from direct observations of Long-tailed Ducks near our research vessels that they 

frequently dive and swim short distances away. However, we typically travel through an 

area quickly, allowing them to resurface and return to their previous location. How 

Long-tailed Ducks respond to slow moving boats and loud airgun explosions is 

unknown. 

There are other factors that may have confounded our comparison among areas all of 

which contribute to the stochastic nature of duck distribution and numbers. For 

example, consistent strong southwesterly winds influenced duck distributions in our 
56

study and likely caused some birds to move eastward (out of the Seismic Area). 

Indeed, Dygas (1975) documented a significant correlation (r = 0.73) between wind 

speed and current velocity in the Simpson Lagoon, and suggested that waves are 

generated by local winds. By coincidence, strong (averaging 4-9 m/sec) southwesterly 

winds were present during much of the pre- and during-seismic periods of our study. 

These winds corresponded with a decrease in duck numbers. Islands located in the 

Seismic Area are especially exposed to southwesterly winds (Figure 5-1), thus ducks in 

the area might seek better protected habitats. An abrupt shift in wind direction (i.e., 

easterly winds) occurred during the 20-30 August also coincided with duck numbers 

either stabilizing or increasing in the Seismic Area (e.g. East and West Spy, and Pingok, 

Figures 5-2 and 5-3). 

Additional factors that may affect duck abundance and distribution, include seasonal 

shifts in prey distribution and abundance, lagoon orientation and configuration, bottom 

configuration, and other weather related phenomenon (Griffiths and Dillinger 1981, 

Bartels and Doyle 1983, Noel et al. 2001, Fischer et al. 2002). Despite the potential for 

these factors to affect duck distribution and numbers, other studies have shown that 

radio-equipped Long-tailed Ducks typically remained within the same lagoon system 

throughout the molt period (Bartels et al. 1983, Brackney et al. 1985). 

How seismic activities affected Long-tailed Duck feeding is less clear. The higher 

feeding rates, although not significant, observed on days when seismic activities were 

occurring suggests ducks may need to feed more or have more difficulty obtaining prey 

during these days. Given that Long-tailed Ducks are heavily dependent on exacting 

exogenous food resources to successfully complete molt, additional behavioral 

observations documenting this relationship are needed. 

Effects of Seismic Activities on Other Animals 

Whereas extensive field studies on the effects of seismic activity on marine mammals 

have been conducted, very little information on birds is available. Stemp (1985) failed 

to document any effect of seismic activities on seabirds, although these data were 

confounded with seasonal changes in bird numbers related to migration. Stemp (1985) 

insisted that these results not be extrapolated to areas with large concentrations of 

feeding or migrating birds, or birds that were molting. Most studies of marine mammals 

have documented general avoidance or behavioral changes, although the overall 

reaction depends on the species, the strength of the seismic pulses, and whether 

animals are attracted to an area for feeding or reproduction (Richardson et al. 1986, 

Richardson et al. 1995, Goold 1996, Richardson 2001). Given the variable reaction of 

mammals to seismic activity, it is clear that additional studies on other species of birds 

are needed to fully understand the effects of underwater seismic testing. Accordingly, 

we suggest several practical guidelines be implemented when conducting seismic 

surveys in the offshore lagoons. First, we suggest seismic activities be conducted 

during the winter. If summer surveys are unavoidable, then they should be scheduled 

around the period of peak concentration of flightless Long-tailed Ducks (late July 

through mid August). If seismic activity must be conducted, studies should avoid 
57

lagoons or shorelines where previous studies have documented large numbers of Longtailed Duck. These precautions seem especially prudent given the 67% decline in the 

species’ population size documented between 1977 and 1998 (Wilbor 1999). 
58

Chapter 6 

Habitat Use and Foraging Patterns of Long-tailed Ducks 

INTRODUCTION 

One set of possible effects from industrial development on Long-tailed Ducks could be 

changes in daily activity patterns, including foraging and habitat use. Changes in 

foraging patterns could be of particular importance because these Long-tailed Ducks 

meet the nutritional needs by foraging during the molt period (see Chapter 1). 

Decreased feeding, increased movement, and use of less preferred habitat could lead 

to diminished body condition. This, in turn, could lead to increased susceptibility to 

diseases (see chapter 7) or extreme weather. 

The problem of human disturbance on molting Long-tailed Ducks has been studied 

previously. In particular, the effects of helicopter over-flights at Thetis Island (Johnson 

1984) and Herschel Island, Yukon Territory (Ward and Sharp 1974). Johnson found no 

effect on numbers and distribution of birds. Ward and Sharp (1974) saw only brief 

displacements of ducks immediately following low-level (100m) over-flights. No studies 

have looked more broadly at possible effects of proximity to oilfield development on 

molting Long-tailed Ducks. 

Previous attempts to characterize patterns of foraging and habitat use by molting Longtailed Ducks in the Beaufort Sea have relied on aerial surveys (Johnson and Richardson 

1980, Johnson and Gazey 1992), observations from islands (Johnson 1982, Ward and 

Sharp 1974), and aerial telemetry (Brackney et al 1985). All of these studies conclude 

that molting ducks concentrate in the waters near the barrier islands and that there is a 

diurnal pattern of habitat use in which ducks approach the islands at night and move 

offshore into the lagoons during the day. However, these approaches have limitations: 

1) aerial surveys and telemetry fail to provide data on diurnal movement and feeding 

patterns and are frequently limited by poor weather conditions, 2) island based 

observers can only classify birds within view, ca. 150m (less during periods of fog), and 

3) all of these approaches characterize flocks instead of component individuals. To 

overcome these limitations, in 2000 and 2001 we conducted an intensive study of 

foraging and habitat use patterns of molting Long-tailed Ducks using radio triangulation. 

METHODS 

In 2000 and 2001 we erected rotating antenna towers near capture locations at Bodfish, 

Maguire, and Flaxman Islands (Figure 6-1). We followed the same radio-equipped 

ducks described in Chapters 4 and 5. Observers monitored each transmitter 

simultaneously from two or three antenna towers in each area. We used a two-tower 

approach in 2000 and a three-tower approach in 2001. Once per hour, observers 

listened for transmitters sequentially until all radios in the area were monitored. For 

each transmitter, observers recorded two bearings that bracketed the strongest signal, 

thus incorporating a margin of error around the duck’s true location. In addition to 

directional data, observers recorded foraging activity based on the pattern of radio 
59

signals. Long-tailed Ducks feed by diving below the surface of the water where their 

transmitter signals attenuate. Consequently, signals from foraging ducks are marked by 

10-30 second breaks in transmission, whereas signals from roosting ducks are 

continuous. At least once per day observers monitored the frequencies of birds 

captured in neighboring areas to investigate larger scale movements of ducks. When 

new birds were detected, they were added to the list for hourly monitoring. 

When a duck was detected from two or three towers in the same hour, the individual’s 

location (X, Y position in UTM coordinates) was calculated by triangulation (White and 

Garrott 1990). We used two different algorithms to calculate locations; one based on 

bearings from only two towers and the other based on bearings from three towers. Both 

algorithms calculate the coordinates and the area of the associated standard error 

polygon or ellipse. For locations determined from three bearings, we considered those 

with error polygons larger than the 95th percentile (0.71km2

) to be outliers. Locations 

determined from two bearings are inherently less precise, thus we set the maximum 

acceptable error polygon size for two bearing calculations at 1.0 km2

. Although these 

error polygon cutoff sizes seem large, we believe our locations are sufficiently precise 

for our analyses. First, the median error polygon size for all locations is only 0.11 km2

. 

Second, within an error polygon the location with the highest probability of being correct 

is the calculated location. Third, error polygon size increases with distance from 

antennas, however our greatest need for precision was for birds in nearshore habitats. 

Thus the proximity of receivers to the nearshore habitats reduced the likelihood of 

Figure 6-1. Locations of triangulation towers Control and Industrial Areas, 2000-2001. 
60

misclassification. Fourth, error polygons shapes are elongated toward receivers when 

the calculated location is between the receivers. This elongation parallels the islandlagoon habitat boundary, thus imprecise calculated locations are likely to be correctly 

categorized. And fifth, inaccurate habitat assignments have equal probability in all 

habitat types, thus the few inaccurately assigned locations should be unbiased. 

In ArcView, we overlaid the calculated Long-tailed Duck locations on a GIS coverage of 

the coastline and lagoon system. The locations were then subdivided into the following 

habitat classes: Island (within 300m), Mainland (within 300m), Lagoon (greater than 

300 m from Island and Mainland), and Ocean (greater than 300m from Island). We 

examined diurnal and seasonal patterns of foraging and habitat use patterns at each 

capture site. In 2000, our triangulation study was terminated early by a major storm on 

10 August. 

For additional insight into the foraging patterns of molting Long-tailed Ducks we used 

data gathered from Data Collection Computers (DCCs) placed throughout the lagoon 

system in 2000-2002. See Chapters 5 and 6 for details of their setup and use. 

RESULTS 

We recorded locations for Long-tailed Ducks during 1-13 August in 2000 and 1-23 

August in 2001. A total of 3316 and 4782 duck locations were recorded in 2000 and 

2001, respectively. Table 6-1 summarizes our sampling regime. The majority of 

locations fell within the Lagoon and Island Habitats. At Bodfish, only 1.5% of locations 

were within the Mainland Habitat. By contrast, 11.6% of Maguire and 13.8% of Flaxman 

locations were in the Mainland Habitat. Use of the Mainland Habitat did not differ 

between Control sites (2000: X2

=0.47 df=1 P=0.49; 2001 X2=1.35 df=1 P=0.25), but it 

was significantly different between the Industrial and Control Areas (2000: X2

=17.83 

df=1 P<0.0001; 2001: X2

=123.34 df=1 P<0.0001). 

A diurnal foraging pattern is evident in all areas in both years. Ducks tend to roost at 

night and forage during the day (Figure 6-2). Although this pattern is consistently 

evident, there is a great deal of variation in the exact proportions during each time 

period, thus Chi-square analyses yielded no significant relationships. Interestingly, this 

pattern exists even though there is no nighttime dark period until mid-August. 
61

A correlated diurnal pattern of habitat use is also evident, though less clearly (Figure 6-

3). By and large, a greater proportion of ducks were located in the Lagoon Habitat 

during the day and a greater proportion were near shore (esp., the islands) at night. 

This supports observations that ducks tend to forage in the lagoons during the day and 

roost near shore (esp., islands) at night. The one noteworthy exception was at Flaxman 

in 2001, where the proportion of locations in each habitat varied little through the day. 

To examine foraging and habitat use patterns through the course of the molt period, we 

divided our data into three week-long sampling periods. As these ducks had little flight 

feather re-growth when marked (9th primary: x=23.6mm±1.3 [SE]), the sampling 

periods represent early, mid, and late molt stage. We suspect that some of our birds 

attained flight during the third week. Unfortunately for these analyses, a major storm on 

10 August cut our sampling short in 2000. We were only able to sample for one day at 

one location (13 August, Flaxman) after the storm. In 2001, we were able to sample 

through the three sampling periods. 

In all areas except Flaxman in 2001, the proportion of foraging birds increased through 

the study periods (Figure 6-4). At Flaxman in 2001 the proportion of foraging ducks 

decreased in the third week, however the majority of locations from this period were 

recorded between 2100-0800 (72.4%). It is likely that newly flighted ducks were leaving 

the area during the day and returning at night to roost in the protected lagoons. 

Foraging data from DCCs show a similar tendency for increased foraging through the 

molt period (Figure 6-5). In contrast to foraging patterns, there is no clear pattern of 

habitat use through the molt period (Figure 6-6). 

Table 6-1. Sampling effort and results of radio-equipped Long-tailed Ducks monitored by 

triangulation in the Control and Industrial Areas, 2000-2001 

Industrial Area Control Area 

Bodfish Maguire Flaxman 

2000 Sampling Dates (range) 1-10 August 1-8 August 2-13 August 

Sampling (days) 10 7 10 

Sampling (hours) 129 47 91 

Number of Radios 18 25 25 

Total Fixes 872 492 933 

2001 Sampling Dates (range) 10-23 August 6-18 August 1-23 August 

 Sampling (days) 14 10 15 

 Sampling (hours) 168 87 162 

 Number of Radios 39* 31 32 

 Total Locations 717 1106 2959 

* 10 ducks captured near Bodfish were monitored 10-23 August. Additionally, 29 ducks 

captured near Leavitt were monitored 16-23 August 
62

Bodfish 2001

0100-0400

0500-0800

0900-1200 1300-1600 1700-2000

2100-0000

0

20

40

60

80

100

Maguire 2001

0100-0400

0500-0800

0900-1200 1300-1600 1700-2000

2100-0000

0

20

40

60

80

100

Flaxman 2001

Time

0100-0400

0500-0800

0900-1200 1300-1600 1700-2000

2100-0000

0

20

40

60

80

100

Bodfish 2000

0100-0400

0500-0800

0900-1200 1300-1600 1700-2000

2100-0000

Percentage of Locations

0

20

40

60

80

100

Maguire 2000

0100-0400

0500-0800

0900-1200 1300-1600 1700-2000

2100-0000

Percentage of Locations

0

20

40

60

80

100

Flaxman 2000

Time

0100-0400

0500-0800

0900-1200 1300-1600 1700-2000

2100-0000

Percentage of Locations

0

20

40

60

80

100

Roost

Forage

Figure 6-2. Diurnal foraging activity pattern of Long-tailed Ducks determined by radio triangulation 

in 2000-2001. 
63

Bodfish 2001

0100-0400

0500-0800

0900-1200

1300-1600

1700-2000

2100-0000

0

20

40

60

80

100

Mainland

Island

Lagoon

Ocean

Maguire 2001

0100-0400

0500-0800

0900-1200

1300-1600

1700-2000

2100-0000

0

20

40

60

80

100

Flaxman 2001

Time 0100-0400

0500-0800

0900-1200

1300-1600

1700-2000

2100-0000

0

20

40

60

80

100

Bodfish 2000

0100-0400

0500-0800

0900-1200

1300-1600

1700-2000

2100-0000

Percentage of Locations

0

20

40

60

80

100

Maguire 2000

0100-0400

0500-0800

0900-1200

1300-1600

1700-2000

2100-0000

Percentage of Locations

0

20

40

60

80

100

Flaxman 2000

Time 0100-0400

0500-0800

0900-1200

1300-1600

1700-2000

2100-0000

Percentage of Locations

0

20

40

60

80

100

Figure 6-3. Diurnal habitat use patterns of Long-tailed Ducks determined by radio triangulation in 

2000-2001. 
64

Bodfish 2000

Week 1 Week 2 Post-Storm

Percentage of Locations

0

20

40

60

80

100

Roost

Forage

Maguire 2000

Week 1 Week 2 Post-Storm

Percentage of Locations

0

20

40

60

80

100

Flaxman 2000

Week 1 Week 2 Post-Storm

Percentage of Locations

0

20

40

60

80

100

Bodfish 2001

Week 1 Week 2 Week 3

0

20

40

60

80

100

Maguire 2001

Week 1 Week 2 Week 3

0

20

40

60

80

100

Flaxman 2001

Week 1 Week 2 Week 3

0

20

40

60

80

100

Figure 6-4. Pattern of foraging activity by Long-tailed Ducks through the molt period determined by radio 

triangulation. 
65

Cottle 2000

Week 1 Week 2 Post-storm

Percentag of Locations

0

20

40

60

80

100

Maguire 2000

Week 1 Week 2 Post-storm

Percentage of Locations

0

20

40

60

80

100

Roost

Forage

Flaxman 2000

Week 1 Week 2 Post-storm

Percentage of Locations

0

20

40

60

80

100

Figure 6-5a. Pattern of foraging activity of Long-tailed Ducks determined by Data Collection 

Computers in 2000. 
66

East Spy 2001

Week 1 Week 2 Week 3

Percentage of Locations

0

20

40

60

80

100

Pingok 2001

Week 1 Week 2 Week 3

0

20

40

60

80

100

Bodfish 2001

Week 1 Week 2 Week 3

Percentage of Locations

0

20

40

60

80

100

Cottle 2001

Week 1 Week 2 Week 3

0

20

40

60

80

100

Roost

Forage

Maguire 2001

Week 1 Week 2 Week 3

Percentage of Locations

0

20

40

60

80

100

Flaxman 2001

Week 1 Week 2 Week 3

0

20

40

60

80

100

Figure 6-5b. Pattern of foraging activity of Long-tailed Ducks determined by Data Collection 

