OCS Study MMS 2005-055

University of Alaska Coastal Marine Institute

IN COOPERATION

Minerals Management Service

University of Alaska

State of Alaska

Annual Report No. 11

Federal Fiscal Year 2004

SUBMITTED BY

Vera Alexander

Director

University of Alaska Coastal Marine Institute

TO

U.S. Department of the Interior

Minerals Management Service

Alaska OCS Region

Anchorage, Alaska

November 2005
Contact information

email: cmi@sfos.uaf.edu

phone: 907.474.1811

fax: 907.474.1188

postal: Coastal Marine Institute

School of Fisheries and Ocean Sciences

University of Alaska Fairbanks

Fairbanks, AK 99775-7220
iii

Table of Contents

Introduction .................................................................................................................................................. 1

Correction Factor for Ringed Seal Surveys in Northern Alaska

[TO 15162]

Brendan P. Kelly ................................................................................................................................... 3

Circulation, Thermohaline Structure, and Cross-Shelf Transport in the Alaskan Beaufort Sea

[TO 15163]

Thomas J. Weingartner and Knut Aagaard ......................................................................................... 4

Alaska Sea Ice Atlas

[TO 15177]

Orson P. Smith and William J. Lee ...................................................................................................... 5

A Nowcast/Forecast Model for the Beaufort Sea Ice–Ocean–Oil Spill System (NFM-BSIOS)

[TO 15178]

Jia Wang and Meibing Jin .................................................................................................................... 6

Satellite Tracking of Eastern Chukchi Sea Beluga Whales in the Beaufort Sea and Arctic Ocean

[TO 15179]

Robert S. Suydam, Lloyd F. Lowry and Kathryn J. Frost ................................................................... 7

Timing and Re-Interpretation of Ringed Seal Surveys

[TO 15180]

Brendan P. Kelly, Oriana R. Harding, Mervi Kunnasrantaand John R. Moran, Jr. ......................... 8

Archiving of Shelikof Strait Sediment Samples at the University of Alaska Museum

[TO 15181]

A. Sathy Naidu and John J. Kelley ....................................................................................................... 9

CODAR in Alaska

[TO 74261]

Hank Statscewich and David L. Musgrave ........................................................................................ 10

Observations of Hydrography in Central Cook Inlet, Alaska, During Diurnal

and Semidiurnal Tidal Cycles

[TO 85243]

Stephen R. Okkonen ............................................................................................................................ 11
iv

Importance of the Alaskan Beaufort Sea to King Eiders (Somateria spectabilis)

[TO 15182]

Abby N. Powell, Laura M. Phillips, Eric A. Rexstad and Eric J. Taylor .......................................... 12

Population Structure of Common Eiders Nesting on Coastal Barrier Islands Adjacent

to Oil Facilities in the Beaufort Sea

[TO 85239]

Sarah A. Sonsthagen, Kevin G. McCracken, Richard B. Lanctot,

Sandra L. Talbot and Kim T. Scribner ............................................................................................... 23

Breeding Biology and Habitat Use of King Eiders on the Coastal Plain of Northern Alaska

[TO 85240]

Abby N. Powell, Rebecca A. McGuire and Robert S. Suydam .......................................................... 34

King and Common Eider Migrations Past Point Barrow

[TO 85241]

Lori T. Quakenbush and Robert S. Suydam ....................................................................................... 40

Susceptibility of Sea Ice Biota to Disturbances in the Shallow Beaufort Sea.

Phase 1: Biological Coupling of Sea Ice with the Pelagic and Benthic Realms

[TO 85242]

Rolf R. Gradinger and Bodil A. Bluhm .............................................................................................. 50

Role of Grazers on the Recolonization of Hard-Bottom Communities in the Alaska Beaufort Sea

[TO 85244]

Brenda Konar ..................................................................................................................................... 60

Water and Ice Dynamics in Cook Inlet

[TO 85308]

Mark A. Johnson, Stephen R. Okkonen and Andrey Y. Proshutinsky ............................................... 67

Foraging Ecology of Common Ravens (Corvus corax) on Alaska’s Coastal Plain

[TO 85294]

Abby N. Powell, Stacia A. Backensto ................................................................................................. 81

High-Resolution Numerical Modeling of Near-Surface Weather Conditions over

Alaska’s Cook Inlet and Shelikof Strait

[TO 73070]

Peter Q. Olsson.................................................................................................................................... 90

Trace Metals and Hydrocarbons in Sediments of Beaufort Lagoon, Northeast Arctic Alaska

[TO 74464]

A. Sathy Naidu, John J. Kelley and Debasmita Misra ...................................................................... 99
v

New Projects ............................................................................................................................................ 131

Satellite Tracking of Bowhead Whales: The Planning Process

[TO 35248]

Lori T. Quakenbush and Robert J. Small .................................................................................. 132

Workshop on Hydrological Modeling for Freshwater Discharge from

the Alaska Arctic Coast

[TO 35262]