Computers in 2001. 
67

East Spy 2002

Week 1 Week 2 Week 3

Percentage of Locations

0

20

40

60

80

100

Pingok 2002

Week 1 Week 2 Week 3

0

20

40

60

80

100

Bodfish 2002

Week 1 Week 2 Week 3

Percentage of Locations

0

20

40

60

80

100

Cottle 2002

Week 1 Week 2 Week 3

0

20

40

60

80

100

Roost

Forage

Maguire 2002

Week 1 Week 2 Week 3

Percentage of Locations

0

20

40

60

80

100

Flaxman 2002

Week 1 Week 2 Week 3

0

20

40

60

80

100

Figure 6-5c. Pattern of foraging activity of Long-tailed Ducks determined by Data Collection 

Computers in 2002. 
68

Bodfish 2000

Week 1 Week 2 Post-Storm

Percentage of Locations

0

20

40

60

80

100

Mainland

Island

Lagoon

Ocean

Maguire 2000

Week 1 Week 2 Post-Storm

Percentage of Locations

0

20

40

60

80

100

Flaxman 2000

Week 1 Week 2 Post-Storm

Percentage of Locations

0

20

40

60

80

100

Bodfish 2001

Week 1 Week 2 Week 3

0

20

40

60

80

100

Maguire 2001

Week 1 Week 2 Week 3

0

20

40

60

80

100

Flaxman 2001

Week 1 Week 2 Week 3

0

20

40

60

80

100

Figure 6-6. Pattern of habitat use by Long-tailed Ducks through the molt period determined by radio 

triangulation. 
69

DISCUSSION 

In order to meet the nutritional demands of molt, the Long-tailed Ducks in our study area 

must forage during the molt period (see Chapter 1), and we see that foraging accounts 

for 14-50% of weekly locations by area and year from triangulation data. In the DCC 

data foraging accounts for 8-81% by area and year. In general, the proportion of 

foraging ducks as determined by DCCs is lower than from triangulation. This is 

expected because the detection range of DCCs is only 2.5 km, and it is likely that many 

ducks foraging in the Lagoon Habitat were beyond the DCC detection range. Our 

results show substantial variation in the schedule and percentage of time spent 

foraging. These differences among areas and years may reflect variation in foraging 

success (i.e., successfully capturing and eating prey) and determinants of success such 

as prey abundance. Unfortunately, our radio telemetry techniques do not allow us to 

assess this important component in foraging rates. 

Both triangulation and DCC data indicate that foraging rate increased through the molt 

period. This may be related body composition dynamics and preparation for flight. As 

seen in Chapters 1 and 2, wing loading is decreased and flight attainment period is 

shortened by the loss of mass through the molt period. Long-tailed Ducks may follow a 

strategy of foraging less early in the molt period to lose weight and then increasing 

foraging and mass as flight is attained. 

Although habitat use patterns were highly variable, there was a tendency for ducks to 

use nearshore habitats more at night and the Lagoon Habitat more during the day. This 

correlates with our foraging pattern results and is consistent with other studies (Ward 

and Sharp 1974, Johnson 1982) that found a tendency for Long-tailed Ducks to use 

nearshore habitats for roosting at night and the Lagoon Habitat for feeding during the 

day. The notable exception to this (Flaxman Island in 2001) suggests that other 

environmental conditions (possibly wind protection and prey distribution) strongly 

influence habitat use. 

If proximity to industrial development affects foraging or habitat use, we should see 

parallel patterns at the Maguire and Flaxman Areas (Control Areas) that contrast with 

the Bodfish Area (Industrial Area). In these data, only Mainland Habitat use (Figure 6-6) 

follows this pattern. That is, in both years, we recorded significantly more locations in 

the Mainland Habitat in the Control Area. This pattern has been documented in aerial 

surveys (Fischer et al 2002, Noel et al 2002). This difference, however, is likely due to 

the abundance of protective sand spits along the mainland in both the Maguire and 

Flaxman Areas and the dearth of sand spits along the mainland of the Industrial Area. 

In sum, our results do not show an effect on Long-tailed Ducks of proximity to industrial 

development. This parallels previous studies in which effects of human disturbance on 

molting Long-tailed Ducks have not been found (Johnson 1984, Ward and Sharp 1974). 

While interpreting these findings, however, one should consider that during this study 

the amount of direct disturbance by industrial activity (e.g., boat and aircraft traffic, 

construction, or oil spills and response) was low and did not differ among the three 
70

areas. Future coastal development projects could have a greater impact on Long-tailed 

Ducks, especially in the Control Area where Long-tailed Ducks make substantial use of 

the Mainland Habitat. 
71

Chapter 7 

An Adenovirus Linked to Mortality and Disease in Long-tailed Ducks 

INTRODUCTION 

Apart from renal coccidia, identified in a male Long-tailed Duck found dead near 

Teshukpuk Lake on the North Slope in the 1970’s (Franson and Derksen 1981), little is 

known about infectious and parasitic diseases in Long-tailed Ducks in Alaska. 

Elsewhere in North America, Long-tailed Duck populations suffer from mass mortalities 

caused by diseases. Avian cholera outbreaks have killed significant numbers of Longtailed Ducks on at least two occasions in Chesapeake Bay (Locke et al. 1970, Mashima 

et al. 1998, Montgomery et al. 1979) and in 2002, several thousand Long-tailed Duck 

carcasses were collected during a mortality event in the Great Lakes (primarily Lake 

Erie), which was attributed to avian botulism type E (National Wildlife Health Center, 

Madison, WI, unpubl. data). Additionally, cestodes have been identified as intestinal 

parasites (Schiller 1955). Viral diseases have not been reported as a cause of mortality 

in Long-tailed Ducks, although viruses have been linked to die-offs in other species of 

waterfowl, including sea ducks (Converse and Kidd 2001, Hollmén et al. 2002). Viral 

diseases could be particularly significant when birds congregate in dense flocks during 

molt, and bird-to-bird contacts allow efficient transmission and perpetuation of an 

outbreak. 

Evaluation of the effects of infectious diseases on populations of wild birds poses 

difficult challenges. However, recent studies suggest that diseases have significant 

impacts on species already in decline and, furthermore, that avian populations 

previously thought to be healthy and stable should be monitored for potential effects of 

new and rapidly spreading diseases (Friend et al. 2001). Because factors affecting 

survival of Long-tailed Ducks and the causes of recent population declines among sea 

ducks in Alaska are not well understood, our objectives were to evaluate Long-tailed 

Ducks for presence of viruses and viral diseases during molt in the Beaufort Sea. We 

evaluated viruses as a cause of death in Long-tailed Duck carcasses recovered during 

a mortality event in 2000, characterized the pathogenicity of a virus isolated from these 

carcasses in captive Long-tailed Ducks, and compared virus prevalences between livetrapped Long-tailed Ducks at the mortality site and a reference area, where no mortality 

was observed. 

METHODS 

Field studies 

We collected blood and cloacal samples from Long-tailed Ducks caught during the livecapture drives described in Chapter 4. In 2000, samples were collected from 40 birds in 

the Control Area and from 30 birds in the Industrial Area. In 2001, samples were 

collected from 30 and 20 birds at the Control and Industrial Areas, respectively. Cloacal 

samples were collected with Dacron-tipped swabs, transferred into virus transport 

media (Hanks’ balanced salt solution with 0.5% gelatin and 1500 international units [i.e., 

IU] penicillin, 1500 μg streptomycin, 100 μg gentamicin, and 100 IU mycostatin per ml), 
72

and stored in the gaseous phase of a liquid nitrogen dry shipper (Chart, Inc., New 

Prague, MN). Blood samples were collected by jugular venipuncture and transferred 

into Vacuette® (Greiner Meditech, Inc., Bel Air, MD) tubes without anticoagulant. The 

blood samples were allowed to clot for approximately 2 hrs in a cooler with ice packs 

and centrifuged at 1500 × g for 10 min. Serum was harvested, and stored in the dry 

shipper in the field and at -80°C until analyzed. 

Field work on other aspects of Long-tailed Duck molting ecology continued through 

August in 2000 and 2001. In August of 2000, Glaucous Gulls (Larus hyperboreus) were 

observed feeding on Long-tailed Duck carcasses floating on the water surface in the 

near-shore lagoons of the Control Area. A total of 12 duck carcasses were found by 

mid-August, however, we were rarely in a position to observe scavenging or discover 

carcasses. Two intact carcasses were frozen at -20°C in the field and shipped to the 

National Wildlife Health Center (NWHC) (Madison, Wisconsin, USA). 

Diagnostic laboratory analyses 

The two carcasses were examined at necropsy and samples of liver, spleen, lung, 

kidney, small intestine, and cloaca were collected for virus isolation. Moderate autolysis 

prevented histopathologic evaluation of tissues. Samples of liver and intestine were 

submitted to the microbiology laboratory of the NWHC for bacteriology and samples of 

liver were submitted to the chemistry laboratory of the NWHC for heavy metal analyses. 

Primary cultures of Muscovy Duck (Cairina moschata) embryo fibroblasts (MDEF) were 

used for virus isolation (Docherty and Slota 1988). Approximately 1 g of each tissue 

sample was homogenized in virus medium, and the homogenates were centrifuged at 

800 × g for 30 min at 4°C. The cloacal swabs were mixed by vortexing, and centrifuged 

at 800 × g for 15 min. Supernatants of each sample were inoculated separately into 

MDEF monolayers and incubated at 37°C in 5% CO2. The cell cultures were examined 

every other day for 7 days for viral cytopathic effects (CPE). When no CPE was 

observed, samples were freeze-thawed, blind passaged to fresh cell cultures, and 

monitored for an additional 7 days. 

Virus identification 

The nucleic acid type of the isolated viruses was determined by evaluating their 

infectivity to MDEF cells after 5-iodo-2’-deoxyuridine treatment. The presence or 

absence of lipoprotein envelope was evaluated with a chloroform lability assay 

(Feldman and Wang 1961). Positive cells cultures were prepared for electron 

microscopy by slow centrifugation at 800 × g for 30 min, and by ultracentrifugation of the 

supernatant at 35,000 × g for 150 min. The viral pellets were resuspended in distilled 

water, placed on grids, negatively stained with 0.5% phosphotungstic acid, and 

examined with a Hitachi H-500 transmission electron microscope (Hitachi High 

Technologies, Tokyo, Japan). Representative isolates were tested against avian 

adenovirus I (serotypes 1, 3, and 5) (Spafas® Inc., Preston, CT), avian adenovirus II 

(hemorrhagic enteritis virus) (Spafas® Inc.), and avian adenovirus III (egg drop 

syndrome virus, duck adenovirus serotype 1) (National Veterinary Services Laboratory, 
73

Ames, IA) antiserum using a standard virus neutralization assay (Thayer and Beard 

1998). 

Serology 

Serum samples were heat-inactivated at 56°C for 30 min and serial two-fold dilutions 

were tested with a standard virus neutralization assay (Thayer and Beard 1998) for 

antibodies against a representative isolate from the Long-tailed Ducks. 

Experimental studies. 

Three Long-tailed Ducks (Dry Creek Waterfowl, Port Angeles, WA) were received at the 

NWHC on September 20, 2001 for a pilot virus infectivity trial. Ducks were housed in a 

biosafety level III room (5.7 m x 3.2 m) equipped with a pond liner and filled with water 

to a depth of 20 cm (experimental room). Water was exchanged in the pool at a rate of 

4 liters/min and the room was kept on a light cycle of 12 hr per day (0600 to 1800). All 

ducks were fed Mazuri® (PMI Nutrition International, Brentwood, MO) #5681 sea duck 

diet ad libitum throughout the experiment. Feeding stations consisted of two artificial 

islands per pool with open feeding pans placed on top. On 22 September, two pilot 

ducks (one male and one female) were inoculated orally with approximately 7 × 104

infectious units (IU) of the virus (as determined by tissue culture infective dose 50 [i.e., 

TCID50 ]concentrations) isolated from the year 2000 mortality event and the second 

female was inoculated with approximately 3.5 × 105

 IU of the virus to evaluate the dose 

required in the main experiment. Based on the results of the pilot experiment, thirteen 

additional Long-tailed Ducks were received on 22 October and placed in an identical 

biosafety level III room (control room). On 5 November, eight ducks were moved to the 

experimental room and inoculated with 6.2 × 105

 IU of the virus (Figure 7-1). The three 

ducks from the pilot study were moved into the room with the rest of the infected birds, 

and the five ducks remaining in the control room were dosed orally with sterile transport 

media. Pre-inoculation blood samples and cloacal swabs were collected from all ducks. 

Post-inoculation blood samples were collected at intervals of approximately one week 

and cloacal samples were collected on the day after inoculation and every three days 

thereafter from the virus-inoculated ducks and weekly from the control ducks. The 

weight of each duck was recorded each time a cloacal sample was taken. During the 

first two weeks postinoculation, the feeding pans of the ducks were periodically 

swabbed for virus isolation. On 7 December, three experimental ducks (second 

inoculation) and three controls were inoculated with approximately 1.6 × 106

 IU of virus 

(Figure 7-1). Ducks were euthanized and necropsied 6 to 32 days postinoculation 

(Figure 7-1), based on weight changes in conjunction with data on virus activity. The 

three ducks from the pilot study were euthanized on 5 January 2002. Blood samples 

were collected prior to euthanasia, centrifuged at 1500 g for 10 min, and serum was 

harvested for antibody testing and serum biochemistries. At necropsy, the following 

tissues were collected for both virus isolation and histopathology: duodenum, jejunum, 

ileum, large intestine, cloaca, cecum, liver, spleen, kidney, lung, and gonad. 

Additionally, heart and skeletal muscle were collected for histopathology. Virus isolation 

was performed as described for samples collected from the wild birds. 
74

RESULTS 

Necropsy findings 

Both Long-tailed Ducks collected from the Control Area in 2000 were males and in poor 

to moderate body condition (reduced pectoral muscles and subcutaneous fat reserves). 

Gross lesions of enteritis (dilated small intestines, mucoid and hemorrhagic intestinal 

contents) were observed at necropsy in both individuals. No pathogens were isolated in 

bacterial cultures and heavy metal levels in liver samples were not considered elevated. 

Small intestinal and cloacal samples from carcasses were positive in virus cultures, and 

the isolates were characterized as nonenveloped DNA-viruses. By electron 

microscopy, the viral capsomeres were arranged in equilateral triangles and the isolates 

were identified as adenoviruses (Horne et al. 1959). The virus was not neutralized by 

reference antiserum against avian adenovirus group I, II, or III viruses. 

Virus prevalence in the field 

In 2000, live virus was isolated from 50% of cloacal swabs collected at the mortality site 

(Control Area) and from 7% of cloacal swabs from the Industrial Area, where no 

Day of study

40 50 60 70 80 90

Bird Id Number

9380

9381

10940

9369

10941

9358

9383

9368

9384

7213

9361

10942

9343

1

2

Figure 7-1. Time line indicating when individual birds were inoculated with the adenovirus and 

necropsied. Solid lines indicate exposed birds and dashed lines indicate control birds. Number 

1 identifies the day on which control birds were inoculated with the virus. Number 2 identifies 

the day on which exposed individuals were re-inoculated with the virus. Three additional “pilot” 

birds were inoculated on day 1 and necropsied on day 105 (not plotted). 
75

mortality was observed. Prevalence of serum antibody titers ≥1:64 was 86% in the 

Control Area and 10% in the Industrial Area. In 2001, no mortality was observed at 

either of the study areas. Live virus was present in 7% and 0% of the cloacal swabs 

collected in the Control and Industrial Areas, respectively. Seroprevalence was 20% in 

the Control Area and 0% in the Industrial Area. 

Experimental study 

No mortality occurred during the experimental study. Clinical signs indicative of 

gastrointestinal disease were observed in ducks inoculated with adenovirus and 

included watery consistency of feces and blood in feces. Serologic testing indicated 

that all inoculated ducks became infected with the virus. Titers of >1:256 were 

measured at 7 to 14 days post-inoculation. After three weeks, the circulating antibody 

levels dropped and exhibited an undulating pattern in the three ducks that were 

monitored for 3.5 months. Most inoculated birds were shedding viruses from their 

cloaca for approximately two weeks, and the three ducks that were monitored for 3.5 

months were shedding periodically throughout the entire period. In the 11 ducks that 

were necropsied during days 6-32 post-inoculation, virus was isolated most frequently 

from the ileum and large intestine (73%), but was also isolated from duodenum (63%), 

jejunum (55%), cloaca (55%), cecum (55%), liver (36%), kidney (27%), lung (9.1%), 

testis (14% of males), and ovary (14% of females). At necropsy, pinpoint and 

paintbrush hemorrhages were noted in the proximal intestine of eight (73%) birds. 

Histological findings included widespread lymphoid hyperplasia and necrosis (in 91% of 

the ducks), mild to moderate multifocal non-suppurative enteritis (91%), mild to 

moderate multifocal non-suppurative typhlocolitis (36%), and multifical non-suppurative 

hepatitis (36%). Lymphoid and enteric lesions were seen throughout the 6-32 day 

period, and hepatic lesions were seen in ducks euthanized on days 6, 7, and 12 postinoculation. Hepatic intranuclear inclusion bodies were found in one duck at 12 days 

post-inoculation. Virus was re-isolated from the spleen of two of three individuals 

monitored for 3.5 months. One of these ducks showed lymphoid hyperplasia and 

necrosis of spleen and small intestine, and had multifocal non-suppurative enteritis. 

Viruses were also isolated from materials swabbed from the feeding pans during the 

first two weeks of the study. 

DISCUSSION 

We isolated an adenovirus from intestinal tissues of Long-tailed Ducks found dead in 

the Beaufort Sea in Alaska in 2000. The virus was not neutralized by antisera against 

previously known avian group I, II, or III adenoviruses and probably represents a new 

duck serotype. Although no mortality occurred during the inoculation study, the virus 

was infectious for Long-tailed Ducks under experimental conditions and resulted in 

lesions previously described for avian adenovirus infections, including intestinal 

hemorrhage, inclusion body hepatitis, and lymphoid tissue pathology (McFerran 1997a, 

McFerran and Adair 1977, Pierson and Domermuth 1997). Based on the pathology 

observed in Long-tailed Ducks, the newly isolated adenovirus appears to share some 

characteristics with both group I and II avian adenoviruses. Inclusion body hepatitis has 
76

been previously reported in chickens, pigeons, and some species of raptors, with the 

etiologic adenovirus belonging to group I. Hemorrhagic enteritis virus has been 

previously reported from turkeys, is classified as a group II adenovirus, and has also 

been associated with immune system pathology. Reproductive pathology has been 

linked to group III avian adenoviruses and virus-associated effects include reductions in 

egg production rate and egg abnormalities (Mcferran 1997b, McFerran and Adair 1977). 

In our experimental study, the adenovirus was isolated from gonad tissues of one male 

and one female Long-tailed Duck, but the potential of the newly isolated virus to affect 

reproductive parameters was not studied in Long-tailed Ducks. 

The carcasses collected from the original die-off in the Beaufort Sea showed lesions 

indicative of enteric pathology. Because the tissues were not suitable for a histological 

evaluation, we were unable to determine if liver inflammation and hepatic intranuclear 

inclusion bodies were present. Based on the serology and virus isolation results from 

live-trapped Long-tailed Ducks, high prevalence of both serum antibodies and live 

viruses in cloacal samples was strongly linked to the mortality event in 2000. The 

prevalence of antibodies and live viruses was significantly lower in birds trapped at the 

reference site 100 km away, where no mortality was observed in 2000. Furthermore, in 

2001, the year following the die-off when no mortality was observed, virus prevalences 

were also very low. These results support the hypothesis that the virus was either 

responsible for or a significant contributing factor in the mortality event in 2000. 

Experimentally infected Long-tailed Ducks were shedding viruses from their cloaca for 

approximately two weeks post-inoculation and intermittently for at least an additional 

four months. Viruses were isolated from feeding pans of the experimental pools for two 

weeks post-inoculation. Because all ducks were shedding viruses and virus persisted in 

the feeding pans, transmission via a fecal-oral route seems likely for the Long-tailed 

Duck adenovirus. In the wild, molting Long-tailed Ducks roost in dense flocks on shore 

during the nights allowing for close bird-to-bird and bird-to-feces contacts, and thus, 

potential virus transmission. 

Animals inoculated with viruses and housed under experimental conditions often exhibit 

less severe disease than that which occurs in natural infections (Turpin et al. 2002), and 

a similar scenario may explain our experimental results in captive birds that were fed ad 

libitum and not stressed by other environmental factors. The virus caused moderate 

intestinal, hepatic, and lymphoid pathology in Long-tailed Ducks under experimental 

conditions, and effects on organs can be more significant in the wild because birds 

undergoing molt may be nutritionally stressed due to energy demands of feather growth 

and decreased insulation (Hohman et al. 1992, Murphy and King 1992). Indeed, Howell 

(2002) documented declines in body mass and lipid levels in Long-tailed Ducks 

undergoing wing molt in the Beaufort Sea during the same time when our study was 

conducted. Additionally, birds in the Control Area, where the prevalence of virus was 

higher, were in poorer condition than birds in the Industrial Area. Birds that experience 

nutritional stress during molt may be more susceptible to viral infection and therefore, 

the molt may have been a contributing factor to the mortality event in 2000. 

Furthermore, an infectious agent interfering with intestinal function could increase the 
77

level of nutritional stress and lead to a cycle of deteriorating body condition during molt. 

Both carcasses that were examined in 2000 were in relatively poor body condition, 

suggesting that interactions between nutrition and intestinal adenovirus infections 

contributed to the mortality. 

Additional indirect health effects of Long-tailed Duck adenoviruses may relate to 

immunopathology and reproductive disorders. If the adenovirus induces 

immunosuppression as a consequence of the lymphoid depletion and necrosis noted in 

our experimental study, interactions between the virus and other pathogens or 

environmental contaminants may be important. Other than gonadotropism, we have no 

evidence for the potential of Long-tailed Duck adenoviruses to cause reproductive 

problems in their host, but because previously reported strains of adenoviruses 

associated with ducks have been linked to decreased egg production and egg 

abnormalities, the possibility of reproductive consequences should be evaluated in 

Long-tailed Ducks during nesting. If the newly isolated adenovirus is associated with 

impaired nutrition, reproduction, and immunocompetence in addition to causing direct 

mortality, its overall effects may be more subtle but result in a long-term reduction in 

population size and viability. 
78

Chapter 8 

Trace Elements in Long-tailed Ducks 

INTRODUCTION 

Metals and trace elements enter the marine environment from natural geologic sources 

and from discharges of anthropogenic materials (Law 1996). In marine ecosystems, the 

highest concentrations of trace elements often occur in coastal regions (Harrison and 

Peak 1995), where they bind to and accumulate in sediments. From sediments, 

contaminants may bioaccumulate in the food chain, a species-dependent process 

associated with feeding ecology, potentially resulting in toxicity (Barron 1995). 

Contaminants also may enter arctic environments through releases associated with 

mining and the use and transport of oil and gas resources. Findings of the Minerals 

Management Service’s Beaufort Sea Monitoring Program indicated that concentrations 

of some trace elements, particularly barium and chromium, were higher in sediments 

from the Beaufort Sea in comparison to other outer continental shelf sediments (Beohm 

et al. 1990). The National Oceanic and Atmospheric Administration’s National Status 

and Trends Program found that sediment concentrations of arsenic, mercury, nickel, 

and silver were higher at Oliktok Point on the Beaufort Sea than at the reference area, 

Lutak Inlet in southeast Alaska (Meador et al. 1994). In the western Beaufort Sea, 

concentrations of arsenic and nickel were higher than the average for U.S. sediments 

(Valette-Silver et al. 1997). 

Lead exposure from ingested shot is a problem that has been identified for the 

threatened Spectacled Eider (Somateria fischeri) in western Alaska, where populations 

of this species have undergone a dramatic decline. Since 1992, lead poisoning has 

been diagnosed as the cause of death of several Spectacled Eiders on the breeding 

grounds (Franson et al. 1995). Lead poisoning and predation were the only two 

identified causes of mortality for adult female eiders followed by radiotelemetry during a 

3-yr study of survival during the brood rearing period (Flint and Grand 1997). Flint et al. 

(1997) studied exposure of diving ducks to lead in western Alaska and reported that 

nearly 12% of the Spectacled Eiders radiographed had ingested shot and that up to 

36% of Spectacled Eider hens, about 12% of Spectacled Eider ducklings, and over 20% 

of Long-tailed Ducks sampled on the breeding grounds had blood lead concentrations 

of >0.20 ppm wet weight. Blood lead concentrations of >0.20 ppm were associated with 

reduced survival rates of immature Canvasbacks (Aythya valisineria) (Hohman et al. 

1995) and a lower survival index was associated with elevated blood lead 

concentrations in American Black Ducks (Anas rubripes) (Samuel et al. 1992). The 

factors contributing to reduced long-term survival of waterfowl exposed to lead may be 

those that are sublethal in the short term, such as anemia and other hematologic 

effects, reduced body condition, and immunosuppression (Pain 1989, Hohman et al. 

1990, Rocke and Samuel 1991). 

High levels of cadmium and selenium have been reported from sea ducks in several 

areas of Alaska. Cadmium is not biologically essential, is toxic at high concentrations, 

and is present in some ores and in various anthropogenic sources, including wastes 
79

from mining, smelting, and the incineration of cadmium-bearing materials (Eisler 2000). 

Reported cadmium concentrations in kidneys of sea ducks from Alaska have been as 

high as 353 ppm dry weight in Spectacled Eiders and 375 ppm dry weight in Whitewinged Scoters (Melanitta fusca) (Henny et al. 1995, Stout et al. 2002). Although 

marine birds may tolerate higher cadmium levels than other species, Furness (1996) 

proposed a threshold level of 100 ppm wet weight (about 333 ppm dry weight) in 

kidneys of birds, above which cadmium poisoning could be expected. Selenium is an 

essential trace element for birds and other animals, but it bioaccumulates through the 

food chain and can be toxic (Eisler 2000). Selenium enters the environment from a 

variety of anthropogenic sources, but it is also a natural component of the earth’s crust 

and the major source of environmental selenium is the weathering of rock (Eisler 2000). 

Selenium concentrations in sediments at remote coastal Alaska sites, including Oliktok 

Point on the Beaufort Sea, Dutch Harbor on the near Aleutian Islands, and Boca de 

Quadra in southeast Alaska are higher than at many other sampling sites along the 

Pacific coast from southern California northward (Meador et al. 1994). High selenium 

concentrations also have been found in waterfowl in Alaska. Spectacled Eiders, most of 

which were collected at St. Lawrence Island and the Yukon-Kuskokwim Delta (YKD), 

had selenium concentrations in their livers that ranged from 4.98 ppm to 401 ppm dry 

weight, with means of 43 ppm dry weight in females and 124 ppm dry weight in males 

(Trust et al. 2000, Stout et al. 2002). In livers of Steller’s Eiders (Polysticta stelleri), 

most of which were collected near Barrow, Kotzebue, and Togiak, selenium 

concentrations ranged from 8.18 ppm to 56.8 ppm dry weight (Stout et al. 2002). Mean 

selenium concentrations of 19.26 and 12.79 ppm wet weight were found in blood of 

male and female Spectacled Eiders, respectively, sampled during the incubation period 

on nesting grounds in the YKD (Grand et al. 2002). Marine birds apparently tolerate 

higher selenium exposure than freshwater species, as levels above 10 ppm wet weight 

(about 33 ppm dry weight) in liver are considered harmful in the latter group (Heinz 

1996). 

Little is known about trace element exposure in Long-tailed Ducks. Our objectives were 

to measure and compare trace elements and delta-aminolevulinic acid dehydratase 

(ALAD), an enzyme inhibited by lead, in blood of Long-tailed Ducks molting at the 

Control and Industrial Areas. 

METHODS 

In 2000, whole blood samples for trace element analysis were collected from 20 male 

Long-tailed Ducks at each of the two study areas (Control and Industrial). Samples 

were frozen in the field in a liquid nitrogen vapor shipper and transported to the National 

Wildlife Health Center, where they were stored at –75°C until shipment to RTI 

International, Research Triangle Park, NC, for analysis of the following trace elements: 

Al, As, B, Ba, Be, Cd, Cr, Cu, Fe, Hg, Mg, Mn, Mo, Ni, Pb, Se, Sr, V, and Zn. Samples 

were weighed, freeze dried (percent moisture determined by the difference in the wet 

and dry weights), and digested in nitric acid. Analysis was done by inductively coupled 

plasma/mass spectrometry (ICP/MS), except mercury was analyzed by cold vapor 

atomic absorption (CVAA) and selenium by graphite furnace/atomic absorption 
80

spectrophotometry (GF/AAS). The ALAD activity was measured colorimetrically in 

whole blood (Burch and Siegel 1971). One unit of enzyme activity is defined as an 

increase in absorbance at 555 nm of 0.100, with a 1.0 cm light path, per ml of 

erythrocytes per hour, at 38°C. Kruskal-Wallis tests were used to compare contaminant 

residues and ALAD activity between the Control and Industrial Areas. Spearman 

correlation was used to evaluate the relationship of Se and Hg concentrations in blood. 

We assigned a concentration of one-half of the lower limit of detection to the samples 

that contained no residues of a particular contaminant, as long as detectable residues 

were found in 50% or more of the samples. If residues of a particular contaminant were 

found in less than 50% of the samples, no median was calculated and the data were not 

used in statistical comparisons. Trace element residues in blood are reported on wet 

weight basis. 

RESULTS 

Nickel and Be were not detected in any of the Long-tailed Duck blood samples. 

Aluminum, Cd, As, and Ba were detected in 7.5% to 35% of the samples. Boron was 

above detectable limits in 30% of the samples at the Industrial Area and in 95% of the 

blood samples from the Control Area, for an overall frequency of 62.5%. The remaining 

trace elements were detected in all samples (Tables 8-1 and 8-2). Concentrations of six 

trace elements differed significantly (P < 0.05) between locations (Table 8-2). Iron, Mg, 

and Zn were higher in blood samples collected from the Industrial Area, while Cr, Sr, 

and V were higher in the Control Area. The median blood Pb concentration in Longtailed Ducks was low (0.01 ppm wet weight) and the ALAD activity was not significantly 

different between locations (249 units in the Industrial Area and 232 units in the Control 

Area). Concentrations of Se in blood were high, Hg was low, and the two were not 

correlated (n = 40, r = -0.04, P = 0.80). 

Table 8-1. Frequency of detection, median, and minimum-maximum concentrations 

(ppm wet weight) of 11 trace elements1 in blood of Long-tailed Ducks at the 

Control and Industrial Aeas (n = 40). 

Trace element % detected2 Median Min-max 

Al Aluminum 7.5 NC3 ND4-2.64 

As Arsenic 20.0 NC ND-0.18 

B Boron 62.5 0.08 ND-0.23 

Ba Barium 35.0 NC ND-0.03 

Cd Cadmium 17.5 NC ND-0.007 

Cu Copper 100.0 0.31 0.15-0.61 

Hg Mercury 100.0 0.12 0.06-0.28 

Mn Manganese 100.0 0.016 0.005-0.412 

Mo Molybdenum 100.0 0.03 0.007-0.060 

Pb Lead 100.0 0.01 0.006-0.081 

Se Selenium 100.0 11.3 4.20-22.1 

1

Trace elements tested for, but not detected, were Be and Ni. 

2

The percentage of the samples in which the trace element was above the detection 

limit. 

3

Not calculated when trace element was detected in <50% of samples. 4

Not detected. 
81

Table 8-2. Median and minimum-maximum concentrations (ppm wet weight) of six trace elements in 

blood of Long-tailed Ducks at the Control and Industrial Beaufort Sea study areas. All six were 

detected in all samples at each location and the concentrations were significantly different 

between locations (Kruskal-Wallis test; P = 0.0066 for Cr, 0.0090 for Fe, 0.0200 for Mg, <0.0001 

for Sr, 0.0102 for V, 0.0167 for Zn ). 

Location Cr Fe Mg Sr V Zn 

Control (n = 20) 

Median 0.15 452 68.6 0.11 0.049 4.52 

Min-max 0.07-0.40 390-755 63.6-116 0.05-0.22 0.023-0.127 4.00-8.37 

Industrial (n = 20) 

Median 0.11 493 71.7 0.05 0.039 4.73 

Min-max 0.05-0.40 422-759 60.1-116 0.03-0.11 0.020-0.130 4.30-7.88 

DISCUSSION 

Apart from several reports in eiders, few published data are available for trace element 

concentrations in blood of sea ducks. The median Se concentration (11.3 ppm wet 

weight) in blood of Long-tailed Ducks in this study was greater than in Common Eiders 

(6.87 ppm wet weight) sampled at the same locations (see Chapter 13). Selenium 

levels in blood of Long-tailed Ducks in the Beaufort Sea were also greater than most 

other published reports from sea ducks, except for Spectacled Eiders sampled during 

the incubation period on the YKD of Alaska, where mean concentrations were 19.26 

ppm wet weight in males and 12.79 ppm wet weight in females (Grand et al. 2002). 

Although marine birds can apparently tolerate higher Se exposure than freshwater 

species, high Se levels in blood of Emperor Geese (Chen canagica) were accompanied 

by increased glutathione peroxidase in plasma, which is consistent with early oxidative 

stress (Hoffman 2002, Franson et al. 2002). High levels of Hg may counteract some of 

the toxic effects of Se (Cuvin-Aralar and Furness 1991), but Hg concentrations in blood 

of Long-tailed Ducks were low. At 0.01 ppm wet weight, the median lead concentration 

was much lower than the threshold (0.20 ppm wet weight) for lead exposure (Pain 

1996). These results suggest that Long-tailed Ducks sampled at molting areas in the 

Beaufort Sea have lower concentrations of lead in their blood than the levels that have 

been reported from Long-tailed Ducks and other sea ducks in some areas of Alaska, 

such as the YKD (Flint et al. 1997, Grand et al. 2002). 

Aluminum was detected in only 7.5% of the blood samples, at a maximum concentration 

of 2.64 ppm wet weight. Aluminum is poorly absorbed, it’s potential for toxicity is low, 

and soft tissue levels do not necessarily reflect exposure (Scheuhammer 1987). 

Detectable levels of Cd were found in the blood of 17.5% of Long-tailed Ducks and the 

maximum concentration was about one-half the maximum found in Common Eiders in 

this study (see Chapter 13) and considerably lower than in blood of Common Eiders 

from the Canadian Arctic (Wayland et al. 2001). Boron is not an essential trace element 

for animals, is distributed widely in the environment, and naturally elevated boron levels 

are often associated with marine sediments, seawater, and some ground waters (Eisler 

2000). Boron was found in 62.5% of the Long-tailed Duck blood samples, at a 

maximum concentration of 0.23 ppm wet weight. In a study evaluating the uptake and 

loss of boron, Mallards were fed a diet containing 1600 ppm B for 32 days, at which 
82

time the mean B concentration in blood was 67.57 ppm dry weight (about 14 ppm wet 

weight, assuming 80% moisture for blood) (Pendleton et al. 1995). Thus, the 

concentrations of B that we found in Long-tailed Ducks are not likely to have adverse 

impacts. Copper concentrations in Long-tailed Ducks were low and similar to the levels 

found in Common Eiders (see Chapter 13). Birds and mammals are more resistant to 

Cu exposure than other animals and interpretive data on the toxicity of Cu are not 

available for wild birds (Eisler 2000). 

Although concentrations of six trace elements in the blood of Long-tailed Ducks differed 

significantly between the two study areas, they were not consistently greater at one 

location. Concentrations of three trace elements (Cr, Sr, and V) were greater in the 

Control Area and three (Fe, Mg, and Zn) were greater in the Industrial Area (Table 8-2). 

Except for Sr (twice as high at the Control area) and Cr (36% greater at the Control 

Area), the differences between the median concentrations at the two locations were 

relatively low (4.5-25%). According to Eisler (2000), presumptive evidence for 

chromium exposure is based on tissue (type not specified) levels of 4 ppm dry weight or 

greater. For blood, this is equivalent to approximate 0.8 ppm wet weight, a considerably 

greater concentration than we found in Long-tailed Ducks. Zinc is an essential trace 

element, but can be toxic at high concentrations. The concentration of Zn in Long-tailed 

Ducks was similar to levels found in blood of Trumpeter Swans (Cygnus buccinator) in 

the western United States (Blus et al. 1989). Iron, Mg, and Mn are required trace 

elements and little information is available about toxic levels. Likewise, few interpretive 

data for Sr and V in blood of birds are available, but normal levels in mammals are 

reported to be about 0.07 and 0.02-0.03 ppm wet weight, respectively (Puls 1988). 

Concentrations of Fe, Mg, Mn, Mo, Cr, V, and Zn were similar to, or somewhat lower 

than, median concentrations in blood samples from Steller’s Eiders from the Alaska 

Peninsula, while Sr at the Control Area was somewhat greater than the median found in 

Steller’s Eiders (Franson, unpublished data). 

The Se concentrations that were found in blood of Long-tailed Ducks in the Beaufort 

Sea are some of the highest levels yet to be reported in sea ducks. Although Se 

concentrations did not differ between locations, concentrations of three trace elements 

were higher in the Control Area and three were higher in the Industrial Area. Strontium 

levels were relatively low, but the median Sr concentration in the Control Area was 

about twice as high as in the Industrial Area. The magnitude of difference between 

medians of the other five trace elements that differed by location was considerably less. 

Except for Se and Sr, concentrations of other trace elements in blood of Long-tailed 

Ducks were often similar to, or lower than, trace element levels in other sea ducks. 
83

Chapter 9 

Synthesis of Aerial and Ground Based Studies of Molting Long-tailed Ducks 

Aerial surveys of Long-tailed Ducks molting in the Beaufort Sea lagoons indicate a longterm population decline (Fischer et al. 2002). This decline in molting populations is 

occurring coincidentally with declines in breeding population indices (USFWS unpubl. 

data). Thus, it is unclear if these long-term population declines are related to factors 

encountered on breeding areas, molting areas, or during other portions of the annual 

cycle. In fact, the breeding and wintering affiliations for birds molting on our study areas 

are unknown. Many species of waterfowl undergo substantial migrations away from 

breeding areas prior to molting. Therefore, the associated breeding affiliations for this 

molting population could be a considerable distance away. However, recent satellite 

telemetry data from the Yukon-Kuskokwim Delta suggests that birds breeding in 

western Alaska do not migrate to the North Slope. Thus, it is most likely that birds 

molting along the Beaufort Sea are from breeding areas in arctic Alaska and Canada. 

There is essentially nothing known about the biology of Long-tailed Ducks breeding in 

the arctic making it impossible to define potential causes of the broad scale population 

decline. 

While aerial surveys indicate a population decline, there has been little change in overall 

patterns of distribution of molting birds within our study areas (Fischer et al. 2002). 

Thus, it does not appear that there has been localized extinction or displacement of 

birds from historic range. The overall patterns of distribution observed during aerial 

surveys are supported by our ground based observations and radio telemetry data. 

Generally, Long-tailed Ducks roost along the barrier islands in the morning and evening, 

and feed in open water areas, mid-day. 

Our data suggest that body condition dynamics are adapted to minimize the flightless 

period. Several factors suggest that forage quality and/or availability are not limiting. 

First, the relative energetic costs of feather production are thought to be relatively 

constant throughout the period of feather regrowth. While molting Long-tailed Ducks 

initially lose weight, they re-gain weight during the latter half of the feather growth 

period. If costs are constant, then the fact that individuals can increase in condition 

suggests that exogenous resources are not limiting. Second, Long-tailed ducks are 

simultaneously molting body feathers during the flightless period. This body molt incurs 

additional costs in two ways. First, there is the additional direct cost of replacing these 

feathers. Second, there may be additional metabolic maintenance costs caused by a 

reduction in plumage insulation during body molt. Other species of waterfowl delay 

body molt until well after wing molt is complete thereby extending the overall period of 

molt, but minimizing immediate costs. If foraging were limiting, we would not expect 

flightless Long-tailed Ducks to be simultaneously molting body feathers during the 

flightless period. Finally, our data indicate that individual birds are quite mobile even 

during the flightless period. While we documented considerable movements of 

individuals, we did not see complete abandonment of specific areas. If birds were 

moving in response to inadequate foraging conditions, we would expect to see 

directional movement away from specific areas resulting in localized reductions in 
84

density and abandonment of these areas. However, our data suggest that individuals 

are moving in multiple directions and that a significant fraction of the observed 

movements are ‘temporary’ and many birds ultimately returned to previously used 

areas. 

We found little evidence for displacement of birds as a direct result of disturbance. 

Levels of disturbance varied from our boating activity to an underwater 3-D seismic 

survey using airguns. In neither case did we detect substantial changes in distribution, 

behavior, or body condition of Long-tailed Ducks. Importantly, our study of individual 

behavior started with a major disturbance event (i.e., the capture and marking of 

individuals), yet we did not see substantial movements immediately following marking. 

Obviously, we have no measure of behavior prior to marking and thus, we cannot 

assess potential changes in behavior as a result of our marking activities. However, we 

conclude that wind and weather patterns have a stronger influence on movements and 

patterns of habitat use than disturbance. 

The strongest negative effect on molting Long-tailed Ducks that we observed was 

caused by the adenovirus outbreak we identified in 2000. The exposure rate to this 

virus was highest in the area with the greatest density. This was likely caused by 

greater bird-to-bird transmission in high-density situations. Aside from causing direct 

mortality, exposure to the virus likely had a major influence on body condition dynamics 

during the flightless period. This virus was detected in subsequent years, but was not 

widespread and did not cause any observed mortality. The factors related to outbreaks 

of this virus and frequencies of mortality events are unknown. However, disease agents 

that influence large concentrations of birds would certainly have the potential to 

influence population dynamics. 

While our studies have yielded useful information regarding behaviors, patterns of 

habitat use, and effects of disturbance, several major questions still remain. Foremost, 

it is unclear what factors influence variation in density within and among lagoons. 

Densities have historically been higher in the Control Area than the Industrial Area. 

Associated with these differences in densities we also observed variation in behavior, 

and habitat use among lagoons. However, this variation in habitat use and behavior 

apparently was not of sufficient magnitude to influence body condition. The disease 

outbreak we observed in 2000, may have been related to overall density of birds; 

however, if this were occurring regularly, we would expect relatively uniform, low 

densities of birds throughout the lagoon systems. Birds in the Control Area also tended 

to use the mainland shoreline more frequently than those in the Industrial Area. 

Certainly this could be a direct influence of the existing development in place along the 

mainland in the Industrial Area. However, an alternative explanation of this variation 

among lagoons is related to the topography of the lagoons and relative protection from 

wind generated waves. The mainland shoreline along the Control Area has a series of 

protected estuaries and small islands. Additionally, the prevailing winds are from the 

northeast. Thus, the limited fetch in the far eastern portion of the lagoon limits the 

overall magnitude of the waves. Conversely, in the Industrial Area, the mainland 

shoreline lacks protective structure and is exposed to considerable fetch and associated 
85

larger waves. Thus, it appears that the primary factors influencing overall variation in 

density are related to habitat selection in relation to topography and protection from 

prevailing winds. 

A second major, unanswered, question is relative to the use of the Beaufort Sea outside 

the barrier islands. Flightless birds were regularly observed on the Beaufort Sea side of 

the barrier islands and several of our radio-equipped birds moved outside of the islands 

during the flightless period. Aerial survey data suggest that a significant proportion of 

Long-tailed Ducks move offshore after regaining flight, where they stage prior to the 

initiation of fall migration. Thus, on the larger scale, off-shore areas may be preferred 

habitat for Long-tailed Ducks. These areas may be unsuitable for molting because they 

lack protection from waves and ice. However, previously we suggested that our data 

support the conclusion that potential forage was not limiting molting birds. While forage 

may not be limiting birds during the flightless period, it may be inadequate to allow birds 

to gain sufficient condition to undertake fall migration. Thus, the preference of the 

Beaufort Sea lagoons for molting Long-tailed Ducks may be related to the proximity of 

post-molt staging habitats offshore. 
86

Chapter 10 

Aerial Population Surveys of Common Eiders Along the Coastline and 

Barrier Islands of the Arctic Coastal Plain 

INTRODUCTION 

Pacific Common Eiders (subspecies v-nigra) breed along the coast of arctic Alaska and 

Canada. Indices to the number of Common Eiders migrating past Barrow, Alaska, have 

dropped dramatically in recent years (Suydam et al. 2000), suggesting that this 

breeding population is declining. Recent satellite telemetry data suggest that the North 

Slope breeding population is largely isolated from other populations of this subspecies 

(Petersen and Flint 2002). This conclusion is supported by recent genetic analyses 

(Sonsthagen unpubl. data). The factors contributing to this overall population decline 

are unknown. We designed an aerial survey to assess the population trend on a more 

localized geographic scale. 

We initiated aerial surveys designed to index the breeding population of Common 

Eiders along the Alaskan Beaufort Sea coast. Here we summarize four years (1999-

2002) of aerial surveys during the early incubation period of Common Eiders. We 

focused on estimates of Common Eider population size, demography, and distribution 

north and east from Omalik Lagoon on the Chukchi Sea to the Canadian border on the 

Beaufort Sea. This survey was designed to provide an estimate of the numbers and 

trends of Alaskan breeding Common Eiders along the Chukchi and Beaufort Seas 

coastline. These data are useful in interpreting ground based nesting surveys and the 

migration counts of the combined Alaska and Canadian populations at Point Barrow. 

METHODS 

The survey was timed to coincide with early incubation while male eiders were in the 

vicinity of breeding sites. Ground surveys of nesting birds were used to determine 

breeding phenology and appropriate survey timing. A Cessna 206 amphibian flown at 

approximately 110 knots and an altitude of 45 meters ASL/AGL was used. 

Observations were made from both sides of the aircraft and entered directly into laptop 

computers using remote microphones. Computers interfaced with the aircraft Global 

Positioning System (GPS) and observations were collected using record and transcribe 

programs. Meandering flight routes followed mainland and barrier island shorelines and 

adjacent nearshore waters providing complete coverage from the shore to 1.6 km 

offshore. Moving map programs on both pilot and observer computers helped ensure 

coverage of the survey area. Flights were conducted in calm or light winds whenever 

possible and during mid-day to maximize height of the sun-angle, reduce glare and 

increase the visibility of birds on the water. 

The survey area included 29 shoreline segments and 20 islands or island groups 

(Figure 10-1). Insular areas along the central Beaufort Sea coast were identified on 

1:63,360 scale topographic maps. Maps were consulted primarily to identify segment 

start and stop points. General observations on habitat and survey conditions were 
87

recorded by the pilot and along with sea surface analyses prepared by the National 

Weather Service, were used to portray ice conditions during the survey. For all 

waterfowl species, we determined the sex and age of single birds and composition of 

flocks whenever possible. Observations of Common Eiders and other species were 

summarized by barrier island and shoreline survey and for the total survey area. The 

distribution and sex and age composition of Common Eiders and the number of 

indicated breeding pairs (single males + pairs) throughout the survey area were 

determined to estimate the proportion of breeding birds and total population size 

(singles + 2x pairs + flocks). 

RESULTS 

In 2002, our estimate of indicated breeding pairs and total Common Eider counts were 

up 56.8% and 24.4%, respectively, from 2001. Total Common Eider counts have 

increased steadily during this study (Figure10-2, Table 10-1a & b) and have paralleled 

the increased severity of sea ice conditions during this time. Estimates of indicated 

breeding pairs across the entire coastline have not increased similarly. However, there 

is an increasing trend of indicated pairs in all areas excluding the central Beaufort Sea 

segments (Figure 10-2, Table 10-1b). In all four years, indicated breeding pairs were 

most numerous along the central Beaufort Sea coast (Figure 10-2,Table 10-1 a & b). 

Figure 10-1. Location of aerial survey segments along the Arctic Coastal Plain (A) shoreline and (B) 

barrier islands.
88

Table 10-1a. Summary of Common Eider counts during aerial surveys of the Arctic Coastal Plain (barrier 

island segments), 1999-2002. 

___1999___ ___2000___ ___2001___ ___2002___

Survey 

Segment 

Indicated 

Pairs 

Total 

Counts 

Indicated 

Pairs 

Total 

Counts 

Indicated 

Pairs 

Total 

Counts 

Indicated 

Pairs 

Total 

Counts 

190 - - 16 19 4 5 8 16 

191 9 14 2 3 16 21 12 20 

192 14 18 6 10 3 9 8 18 

193 0 0 1 2 0 0 2 5 

194 0 0 4 8 0 0 5 7 

195 0 0 0 1 0 0 1 2 

196 21 26 4 4 24 72 5 19 

197 42 45 21 45 7 12 54 205 

198 9 12 34 38 49 77 13 22 

199 45 71 13 18 30 35 41 541 

200 26 49 24 42 25 41 16 24 

201 31 45 8 14 12 16 8 18 

202 14 17 1 3 4 7 3 6 

203 49 64 25 43 11 15 3 6 

204 1 1 5 8 2 4 4 7 

205 0 0 14 20 4 7 7 14 

211 80 104 12 29 18 43 20 31 

212 26 33 24 38 27 41 36 85 

213 6 12 6 7 8 14 4 7 

214 5 11 69 124 3 11 5 8 

TOTAL 378 522 289 476 247 430 255 1061 

1 Single males + pairs = Indicated total pairs. 

2

 Total = singles + 2 x pairs + flocks 

Indicated Pairs

1999 2000 2001 2002

Counts

0

100

200

300

400

500

Kasegaluk Lagoon (segments 2-7)

Peard Bay (segment 10)

Central Beaufort Sea (Segments 18-21,181-214)

Arctic NWR (Segments 22-29)

Other Areas (all remaining segments)

Total Counts

1999 2000 2001 2002

0

500

1000

1500

2000

Figure 10-2. Aerial survey counts of Common Eiders at known breeding areas on the Chukchi and 

Beaufort Seas. 
89

The 3083 adults in flocks (69% of total counts) in 2002 represent a nearly threefold 

increase over the mean counts of flocked adults from the three previous years 

( x=1086.7). Within flocks males predominated in 2002 and 2001, but in 2000 the sex 

ratio was nearly equal, and in 1999 females predominated. Adult males gather in flocks 

and eventually disperse from breeding areas as incubation proceeds, thus survey timing 

can affect the estimate of indicated breeding pairs and the sex ratio of flocks. No 

subadult males were observed in 2002, whereas they comprised 0.3-1.3% of total 

counts in 1999-2001. 

Table 10-1b. Summary of Common Eider counts during aerial surveys of the Arctic Coastal Plain (shoreline 

segments), 1999-2002. 

___1999___ ___2000___ ___2001___ ___2002___

Survey 

Segment 

Indicated 

Pairs 

Total 

Counts 

Indicated 

Pairs 

Total 

Counts 

Indicated 

Pairs 

Total 

Counts 

Indicated 

Pairs 

Total 

Counts 

1 4 5 0 6 1 7 4 5 

2 0 10 0 0 18 59 1 13 

3 4 14 0 0 10 34 15 24 

4 24 48 10 125 72 250 35 337 

5 19 51 58 228 32 179 46 163 

6 8 16 46 553 27 196 64 1095 

7 14 37 5 8 6 26 16 170 

8 0 0 0 0 0 0 0 0 

9 1 29 0 0 1 2 0 0 

10 36 106 1 7 73 288 83 258 

11 3 187 0 0 0 10 0 1 

12 2 7 0 0 1 12 0 0 

13 0 0 0 0 0 0 0 0 

14 0 2 0 0 0 1 0 0 

15 0 0 0 0 0 0 0 0 

16 0 0 0 0 0 0 3 31 

17 0 0 0 3 0 0 0 0 

18 0 0 0 0 0 0 0 0 

19 - - 76 155 5 11 4 4 

20 4 5 45 98 21 46 33 60 

21 0 15 14 30 4 44 58 222 

22 24 65 103 408 53 248 44 174 

23 6 39 14 22 6 52 8 14 

24 1 2 4 94 6 111 7 19 

25 19 135 83 168 10 381 57 253 

26 13 34 80 209 31 68 35 107 

27 0 0 6 6 5 31 0 0 

28 11 22 0 0 37 314 44 316 

29 1 2 29 48 10 37 29 122 

TOTAL 194 831 574 2168 429 2407 586 3388 

1 Single males + pairs = Indicated total pairs. 

2

 Total = singles + 2 x pairs + flocks 
90

There is linear relation between indicated breeding pairs estimated by aerial surveys 

and our and ground based nest searches (R2

= 0.476, Figure 10-3; see chapter 11 for 

discussion of our ground based nest searches). This relation suggests that aerial 

surveys can be an efficient mechanism for estimating nesting effort over a large area. 

Aerial surveys consistently counted fewer pairs than we counted nests from the ground. 

A correction factor can compensate for this if aerial and ground searches coincide. 

DISCUSSION 

Primarily, these aerial survey data provide an estimate of Common Eider nesting effort 

and distribution. As evidenced by these data and our ground based nest searches (see 

Chapter 11), nesting effort is highly variable by location and year. Nesting effort can be 

an important indicator of population dynamics and breeding ecology trends, but it does 

not provide insight into the causes of population change. 

Aerial surveys do, however, show aspects of eider biology that ground based surveys 

cannot. The correlation of ice cover and the dramatic increase in total counts and 

flocked adults in 2002 may indicate larger scale dynamics of Common Eiders in the 

Beaufort Sea. Possibly, Canadian breeding eiders briefly delayed their arrival in the 

Canadian Arctic or a concentration of Alaskan Beaufort Sea eiders chose to forgo 

nesting due to poor conditions on breeding grounds. This conclusion is supported by 

the increase in flocked adults. Throughout this study the extent of sea and shorefast ice 

has increased. Common Eiders typically do not nest until the barrier islands are 

surrounded by open water. With few ice-free islands many potential breeders likely 

joined flocks instead of nesting. 

Aerial Survey

0 20 40 60 80

BRD Counts

0

20

40

60

80

100

120

140

2000

2001

2002

r

2

=0.870 r

2

=0.764

r

2

=0.404

Figure 10-3. Correlation of aerial survey counts (indicated breeding pairs) and ground based 

nest counts (all successful and failed eider nests).
91

Chapter 11 

Productivity of Nesting Common Eiders 

INTRODUCTION 

Clutch size, hatch success, fledging success, and adult survival are important 

parameters of population dynamics (Johnson et al. 1992, Coulson 1984). Milne (1974) 

demonstrated considerable annual variation in the proportion of Common Eider 

ducklings surviving to fledge and linked years of high duckling survival to subsequent 

increases in population size. Reported estimates of fledging success in other 

subspecies of Common Eiders vary widely among studies. Estimates of fledglings per 

female range from 0.47-0.89 (Hilden 1964, Swennen 1983) and duckling survival (hatch 

to fledge) has varied from 10% to 24% (Milne 1974, Mendenhall and Milne 1985). 

Brood rearing may be a bottleneck in annual productivity, and low duckling survival may 

be a major determinant of recruitment. Information on adult annual survival is absent 

for North America but presumed to be high (Suydam et al. 2000). Estimates of adult 

mortality in Europe, however, vary from less than 10% to 39% per year (Boyd 1962, 

Paludan 1962, Swennen 1972). 

There have been a variety of studies of Common Eider breeding ecology on the barrier 

islands of the Beaufort Sea. Schamel (1977) observed breeding on Egg Island before 

oilfield production began (1971-1973). He was able to measure nesting effort, hatch 

success, and predation pressure, but lacking marked animals, he did not estimate 

fledging success or adult survival. Ground based nest searches were used to estimate 

nesting effort and hatch success (Johnson and Richardson 1980, Johnson 1984), but 

again no birds were marked. During 1999-2002, biologists from LGL Alaska Research 

Associates located all Common Eider nests on islands between Thetis and Flaxman 

Islands during a short period in mid-incubation (Noel et al 2002). While geographically 

comprehensive, this study was unable to assess hatch success and may have missed 

nests that failed early or were initiated late. In an attempt to produce a comprehensive 

population model for Common Eiders in the Beaufort Sea, we implemented a protocol 

for consistent yearly ground based nest searches, monitoring, and mark-recapture over 

a large geographic area. 

METHODS 

During 2000-2002, we searched all islands from Brownlow Point through the Maguire 

Islands (Control Area) and from Stump Island to Spy Island (Industrial Area). Teams of 

observers searched islands by systematically examining all potential nesting cover in 

sufficient detail to detect nests not attended by females. Nest searches began as soon 

as incubation was detected during spot checks of suitable nesting habitat. 

We recorded the exact location (determined by GPS) and habitat information (i.e., 

landform, distance to water, height above water, density, and size of driftwood within 

one meter) for all nests. Active nests were marked with a lathe placed 5 m north of the 

nest bowl. We numbered and candled each egg to determine viability and stage of 
92

incubation (Weller 1956). During periodic revisits, we recorded the presence of the 

female, condition and number of eggs, and stage of incubation. After hatch, we visited 

nests and determined egg fates from nest contents. We subtracted depredated and 

unhatched eggs from the number of eggs laid into the nest to determine the number of 

ducklings produced. We calculated nest initiation dates by subtracting the estimated 

age of embryos, as determined by candling, plus the number of eggs laid into the nest, 

from the date of discovery. Alternatively, we back-calculated from known hatching 

dates to determine nest initiation. We used a 26 day incubation period and assumed 

one egg was laid each day prior to the start of incubation. 

We used only nests with signs of embryonic development in the determination of nest 

initiation date and clutch size. We defined clutch size as the number of eggs laid into a 

nest, partial depredation as the number of eggs missing from nests that remained 

active, and successful nests as those in which at least one egg hatched. For all nests 

with known fates we determined the number of successful, depredated, abandoned, or 

destroyed (e.g., by ice, or flooding) nests. We used a Mayfield approach to estimate 

daily nest survival rates and nest success (Johnson 1979). Analysis of nest success did 

not include nests that were depredated, destroyed, abandoned, or hatched when first 

discovered. 

We trapped females on nests before hatch using dipnets or string-activated bownets 

(Sayler 1962). Captured females were weighed, the lengths of the culmen and total 

tarsus were measured, and blood and cloacal samples were collected. Birds were 

marked with metal U.S. Fish and Wildlife Service tarsus bands and in most instances 

with numbered colored tarsal bands. In 2000 and 2001, a sub-sample of females was 

fitted with 15 g subcutaneously anchored radio transmitters (Pietz et al. 1995). These 

females were then tracked to determine the fate of broods. Initial brood size was 

assumed to be equal to the number of eggs present in the nest on the last visit prior to 

hatch. 

The fate of broods was assessed by ground based tracking in 2000 and a combination 

of aerial and ground based tracking in 2001. Searches were conducted as weather and 

pilot availability allowed (all females were sought at least four times). Birds detected 

from the air were subsequently located from the ground to determine brood fate. Aerial 

searches were conducted along the mainland and barrier islands in the Industrial Area 

only, as no Common Eiders were equipped with radios in the Control Area in 2001. 

When a female was located, we determined whether the female had young and if so, 

how many. Females not detected from the air were assumed to have lost their brood. 

Our estimate of duckling survival rates is based on all visits combined over the entire 

study. 
93

Table 11-1. Number of nests found by species and study area in 2000-2002. 

Number of nests found 

Species Control Area Industrial Area 

2000 2001 20021

 2000 20012 20022

 

Common Eider 228 92 52 (33) 218 110 (134) 56 (67) 

King Eider 1 0 0 6 2 0 (1) 

Unknown Eider3 0 49 28 (6) 17 58 (17) 58 (26) 

Long-tailed Duck 2 0 0 2 0 0 

Northern Pintail 4 0 0 0 0 0 

Black Brant 0 0 0 4 5 (3) 2 

Canada Goose 2 0 0 0 0 0 

White-fronted Goose 0 0 0 0 1 0 

Arctic Tern 0 2 0 3 2 1 

Glaucous Gull 19 12 0 47 31 (60) 12 (16) 

Total 256 155 80 (39) 297 209 (214) 129 (110) 

1

In the Control Area, the search area increased in 2002 to include five, small, sand spit 

islands adjacent to the mainland. The numbers of additional nests found in the 

expanded area are enclosed in parentheses. 2

In the Industrial Area, the search area increased in 2001 to include East Stump Island. 

The numbers of additional nests found in the expanded area are enclosed in 

parentheses. 3

Indicates nests where no eggs or females were observed to help differentiate between 

eider species. Based on habitat characteristics, the majority of these nests are 

likely Common Eider. 

RESULTS 

Through the course of this study, we located a total of 1489 waterbird nests. Of these, 

1259 (84%) were eider nests. Among eider nests, less than 1% (n=10) were King Eider 

(Somateria spectabilis), all others were Common Eider (n=990, 79%) or unidentified, 

though likely Common Eider (n=259, 20%). A summary of nest counts by species and 

year is given in Table 11-1. Each year since the start of the study we have increased 

the nest search area. In the Control Area, we added the eastern portion of Stump 

Island to our search in 2001 and 2002. In the Industrial Area, we added five, small, 

sand spit islands adjacent to the mainland near Point Thomson in 2002. Although we 

did not systematically search for nests on these sand spit islands prior to 2002, we 

believe it was the only year during our study in which eiders nested on them; ice breakup was delayed in this year and these islands provided the only nesting habitat with 

adjacent open water. We report nest counts for areas searched in all years separately 

from areas added in subsequent years (Table 11-1). When we examine only the areas 

searched in all three years, we see a steady decrease in the total number of eider nests 

found in each year of the study (2000: 470, 2001: 301, 2002: 194). Similarly, the 

number of Glaucous Gull (Larus hyperboresus) nests has decreased since 2000 (2000: 

66, 2001: 43, 2002: 12). 
94

Of nest site characteristics that we measured (including distance to water, height above 

water, driftwood density and diameter, and cover height toward the prevailing wind), 

only cover height shows a significant pattern across years or areas. Cover height has 

increased across years (F2,1254=26.3, P<0.001), however this seems unlikely to be 

biologically significant. 

In 2002 only, the distribution of nest initiation dates differed significantly between the 

study areas (Wilcoxon Two-Sample Test = 4904, two-sided t approximation P<0.05) 

with the initiation period earlier and more spread out in the Industrial Area than the 

2000

170 180 190 200 210

Number of Nests

0

5

10

15

20

25

30

2001

170 180 190 200 210

Number of Nests

0

5

10

15

20

25

30

2002

Julian Date

170 180 190 200 210

Number of Nests

0

5

10

15

20

25

30

2000

170 180 190 200 210

0

5

10

15

20

25

30

2001

170 180 190 200 210

0

5

10

15

20

25

30

2002

Julian Date

170 180 190 200 210

0

5

10

15

20

25

30

Control Area Industrial Area

Median = 28 Jun Median = 26 Jun

Median = 26 Jun Median = 24 Jun

Median = 3 Jul Median = 29 Jun

Figure 11-1. Distribution of Common Eider nest initiation dates on the Control and Industrial Areas in 

2000-2002. 
95

Control Area (Figure 11-1). Initiation dates in 2002 were substantially later than 

previous years, likely due to the particularly late ice breakup (median initiation date: 

2002: 1 July, 2001: 24 June, 2000: 27 June). In all years, mean clutch size declined 

with nest initiation date in (2002: 0.05 ± 0.02 eggs per day, 2001: 0.04± 0.01, 2000: 

0.10± 0.02). Average clutch size showed a significant year by area interaction 

(F2,688=16.35, P<0.001), with clutch size decreasing across years in the Industrial Area 

and increasing in the Control Area. 

In all years, the distribution of Common Eider nests was not uniform throughout the 

study areas (Figures 11-2). Both study areas had small areas of high nesting densities, 

especially on Spy, Egg, Stump, and Duchess Islands. In all three years, the 

concentration of Glaucous Gull nests was greater in the Industrial Area and tended to 

occur where large colonies of eiders were present. In 2002, we located no gull nests in 

the Control Area. This is likely due to the overall lower nesting effort seen in 2002 

(Table 11-1). 

Common Eider hatch success, though substantially variable among years, has been 

particularly low in our study areas (1-31%, Table 11-2). The primary cause of nest 

failure has been predation by Arctic foxes (Alopex lagopus) and Glaucous Gulls 

Figure 11-2. Locations of Common Eider nests on the Control and Industrial Areas in 2000-2002. 
96

(accounting for 58-83% of nest loss). The complete nest failure in the Industrial Area in 

2001 and the substantial failure in the Control Area in 2002 were due to fox predation. 

On 3 July 2002 we observed a fox on West Egg Island. In less than four hours, the 

single fox, followed closely by many gulls, destroyed every active eider (n=29) and gull 

(n=6) nest on the island. Stochastic weather related events have also played an 

important role in nest failure. For example, drifting sand buried 6% of nests in 2000 and 

5% of nests in 2002 were flooded. 

Table 11-2. Estimates of nesting success for Common Eiders in 2000-2002. 

Control Area Industrial Area 

2000 2001 2002 2000 2001 2002 

Number of nests 193 67 85 190 220 123 

Exposure Days 2385 501 619 2803 2623 1087 

No. Hatched 48 0 21 47 90 12 

Daily Survival Rate 0.9392 0.8663 0.8966 0.9489 0.9622 0.8979 

Hatch success 0.1523 0.0135 0.0379 0.2078 0.3152 0.0395 

95% CI 0.11-0.20 0.005-0.04 0.014-0.081 0.15-0.27 0.25-.40 0.019-0.073 

Since 2000, we have captured and marked a total of 209 nesting female Common 

Eiders. Due to particularly poor nesting success in 2001 and 2002, we have only 

recaptured 13 of these birds. Mean female weight ( x=1732±68 [SE]) has not varied 

among years or areas. In 2002, two recaptures were females nesting on the sand spit 

islands near Point Thomson. Those hens were originally captured in 2000, while 

nesting on the Maguire Islands. Our banding effort is summarized in Table 11-3. 

Table 11-3. Banding effort for female Common Eiders in 2000-2002. 

Control Area Industrial Area 

2000 2001 2002 2000 2001 2002 

Total Captures 47 0 30 66 53 131

First time Captures 47 0 27 66 46 10 

Recaptures - 0 3 - 7 3 

1

 Additionally, we banded one nesting King Eider 

In 2000 and 2001, we fitted 65 females with subcutaneously anchored VHF transmitters 

in the Industrial Area (2000: 30; 2001: 35) and 31 females in the Control Area (2000 

only, due to total nest failure in 2001). Subsequently, we followed those females to 

assess fledging success of broods. Because of difficulties with ground-based tracking 

in 2000, we were only able follow broods in the Control Area, only one brood survived 

as late as 12 August. In 2001, we conducted five aerial searches for radio-marked 

females (6 August, 14 August, 15 August, 8 September, and 26 September) on the 
97

Industrial Area. We confirmed brood failure for all but two marked females; the fates of 

those two broods could not be reliably classified. It is most likely that they failed as well. 

DISCUSSION 

Nesting Effort 

From 2000 to 2002, we observed a steady decline in nesting effort, with particularly low 

nest numbers in 2002. The reduction in effort between 2000 and 2001 was likely 

influenced by a major storm event in August 2000. The storm significantly degraded 

nesting habitat by removing and concentrating the driftwood that eiders use for nest 

cover. Late ice breakup in 2002 likely contributed to the further diminished nesting 

effort in 2002. Based on long-term nest census data, it does appear that substantial 

variation in nesting effort is normal on the barrier islands of the Beaufort Sea (Noel et al 

2002). Unfortunately, variability among previous studies has made conclusions about 

nesting effort difficult. In our study, the methods, duration, and overall effort of nest 

search has been the same across years, thus we do not have the problem of 

inconsistent data. In other nest search studies a correlation between low nesting 

success and the probability of nest detection has been noted (Flint, unpubl. data). 

Nests destroyed prior to their first discovery, can be difficult to detect and identify as a 

nest from the current year. Nonetheless, we are confident that there was indeed a 

reduced nesting effort in 2002. 

Nest Initiation and Clutch Size 

Nest initiation has been slightly earlier and of a longer duration in the Industrial Area in 

all three years. Ice breakup has been progressively later through the course of this 

study, with 2002 being particularly late. Consequently, median initiation date was 

delayed in 2002 and fewer eiders nested (Figure 11-1). During this study, clutch size 

has decreased in the Industrial Area while increasing in the Control Area. The trend is 

consistent with an effect of industrial development, although it may be a result of natural 

differences (e.g., forage availability) across areas and years. Additional data are 

needed to test this hypothesis. 

Nest Success 

During the three years of this study we have seen dramatic variation in daily and nest 

survival rates (Table 11-2). Our highest hatch success estimates are comparable to 

historic estimates from Beaufort Sea barrier islands (19.3% on Thetis Island in 1983, 

Johnson 1984; 33% on Egg Island in 1972, Schamel 1977). Our low estimates have 

been due largely to catastrophic nest predation by Arctic foxes. Arctic foxes have been 

implicated in total nest failure on several barrier islands through the years (Johnson and 

Richardson 1980, Noel et al 2002). While the ice pack is intact foxes can access 

islands, but typically, as the ice pack begins to break up foxes leave the islands. In 

years of late breakup, however, eiders may initiate nests while foxes still have access to 

islands. Artic foxes will eat or cache any egg they discover, and one or two foxes can 
98

readily destroy all nests on an island (or island complex). We have observed gulls 

following a fox during a predation event and depredating eggs as hens flushed. Clearly, 

the timing of breakup, the presence of foxes, and other natural events all affect the 

nesting success on each island differently each year. Nonetheless, increased nest 

predation is a possible indirect effect of industrial development. Fox populations have 

likely increased in the oilfields (Burgess 2000), due to improved den sites and 

scavenging opportunities. Glaucous Gull population trends remain unclear, however we 

have documented greater numbers of gull nests in the Industrial Area throughout this 

study. Increased numbers of predators in the oilfields is likely to increase the probability 

of predators accessing the adjacent islands. 

Brood Success 

Our study of duckling survival in 2000 and 2001 was compromised by methodological 

problems. Most significantly, persistent fog and wind limited our opportunities to track 

radio-equipped females from both the air and ground. Additionally, the complete nest 

failure in the Control Area in 2001 limited the number of females that we could follow. 

Nonetheless, we found exceptionally low fledging success through the course of the 

study. In 2000, only one of 31 Control Area broods survived as late as 12 August. In 

2001, of 21 Control Area broods, 19 were confirmed failures and the remaining two 

were suspected failures. The evidence of colossal brood failure is supported (at least 

anecdotally) by field observations in 2000-2002. Each year we encounter broods 

shortly after peak hatch, however by the time ducklings should be fledging we no longer 

encounter broods. We have no direct data explaining duckling loss, however two 

ducklings collected at Northstar in 2000 were infected with a reovirus similar to one 

implicated in a massive die-off of Common Eider ducklings in Finland (see Chapter 12). 

Adult Survival and Site Fidelity 

Nest predation and low nesting effort in 2001 and 2002 have limited our ability to 

capture and recapture a sufficient number of nesting adult females to make reasonable 

estimates of adult survival and site fidelity. With one or two more seasons of capture 

effort we could estimate this important population parameter. In 2002, we made the 

important discovery of nesting Common Eiders on the sand spit islands near the 

mainland around Point Thomson. Of particular interest was the confirmation (from 

recapture data) that at least two of these females nested previous on barrier islands 

(Duchess Island in 2000). Ice breakup patterns are highly variable; in 2002 breakup 

was particularly delayed and these sand spit islands provided the only nesting habitat 

with adjacent open water. We believe that the low nesting effort seen in 2002 was a 

result of females choosing not to nest on ice-bound barrier islands. Non-breeding in 

response to ice or other environmental factors has been documented in other eider 

populations (Bird and Bird 1940, Coulson 1984). That many eiders used the sand spit 

islands in 2002 suggests that alternative habitats can be important for nesting Common 

Eiders when environmental conditions prevent nesting in preferred habitats. The 

apparent lower nesting effort in recent years of delayed break-up may be associated 
99

with displacement of birds to alternative nesting areas. Whether displaced females will 

return in subsequent years, or show fidelity to the new nesting areas, is unknown. 

Population Model 

To properly develop a population model we need estimates of both fledgling production 

and adult survival. As indicated above, additional seasons of mark-recapture data are 

needed for survival estimates. 

Status of Breeding Common Eiders in the Beaufort Sea 

During the three years of this study, Common Eiders breeding in our study area have 

shown exceptionally poor success in all components of production (i.e., nesting effort, 

clutch size, hatch success, and fledging success). In contrast, Pacific Common Eiders 

breeding at two sites on the Yukon-Kuskokwim Delta (YKD), Alaska had consistently 

higher productivity (H. Wilson, unpub. data). Average clutch sizes on YKD sites, in 

2002, were 5.2 and 4.9, or approximately two eggs/nest more than average clutch sizes 

in our study. Hatch success estimates on the YKD sites were 53.7% and 45.2%. 

During our study hatch success estimates have ranged from 31.5% to 1.4% (3.8% in 

2002). In a 1997 study, fledging success was 27.1% on the YKD (Flint et al. 1998). 

North Slope brood success has been essentially zero during our study. Using these 

data to calculate a rough index of productivity, we estimate that production on the YKD 

ranges from 0.63 to 0.73 fledglings/nest/year. In contrast, using maximum estimates, 

North Slope production is 0.09 fledglings/nest/year. 

Common Eiders are long-lived birds that breed and over-winter in particularly harsh and 

variable environments. It is certainly possible that this study has spanned a particularly 

poor breeding period. Only through additional years of study could we determine this. 

Regardless, our data strongly suggest a need to be concerned for the future of this 

population. 
100

Chapter 12 

Isolation and Characterization of a Reovirus from Nesting Common Eiders 

INTRODUCTION 

The reasons for Common Eider population declines are not well understood. As part of 

our study of the breeding ecology of Common Eiders in the Beaufort Sea, we initiated 

research into viral diseases of eiders in 2000. We collected serum samples from 

nesting Common Eiders and conducted postmortem studies on carcasses to determine 

causes of death. In 2000, a mortality event was documented in molting Long-tailed 

Ducks (see Chapter 7), and two Common Eider ducklings were found dead at our study 

areas. 

Apart from serologic evidence of exposure to the infectious bursal disease virus or a 

related virus (Hollmén et al. 2000), little is known about the prevalence of viruses in 

eiders in Alaska or potential effects of viral diseases on eider duckling survival. 

Reoviruses have been linked to mortality of Common Eider ducklings in the Baltic Sea 

(Hollmén et al. 2002), but little is otherwise known of viral diseases of free-ranging eider 

ducklings. Tissues were collected from the two eider duckling carcasses and evaluated 

for the presence and potential involvement of viruses in duckling mortality in the 

Beaufort Sea nesting areas. We also tested serum samples from Common Eider hens 

to evaluate the prevalence of isolated viruses in the local breeding population. 

METHODS 

To screen for diseases and to determine causes of mortality in the population, 

necropsies were conducted on eider carcasses located at our study areas during the 

course of the breeding ecology study. Carcasses were weighed, gross lesions 

recorded, and tissues collected for further laboratory analyses. Specimens of liver, 

spleen, heart, lung, kidney, small intestine, large intestine, pancreas, bursa of Fabricius, 

and thymus were preserved in 10% buffered formalin and processed for histopathology 

evaluation. Approximately 1 g of brain, intestine, liver, lung, spleen, and bursa of 

Fabricius were collected for virus isolation. In the laboratory, the samples were 

homogenized in media and the homogenates were centrifuged at 800 × g for 30 min at 

4°C. Virus isolation was attempted in specific pathogen-free chicken embryos (Spafas® 

Inc., Preston, CT) and Muscovy Duck (Cairina moschata) embryo fibroblasts (MSDEF). 

Supernatants from the homogenized samples of intestine, liver, and lung were filtered 

through a cellulose acetate membrane filter (Corning Glass Works, Corning, NY) before 

the inoculations. Samples of all supernatants were inoculated separately into MSDEF 

monolayers, and subsamples of supernatants of brain, intestine, and lung suspensions 

were also inoculated into the chorioallantoic chamber of 9-day-old embryonated chicken 

eggs. Embryonic viability was monitored daily for 5 days. Eggs were removed from the 

incubator and refrigerated at 4°C when embryo mortality was observed and all eggs 

remaining after 5 days of incubation were refrigerated at 4°C. Chorioallantoic fluid from 

each egg was tested for the presence of viruses with hemagglutination activity using 

chicken red blood cells. The cell cultures were examined every other day for 7 days for 
101

viral cytopathic effect (CPE) using an inverted stereoscopic microscope. After 7 days 

(or when viral CPE had destroyed 100% of the cell monolayer) the flasks were frozen at 

-80°C. Cell cultures that were negative for CPE were thawed, harvested, and blind 

passaged twice before being diagnosed negative. 

Virus isolates were tested with 5-iodo-2’-deoxyuridine and chloroform treatments to 

evaluate their nucleic acid type and presence of a lipid envelope (Feldman and Wang 

1961, Docherty et al. 1994). A constant virus varying serum neutralization assay 

(Thayer and Beard 1998) was performed on a 96-well microplate with reference 

antisera to avian arthritis reovirus (Spafas® Inc., Preston, CT) using MSDEF as an 

indicator system. 

To determine the prevalence of neutralizing antibodies in adult hens to the reovirus that 

was isolated from the ducklings, a virus neutralization assay was performed in a 96-well 

plate according to standard procedures (Thayer and Beard 1998) using MSDEF as an 

indicator system. The serum samples were heated at 56°C for 30 min before use and 

run in duplicates. The titer of the serum was determined as the highest dilution at which 

viral activity was inhibited on cell monolayers of both test wells. Titers of 1:128 or 

greater were considered positive (Giambrone 1980). 

RESULTS 

Two Common Eider ducklings were found dead on August 3, 2000 on the Northstar 

Unit. Both were females in poor body condition and weighed 110 and 116 grams. One 

of the ducklings had multifocal hemorrhages of the liver and microscopic evidence of 

liver necrosis. 

No embryonic mortality was observed in the chicken eggs inoculated with tissue 

homogenates, and the chorioallantoic fluids from all embryos were negative in the 

hemaglutination test. We observed CPE and syncytia formation on the third day of 

incubation after the first blind passage in MSDEF inoculated with bursal samples. The 

isolated viruses were resistant to 5-iodo-2’-deoxyuridine and chloroform treatments and 

were characterized as nonenveloped RNA-viruses. Tissue culture fluids from positive 

flasks were inoculated into the chorioallantoic chambers of 9-day-old embryonated 

chicken eggs. All embryos died 4 or 5 days post inoculation but chorioallantoic fluids 

were negative in the hemaglutination test. The particles showed icosahedral symmetry 

by electron microscopy, consistent with the family Reoviridae (McNulty 1993). The virus 

was stable at pH 3.0 indicating that it belongs to the genus Orthoreovirus (McNulty 

1993). Growth of the virus in cell cultures was not inhibited by avian arthritis reovirus 

antiserum. 

Serum samples were collected from 20 Common Eider hens nesting in the Industrial 

Area and 19 hens in the Control Area. Antibodies to the duckling reovirus were 

detected in adult females at both study areas, with a prevalence of 20% in the Industrial 

Area and 16% in Control Area. 
102

DISCUSSION 

The virus isolated from Common Eider ducklings was compatible with the genus 

Orthoreovirus. The virus did not hemaglutinate chicken red blood cells, which is 

characteristic of most avian reovirus isolates, in contrast to the mammalian reoviruses 

(McNulty 1993). The eider reovirus probably represents a different subtype of avian 

reovirus than that found in domestic poultry, based on the lack of serological crossreactivity with the avian arthritis virus of poultry origin. 

Reoviruses have been found in chickens and turkeys worldwide and may also be 

prevalent in other avian species (McNulty 1993, van der Heide 2000). Reoviruses have 

been associated with a variety of disease conditions, including arthrititis/tenosynovitis, 

growth retardation, pericarditis, myocarditis, hydropericarditis, enteritis, hepatitis, bursal 

and thymic atrophy, osteoporosis, respiratory syndromes, and sudden death (Robertson 

and Wilcox 1986, McNulty 1993, Rosenberger and Olson 1997). Furthermore, 

reoviruses have been associated with immune system compromise (Rinehart and 

Rosenberger 1983, Montgomery et al. 1986), and the pathogenicity of some reoviruses 

has been shown to increase as a result of co-infection by parasites (Ruff and 

Rosenberger 1985a, Ruff and Rosenberger 1985b). Reoviruses have been reported to 

cause severe disease with high morbidity and up to 50% mortality in young Muscovy 

Ducks (Malkinson et al. 1981, Heffels-Redmann et al. 1992) and they also have been 

associated with mortality in wild birds, including Common Eiders (Docherty et al. 1994, 

Hollmén et al. 2002). 

Our postmortem findings of poor body condition and necrosis of the liver in viruspositive ducklings suggest similarities with reovirus infections in chickens and Muscovy 

Ducks and those described for Common Eider ducklings in the Baltic Sea (Malkinson et 

al. 1981, Heffels-Redmann et al. 1992, Rosenberger and Olson 1997, Hollmén et al. 

2002). Therefore, we suggest that the virus found in eiders could affect duckling 

survival either directly or through immunosuppressive effects. The results of the 

seroprevalence studies indicate that at least 16-20% of Common Eiders nesting at our 

study areas in the Beaufort Sea have been exposed to this or a closely related virus. 

One interpretation of our data is that some fraction of the adult females are carriers of 

the virus, which is then transmitted to their ducklings. Common Eiders may be 

particularly succeptable to virus transmission from adults to ducklings because of their 

brood rearing behavior. Common Eiders typically form creches shortly after hatch. 

These creches are frequently attended by multiple females, some of whom may not 

have any offspring in the creche. Thus, even though only a small percentage of the 

adult females appear to have been exposed to the virus, a large number of offspring 

could be exposed to a virus from a single female. Additionally, transient females may 

move among creches and serve as a vector for the spread of the virus. Thus, viral 

infections could affect demographics by lowering offspring survival, but further research 

on pathogenicity and epidemiology will be needed to evaluate the significance of 

neonatal reoviruses on Common Eider populations in Alaska. 
103

Chapter 13 

Trace Elements and Persistent Organic Pollutants in Common Eiders 

INTRODUCTION 

Chlorinated hydrocarbons and trace metals are known to be widely distributed in arctic 

and subarctic ecosystems that have previously been considered pristine (Anthony et al. 

1999, Henny et al. 1995, Hargrave et al. 1992). Organochlorine compounds are 

resistant to degradation and are distributed to the poles through the atmosphere after 

evaporation or sublimation from points of release, aided by heat currents from 

equatorial regions (Ottar 1981). Metals enter the marine environment from natural 

geologic sources and from discharges of anthropogenic sources. In marine 

ecosystems, the highest concentrations of contaminants occur in coastal regions 

(Harrison and Peak 1995), where trace elements and organics bind to and accumulate 

in sediments. The availability and uptake of persistent contaminants from sediments by 

forage species can lead to bioaccumulation, a species-dependent process associated 

with feeding ecology, potentially resulting in toxicity (Barron 1995). 

Contaminants may also enter arctic environments through releases associated with 

mining and the use and transport of oil and gas resources. Several monitoring studies 

have reported the presence of contaminants in the Beaufort Sea. Findings of the 

Minerals Management Service’s Beaufort Sea Monitoring Program indicated that 

concentrations of some trace elements, particularly barium and chromium, as well as 

levels of saturated and aromatic hydrocarbons, were higher in sediments from the 

Beaufort Sea in comparison to other outer continental shelf sediments (Beohm et al. 

1990). Concentrations of polycyclic aromatic hydrocarbons (PAHs) in Beaufort Sea 

sediments are higher than expected for a pristine area, making the region particularly 

sensitive to additional anthropogenic inputs of PAHs (Yunker and MacDonald 1995). 

The National Oceanic and Atmospheric Administration’s National Status and Trends 

Program found that sediment concentrations of arsenic, mercury, nickel, and silver were 

higher at Oliktok Point on the Beaufort Sea than at a reference area, Lutak Inlet in 

southeast Alaska (Meador et al. 1994). In the Industrial Beaufort Sea, concentrations of 

arsenic and nickel were higher than the average for U.S. sediments and the occurrence 

of certain naphthalenes indicated the presence of crude oil (Valette-Silver et al. 1997). 

Although regulations requiring the use of nontoxic shot for hunting waterfowl have been 

in effect at scattered locations in the United States since the 1970s and were instituted 

nationwide in 1991, waterfowl continue to be exposed to lead shot in some areas, 

including Alaska. Lead poisoning was first diagnosed in a Spectacled Eider (Somateria 

fischeri) found dead on the Yukon-Kuskokwim Delta (YKD) in 1992 (Franson et al. 

1995). Since that time, lead exposure and poisoning has been reported in additional 

studies of eiders (Flint and Grand 1997, Flint et al. 1997). The factors contributing to 

reduced long-term survival of waterfowl exposed to lead may be those that are sublethal 

in the short term, such as anemia and other hematologic effects, reduced body 

condition, and immunosuppression (Pain 1989, Hohman et al. 1990, Rocke and Samuel 

1991). 
104

Selenium is an essential trace element for birds and other animals, but it 

bioaccumulates through the food chain and can be toxic at higher exposure levels 

(Eisler 2000). Surveys indicate that high selenium concentrations occur in sediments 

and biota of the marine environment of Alaska. Selenium concentrations in sediments 

at remote coastal Alaska sites, including Oliktok Point on the Beaufort Sea, are higher 

than at many other sampling sites along the Pacific coast from southern California 

northward (Meador et al. 1994). High selenium concentrations also have been found in 

tissues of waterfowl, including Spectacled and Steller’s Eiders (Polysticta stelleri), in 

Alaska (Trust et al. 2000, Stout et al. 2002). Mean selenium concentrations of up to 

19.3 ppm and 7.3 ppm wet weight were found in blood of Spectacled Eiders and 

Common Eiders, respectively, sampled on nesting grounds in the YKD (Grand et al. 

2002). Steller’s Eiders captured at molting (Izembek Lagoon) and wintering (Dutch 

Harbor) areas in Alaska had mean selenium concentrations of 5-10 ppm wet weight in 

their blood (Franson, unpublished data). Tissue concentrations of mercury in these 

studies were low, thus the potential counteractive effect of mercury (Cuvin-Aralar and 

Furness 1991) was absent, making the high selenium levels all the more significant. 

Selenium exposure may cause oxidative stress, leading to adverse effects on 

immunocompetence, inflammation, and other defense systems (Sies 1985). 

Our objectives were to measure and compare contaminants in blood and eggs of 

Common Eiders nesting in our Control and Industrial Areas. Few studies of trace 

elements in blood of sea ducks are available, particularly from this area of Alaska, and 

little has been published about organic contaminants in eggs of birds nesting in Alaska, 

apart from a survey of seabirds done in the 1970s (Ohlendorf et al. 1982) and a report 

of contaminants in eggs of Bald Eagles (Haliaeetus leucocephalus) collected from the 

Aleutian archipelago in the 1990s (Anthony et al. 1999). 

METHODS 

In 2000, whole blood samples for trace element analysis were collected from 20 female 

Common Eiders at each of the two study areas (Control and Industrial). Samples were 

frozen in the field in a liquid nitrogen vapor shipper and transported to the National 

Wildlife Health Center, where they were stored at –75°C until shipment to RTI 

International, Research Triangle Park, NC, for analysis. The following trace elements 

were tested: Al, As, B, Ba, Be, Cd, Cr, Cu, Fe, Hg, Mg, Mn, Mo, Ni, Pb, Se, Sr, V, and 

Zn. Blood samples were weighed, freeze dried (percent moisture determined by the 

difference in the wet and dry weights), and digested in nitric acid. Analysis was done by 

inductively coupled plasma/mass spectrometry (ICP/MS), except for mercury, which 

was analyzed by cold vapor atomic absorption (CVAA) and selenium by graphite 

furnace/atomic absorption spectrophotometry (GF/AAS). The activity of deltaaminolevulinic acid dehydratase (ALAD), an enzyme inhibited by lead, was measured 

colorimetrically in whole blood (Burch and Siegel 1971). One unit of enzyme activity is 

defined as an increase in absorbance at 555 nm of 0.100, with a 1.0 cm light path, per 

ml of erythrocytes per hour, at 38°C. 
105

Also in 2000, eggs were collected from nests of 20 (n = 9 from the Control Area and n = 

11 from the Industrial Area) of the same females from which blood samples were 

collected. Eggs were wrapped in aluminum foil and kept cool until they were returned to 

the USGS National Wildlife Health Center (Madison, WI). The surface of each egg was 

washed with distilled deionized water, scored around the equator with a stainless steel 

scalpel blade, and the contents were emptied into pre-cleaned glass jars (I-Chem, 

Nalge Nunc International, Rochester, NY) and frozen at –20°C. Eggs were submitted to 

En Chem, Inc. (Madison, WI) for contaminants analysis by inductively-coupled 

plasma/mass spectrometry (ICP/MS) emission spectroscopy, cold vapor atomic 

absorption spectrometry (CVAA), and gas chromatography/mass spectrometry 

(GC/MS). Twenty eggs were individually analyzed for trace elements (Al, Sb, As, Ba, 

Be, Cd, Cr, Co, Cu, Fe, Pb, Mg, Mn, Hg, Ni, Se, Ag, Sr, Tl, V, and Zn), a suite of 30 

organochlorines, and total polychlorinated biphenyls (209 congeners). A subset of 10 

eggs was analyzed for 18 polycyclic aromatic hydrocarbons, and four eggs were 

composited for quality assurance/quality control spikes and duplicates. Kruskal-Wallis 

tests were used to compare medians of contaminant residues and ALAD activity 

between the Control and Industrial Areas. Spearman correlation was used to evaluate 

relationships between concentrations of Se and Hg in blood and eggs. We assigned a 

concentration of one-half of the lower limit of detection to the samples that contained no 

residues of a particular contaminant, as long as detectable residues were found in 50% 

or more of the samples. If residues of a particular contaminant were found in less than 

50% of the samples, no median was calculated and the data were not used in statistical 

comparisons. Residues in blood and eggs are reported on wet weight basis. Total 

polychlorinated biphenyls were corrected for percent recovery; results for the remaining 

analytes were not. 

RESULTS 

Nickel and Be were not detected in any of the eider blood samples. Aluminum, As, B, 

Ba, Cd, and Mo were detected in 2.5% to 95% of the eiders and all other trace elements 

were detected in 100% of the ducks (Tables 13-1, 13-2). Although median 

concentrations were low, blood Pb was significantly greater in the Control Area and Hg 

was significantly greater at the Industrial area (Table 13-2). The median ALAD activity 

was not significantly different (178 units at the Industrial area versus 168 at the Control 

area). The median Se concentration in the blood of the 40 Common Eiders was 6.87 

ppm wet weight. Neither Se, nor any of the remaining trace elements, differed between 

areas. Concentrations of Se and Hg in blood were not correlated (n = 40, r = -0.06, P = 

0.73). 
106

Table 13-1. Frequency of detection, median, and minimum-maximum 

concentrations (ppm wet weight) of 15 trace elements1 in blood of 

Common Eiders in the Control and Industrial Areas (n = 40). 

Trace element % detected2 Median Min-Max 

Al Aluminum 7.5 NC3 ND4-0.97 

As Arsenic 2.5 NC ND-0.07 

B Boron 30.0 NC ND-0.22 

Ba Barium 42.5 NC ND-0.04 

Cd Cadmium 95.0 0.006 ND-0.015 

Cr Chromium 100.0 0.19 0.10-0.44 

Cu Copper 100.0 0.38 0.29-0.60 

Fe Iron 100.0 416 322-675 

Mg Magnesium 100.0 61.8 52.6-104 

Mn Manganese 100.0 0.013 0.007-0.037 

Mo Molybdenum 87.5 0.009 ND-0.016 

Se Selenium 100.0 6.87 4.14-18.0 

Sr Strontium 100.0 0.06 0.03-0.57 

V Vanadium 100.0 0.06 0.04-0.14 

Zn Zinc 100.0 5.16 3.90-8.74 

1

Trace elements tested for, but not detected, were Be and Ni. 

2

The percentage of the samples in which the trace element was above the 

detection limit. 

3

Not calculated when trace element was detected in <50% of samples. 4

Not detected. 

Table 13-2. Median and minimum-maximum concentrations (ppm wet 

weight) of Lead (Pb) and Mercury (Hg) in blood of Common Eiders 

of the Control and Industrial Areas. Both were detected in all 

samples at each location and the concentrations were significantly 

different between locations (Kruskal-Wallis test; P = 0.0360 for Pb, P 

= 0.0265 for Hg). 

Location Pb Hg 

Control (n = 20) 

 Median 0.05 0.15 

 Min-Max 0.02-0.14 0.09-0.22 

Industrial (n = 20) 

 Median 0.04 0.17 

 Min-Max 0.02-0.10 0.12-0.25 
107

Table 13-3. Frequency of detection, median, and minimummaximum concentrations (ppm wet weight) of 14 trace 

elements1

 in eggs of Common Eiders in the Control and 

Industrial Areas (n=20). 

Trace element % detected2 Median Min-Max 

Al 95 0.87 ND3-2.30 

As 100 0.09 0.05-0.15 

Ba 100 0.33 0.21-1.30 

Cd 30 NC4 ND-0.02 

Cr 100 0.08 0.07-0.25 

Cu 100 1.20 0.67-16.0 

Fe 100 40.5 30.0-46.0 

Pb 35 NC ND-0.60 

Mg 100 125 86.0-160 

Hg 100 0.20 0.13-0.28 

Se 100 0.79 0.55-1.00 

Sr 100 9.10 3.90-19.0 

V 100 0.12 0.10-0.14 

Zn 100 16.0 12.0-31.0 

1

Trace elements tested for, but not detected, were Sb, Be, Co, Ag, 

and Tl. 

2

The percentage of the samples in which the trace element was 

above the detection limit. 

3

Not detected.

4

Not calculated when trace element was detected in <50% of 

samples. 

Table 13-4. Median and minimum-maximum concentrations (ppm 

wet weight) of Manganese (Mn) and Nickel (Ni) in eggs of 

Common Eiders of the Control and Industrial Areas. Mn 

was detected in all eggs and Ni was detected in all 11 from 

the Industrial Area and in 8 of 9 from the Control Area. 

Concentrations were significantly different between 

locations (Kruskal-Wallis test; P = 0.0479 for Mn, P = 

0.0183 for Ni). 

Location Mn Ni 

Control (n = 9) 

 Median 0.51 0.064 

 Min-Max 0.30-0.69 0.029-0.078 

Industrial (n = 11) 

 Median 0.66 0.077 

 Min-Max 0.44-1.00 0.063-0.180 
108

Table 13-5. Frequency of detection, median, and minimum-maximum 

concentrations (ppb wet weight) of 11 chlorinated hydrocarbon pesticides1

and total polychlorinated biphenyls (PCBs) in eggs of Common Eiders 

from the Control and Industrial Areas (n=20). 

Compound % detected2 Median Min-Max 

4,4’ DDE 100 7.45 2.80-17.0 

alpha-BHC 5 -3 2.2 

beta-BHC 90 5.85 ND4

-11.0 

cis-nonachlor 100 2.80 1.10-5.30 

dieldrin 100 2.35 1.20-4.10 

gamma-chlordane 5 - 2.2 

heptachlor epoxide 95 2.65 ND-6.80 

hexachlorobenzene 100 6.85 3.60-11.0 

mirex 5 - 1.5 

oxychlordane 100 3.35 1.90-6.60 

trans-nonachlor 100 4.05 1.80-8.40 

total PCBs 100 16.6 7.45-32.7 

1

Pesticides tested for, but not detected, were 2,4’-DDD, 2,4’-DDE, 2,4’-DDT, 4,4’-

DDD, 4,4’-DDT, aldrin, alpha-chlordane, delta BHC, endosulfan I and II, 

endosulfan sulfate, endrin, endrin aldehyde, endrin ketone, lindane, 

heptachlor, methoxychlor, pentachloroanisole, and toxaphene. 2

The percentage of the samples in which the trace element was above the 

detection limit. 

3

Compound was detected in one sample. 

4

Not detected. 

Table 13-6. Frequency of detection, median, and minimum-maximum 

concentrations (ppb wet weight) of nine polychlorinated aromatic 

hydrocarbons (PAHs)1

 in eggs of Common Eiders from the Control and 

Industrial Areas (n=10). 

Compound % detected2 Median Min-Max 

1-methylnaphthalene 100 0.36 0.24-0.56 

2-methylnaphthalene 100 0.48 0.36-0.64 

benz[a]anthracene 10 -3

 0.42 

benzo[a]pyrene 10 - 0.31 

benzo[b]fluoranthene 40 NC4 ND5

-0.36 

fluoranthene 50 0.24 ND-0.97 

naphthalene 100 0.88 0.68-1.10 

phenanthrene 70 0.44 ND-1.10 

pyrene 30 NC ND-0.54 

1

PAHs tested for, but not detected, were acenaphthene, acenaphthylene, 

anthracene, benzo[g,h,i]perylene, benzo[k]fluoranthene, chrysene, 

dibenz[a,h]anthracene, fluorene, and indeno[1,2,3-cd]pyrene. 2

The percentage of the samples in which the trace element was above the 

detection limit. 

3

Compound was detected in one sample. 

4

Not calculated when trace element was detected in <50% of samples. 5

Not detected. 
109

Silver, Be, Co, Sb, and Tl were not detected in any of the Common Eider eggs. 

Cadmium and Pb were found in about one-third of the eggs, Al and Ni in 95%, and the 

remaining trace elements were found in all eggs (Table 13-3). The median 

concentrations of Mn and Ni were greater in the Industrial Area (Table 13-4). Median 

Se concentrations in eggs did not differ between locations, and the overall median for 

20 eggs was 0.79 ppm wet weight. Concentrations of Se in eggs were not correlated 

with blood levels (n = 20, r = -0.16, P = 0.50), but concentrations of Hg in eggs were 

positively correlated with blood Hg (n = 20, r = 0.70, P = 0.0006). Concentrations of Se 

and Hg in eggs were not correlated (n = 20, r = 0.30, P = 0.20). Organochlorine 

pesticides detected in each egg included 4,4’-DDE, cis-nonachlor, dieldrin, 

hexachlorobenzene, oxychlordane, and trans-nonachlor (Table 13-5). Nineteen of the 

30 organochlorines that were tested for were not found in any of the eggs. PCBs and 

three of the PAHs (1- and 2-methylnaphthalene and naphthalene) were found in all 

eggs (Tables 13-5, 13-6). Nine of the PAHs tested for in eggs were below detection 

limits (Table 13-6). No differences in concentrations of persistent organic pollutants 

were detected between the two locations. 

DISCUSSION 

Except for Se, trace element concentrations in blood were relatively low. The median 

Se concentration (6.87 ppm wet weight) that we found in Common Eiders nesting in the 

Beaufort Sea is considerably higher than levels found in fresh water birds, and may be a 

natural finding in many marine species (Ohlendorf 1993, USDI 1998). Concentrations 

of Se in Common Eiders sampled in the Beaufort Sea were similar to those found in the 

blood of Common Eiders, but lower than those found in Spectacled Eiders, on the YKD 

during incubation and hatch (Grand et al. 2002). However, concentrations of Se in 

blood of Common Eiders from the Beaufort Sea were higher than concentrations 

reported in Common Eiders from Canada and Finland (Franson et al. 2000, Wayland et 

al. 2001). Blood Cd levels in our study were much lower than concentrations reported 

for Common Eiders in Canada, and Hg concentrations were somewhat lower than the 

Canadian study but similar to levels found in eiders from the YKD (Wayland et al. 2001, 

Grand et al. 2002). Mercury concentrations in eiders in our study were low enough at 

both areas, even though significantly greater in the Industrial Area, to reduce the 

potential of any protective effect against Se (Cuvin-Aralar and Furness 1991). Although 

the median concentration of Pb in the blood of Common Eiders was significantly greater 

in the Control Area, concentrations at both locations were well below 0.2 ppm wet 

weight, the commonly accepted threshold for lead exposure (Pain 1996). Furthermore, 

the blood ALAD activity, a sensitive indicator of lead exposure, was not significantly 

different between locations. Our results suggest that Common Eiders sampled at 

nesting areas in the Beaufort Sea have lower concentrations of lead in their blood than 

the levels that have been reported from sea ducks in some other areas of Alaska, such 

as the YKD (Flint et al. 1997, Grand et al. 2002). Concentrations of Fe, Mg, Sr, V, and 

Zn in blood of Common Eiders were similar to, or lower than, concentrations in Steller’s 

Eiders from the Alaska Peninsula (Franson, unpublished data). Concentrations of Cu 

and Mn in Common Eiders were low, but were greater than the levels in Steller’s Eiders. 
110

Based on the moisture content of the Common Eider eggs, 9.10 ppm wet weight of Sr 

(Table 13-3) is equivalent to 23.6 ppm dry weight. This is about twice as high as 

Cochrane and Trust (1996) reported in Spectacled Eider eggs from the YKD and about 

10 times higher than in eggs of Lesser Snow Geese (Anser c. caerulescens) from 

Wrangel Island, Russia (Hui et al. 1998). Custer et al. (2001) reported Sr of 14 and 23 

ppm dry weight in eggs of House Wrens (Troglodytes aedon) at two study sites in 

Wyoming, considering those levels to be elevated. According to Puls (1988), normal 

concentrations of Sr in chicken eggs are about 2.2 ppm dry weight. Selenium 

concentrations in eggs of Common Eiders nesting on the Beaufort Sea were somewhat 

higher than those found in eggs of Common Eiders in the Baltic Sea and in one report 

from Spectacled Eiders on the YKD, but were still below toxic levels (Heinz 1996, 

Franson et al. 2000, Grand et al. 2002). In another study of contaminants in Spectacled 

Eider eggs from the YKD, Cockrane and Trust (1996) reported a mean Se concentration 

of 3.55 ppm dry weight, which is about 50% higher than the median (0.79 ppm wet 

weight, or 2.18 ppm dry weight) that we found in Common Eider eggs. Although our 

results suggest that relatively little selenium is transferred from the blood of Common 

Eider females nesting in the Beaufort Sea to their eggs, the effects of selenium on sea 

duck physiology and reproduction deserves further study. The median Hg 

concentration in Common Eider eggs from the Beaufort Sea was somewhat higher than 

in previous reports from eiders, but below levels that have been reported to cause 

adverse effects in several species of birds (Cochrane and Trust 1996, Eisler 2000, 

Franson et al. 2000, Grand et al. 2002). 

Cadmium and Pb were detected at low levels in about one-third of the Common Eider 

eggs, which is consistent with reports indicating that eggs are not particularly useful for 

evaluating exposure of birds to these two trace elements (Ohlendorf 1993). Cadmium 

and Ni concentrations were lower in eggs of Common Eiders in the Beaufort Sea than in 

Common Eider eggs collected in Norway in 1972-1973 (Lande 1977). The maximum 

Cd concentration in Common Eider eggs was 0.02 ppm wet weight; normal levels in 

chicken egg yolk are reported to be 0.02 to 0.10 ppm wet weight (Puls 1988). 

Concentrations of As, Cu, Fe, Mg, and Zn were similar to those found in Spectacled 

Eider eggs from the YKD (Cochrane and Trust 1996, Grand et al. 2002). 

Concentrations of Mn in Common Eider eggs were slightly higher than Mn in Spectacled 

Eider eggs from the YKD, and similar to concentrations in Lesser Snow Goose eggs 

from Russia (Cochrane and Trust 1996, Hui et al. 1998). Concentrations of Mn and Ni 

in Common Eider eggs were more than 20% greater in the Industrial than the Control 

Area. 

Based on residues in eggs, we found evidence of exposure to a variety of persistent 

organic pollutants in Common Eiders nesting in the Beaufort Sea, which is consistent 

with literature reporting the global distribution of these compounds. However, the 

concentrations that we found in eider eggs were often below those reported in 

comparable studies and lower than levels generally considered harmful. For example, 

although DDE was detected in each egg, the median concentration of 7.45 ppb wet 

weight compares with 13-29 ppb in Common Eider eggs collected in the Gulf of Finland 
111

in the late 1990s, 230 ppb in Common Eider eggs collected off the coast of Maine in 

1977, and is well below levels that have affected even the most sensitive species 

(Szaro et al. 1979, Blus 1996, Franson et al. 2000). Similarly, Szaro et al. (1979) 

reported 100 times more total PCBs in Common Eider eggs from 1977 than we found in 

eider eggs from the Beaufort Sea (median = 16.6 ppb wet weight). In field studies with 

fish-eating birds, adverse effects were associated with total PCB concentrations that 

were, in most cases, well over 1 ppm (1000 ppb) (Hoffman et al. 1996). Mean 

concentrations of PCBs in eggs of several species of seabirds collected in Alaska in the 

mid-1970s ranged from 0.09-3.41 ppm (90-3410 ppb) (Ohlendorf et al. 1982). We 

found a greater percentage of Common Eider eggs with detectable residues of dieldrin, 

hepatchlor epoxide, oxychlordane, cis-nonachlor, and hexachlorobenzene residues than 

Ohlendorf et al. (1982) found in Alaska seabird eggs, but concentrations in eider eggs 

were generally lower. In the seabird egg study, Ohlendorf et al. (1982) found greater 

frequencies of detectable residues of DDD, DDT, mirex, and toxaphene than we did in 

eider eggs. 

Egg injection studies have demonstrated that PAHs are embryotoxic for birds, including 

Common Eiders. When a mixture of 18 PAHs was injected into Common Eider eggs at 

concentrations of 0.2 and 2.0 mg/kg of egg (i.e., 200 and 2000 ppb), the embryo 

mortality rate was 18% and 94%, respectively (Brunström et al. 1990). At the 200 ppb 

level, each of the 18 PAHs in the mixture was present in concentrations of about 9-13 

ppb. We tested Common Eider eggs for 11 of the 18 PAHs in the Brunström et al. 

(1990) mixture, finding detectable residues of five compounds in 10% to 70% of the 

eggs, with maximum concentrations of 0.31 ppb to 1.10 ppb. Brunström et al. (1991) 

reported the LD50 of the four most toxic PAHs that they tested in chicken embryos. Of 

those four, we analyzed eggs for three: benz[a]anthracene, dibenz[a,h]anthracene, and 

benzo(k)fluoranthene. One Common Eider egg had a detectable residue of 

benz[a]anthracene (0.42 ppb wet weight), but the other two compounds were not 

detected in any of the eggs. According to Brunström et al. (1991), the LD50 for 

benz[a]anthracene in chicken embryos is 79 ppb, about 188 times the concentration 

that we found in the eider egg. Common Tern (Sterna hirundo) eggs from the 

Netherlands had maximum concentrations of phenanthrene, fluoranthene, and pyrene 

of 7.0, 2.0, and 4.0 ppb wet weight, respectively and the authors considered those 

levels to be low (Stronkhorst et al. 1993). The maximum concentrations of 

phenanthrene, fluoranthene, and pyrene that we found in Common Eider eggs were 1.1, 

0.97, and 0.54 ppb wet weight. Brunström et al. (1991) observed no mortality in chicken 

embryos when those three PAHs were injected into eggs at a concentration of 0.3 ppm 

(300 ppb). 

Overall, concentrations of persistent organic pollutants in eggs of Common Eiders were 

low and there were no differences between the two locations. We found few differences 

in trace element concentrations in blood and eggs of Common Eiders between the two 

study areas. Although concentrations of Hg in blood, and Mn and Ni in eggs, were 

greater in the Industrial Area, and blood Pb was greater in the Control Area, 

concentrations at both locations were relatively low. Selenium was high in blood and 

low in eggs of Common Eiders, as has been previously reported in Spectacled Eiders. 
112

No location differences occurred for Sr, but Sr levels in eggs of Common Eiders were 

higher than those reported for Spectacled Eiders and Lesser Snow Geese. Little 

information on Sr levels in sea ducks is available. Concentrations of Sr in eggs of wild 

waterfowl that may be associated with adverse effects have not been determined. 
113

Chapter 14 

Synthesis of Aerial and Ground Based Studies of Nesting Common Eiders 

The dramatic decline in the Common Eider population apparently breeding across the 

arctic (Suydam et al. 2002) coupled with the particularly poor breeding success that we 

documented since 2000, strongly indicate the need for continued efforts to understand 

the population dynamics of this local population. Certainly our data suggest that 

consistent reproductive failure is likely contributing to the observed overall population 

decline. 

The relationship between aerial survey counts and ground based nest searches 

indicates that aerial surveys can be an efficient mechanism for estimating nesting effort 

over a large area. However, we caution that aerial survey data must be interpreted 

carefully. Aerial survey indices indicated increasing trends during the same period that 

our ground based indices indicated a population decline. The combination of aerial and 

ground based surveys appears to enhance the interpretation of both indices. Aerial 

surveys can show aspects of eider biology that ground based surveys cannot. The 

correlation of ice cover and total eider counts may show larger scale dynamics of 

Common Eider in the Beaufort Sea. Canadian breeding eiders may have briefly 

delayed their arrival in the Canadian Arctic or a concentration of Alaskan breeding 

eiders may have chosen to forgo nesting due to poor conditions on breeding grounds. 

Nesting effort, estimated by aerial surveys, single pass nest searches, or nest searches 

and monitoring, is an essential component of population dynamics. However, nesting 

effort alone does not provide a sufficient understanding of population changes or 

reproductive success. Timing of aerial surveys and single pass nest searches can 

significantly affect counts, especially when predation levels are high (as they were in our 

study area). 

Our results show that Common Eiders in our study area are doing poorly in all stages of 

breeding (i.e., low nesting effort, small clutch sizes, poor hatch success, and extremely 

poor fledging success). Nesting effort and clutch size are affected by initiation date, 

which is related to the timing of ice breakup. Thus, we expect improvements in these 

parameters in years with early breakup. Hatch success in our study is strongly 

influenced by predation, which, although stochastic in nature, may be indirectly linked to 

oilfield development via enhanced predator populations. We have no conclusive data to 

explain the poor fledging success of Common Eiders in our study. Due to their low 

levels in eggs, contaminants are not implicated. Gull predation certainly plays a role 

(our observers have seen a few ducklings eaten by gulls), but the extent is unknown, as 

the probability of seeing predation events is low even when predation is high. Disease 

(i.e., viruses) may be important, unfortunately the probability of encountering duckling 

carcasses is very low, especially in the presence of scavengers such as gulls, making it 

difficult to assess the relative mortality due to disease. Adult survival is a key parameter 

to population dynamics that, to date, we are unable to estimate. Only with additional 

years of mark-recapture can we estimate to this parameter. 
114

On its own, the local Common Eider population of the central Beaufort Sea can not 

continue to exist for long given the poor breeding success that we have documented 

since 2000. However, Common Eiders are long-lived, and therefore have multiple 

lifetime opportunities to breed. Accordingly, we see three possible scenarios that may 

explain the dynamics of this population. First, the study population may be declining as 

indicated by the migration counts. Second, this population is not self-sustaining and the 

population is maintained by recruitment from other populations. Third, this population 

may be maintained by infrequent years of high recruitment. That is, 2000-2002 have 

been poor years and these years of little or no recruitment will be offset by a series of 

years with high recruitment. Continued monitoring of this population will be necessary 

to distinguish between these three possible explanations. Nonetheless, there is cause 

for significant concern about the long-term viability of this population. 
115

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