Jia Wang and Meibing Jin ......................................................................................................... 137

Pre-Migratory Movements and Physiology of Shorebirds Staging on Alaska’s North Slope

[TO 35269]

Abby N. Powell, Audrey R. Taylor and Richard B. Lanctot ..................................................... 138

Sea Ice–Ocean–Oil Spill Modeling System (SIOMS) for the Nearshore Beaufort and

Chukchi Seas: Improvement and Parameterization (Phase II)

[TO 35407]

Jia Wang and Meibing Jin ......................................................................................................... 147

Evaluating a Potential Relict Arctic Invertebrate and Algal Community on the West Side

of Cook Inlet

[TO 37357]

Nora R. Foster, Dennis C. Lees and Susan M. Saupe .............................................................. 148

Seasonality of Boundary Conditions for Cook Inlet, Alaska

[TO 37628]

Stephen R. Okkonen, W. Scott Pegau and Susan M. Saupe ..................................................... 149

Funding Summary ................................................................................................................................... 150

University of Alaska CMI Publications .................................................................................................. 153
vi
1

Introduction

The University of Alaska Coastal Marine Institute (CMI) was created by a cooperative agreement

between the University of Alaska and the Minerals Management Service (MMS) in June 1993, with

the first full funding cycle beginning late in (federal) fiscal year 1994. CMI is pleased to present this

2004 Annual Report, our eleventh annual report and the second one under MMS Cooperative

Agreement 0102CA85294. Twenty-five research projects are covered, including abstracts for nine

projects in final report preparation (Kelly [TO 15162], Weingartner & Aagaard [TO 15163], Smith &

Lee [15177], Wang & Jin [15178], Suydam, Lowry & Frost [TO 15179], Kelly [TO 15180], Naidu &

Kelley [15181], Musgrave [74261], and Okkonen & Saupe [TO 85243]). Six of the 25 projects were

funded in FY2004 and are in the New Projects section. Four additional project final reports were

published in 2004 (Terschak, Henrichs & Shaw [TO 15170], Duesterloh & Shirley [TO 15171],

Winker & Rocque [TO 15173], and Braddock, Gannon & Rasley [TO 15175].

The Minerals Management Service administers the outer continental shelf (OCS) natural gas, oil, and

marine minerals program in which it oversees the safe and environmentally sound leasing, exploration,

and production of these resources within our nation’s offshore areas. The Environmental Studies

Program (ESP) was formally directed in 1978, under Section 20 of the OCS Lands Act Amendments,

to provide information in support of the decisions involved in the planning, leasing, and management

of exploration, development, and production activities. The research agenda is driven by the

identification of specific issues, concerns, or data gaps by federal decision makers and the state and

local governments that participate in the process. ESP research focuses on the following broad issues

associated with development of OCS gas, oil, and minerals:

• What are the fates and effects of potential OCS-related pollutants (e.g., oil, noise,

drilling muds and cuttings, products of fuel combustion) in the marine and coastal

environment and the atmosphere?

• What biological resources (e.g., fish populations) exist and which resources are at

risk? What is the nature and extent of the risk? What measures must be taken to

allow extraction to take place?

• How do OCS activities affect people in terms of jobs and the economy? What are

the direct and indirect effects on local culture? What are the psychological effects

of the proposed OCS activities?

Because MMS and individual states have distinct but complementary roles in the decision-making

process, reliable scientific information is needed by MMS, the state, and localities potentially affected

by OCS operations. In light of this, MMS has developed a locally managed CMI program. Under this

program, MMS takes advantage of highly-qualified scientific expertise at local levels in order to:

1. Collect and disseminate environmental information needed for OCS oil & gas

and marine minerals decisions;

2. Address local and regional OCS-related environmental and resource issues of

mutual interest; and

3. Strengthen the partnership between MMS and the state in addressing OCS

oil & gas and marine minerals information needs.

CMI is administered by the University of Alaska Fairbanks School of Fisheries and Ocean Sciences

to address some of these mutual concerns and share the cost of research. Alaska was selected as the

location for this CMI because it contains some of the major potential offshore oil and gas producing

areas in the United States. The University of Alaska Fairbanks is uniquely suited to participate by

virtue of its flagship status within the state and its nationally recognized marine and coastal expertise
2

relevant to the broad range of OCS program information needs. In addition, MMS and the University

of Alaska have worked cooperatively on ESP studies for many years. Research projects funded by

CMI are required to have at least one active University of Alaska investigator. Cooperative research

between the University of Alaska and state agency scientists is encouraged.

Framework Issues were developed during the formation of CMI to identify and bracket the concerns

to be addressed:

1. Scientific studies for better understanding marine, coastal, or human environments

affected or potentially affected by offshore oil & gas or other mineral exploration

and extraction on the outer continental shelf;

2. Modeling studies of environmental, social, economic, or cultural processes related

to OCS oil & gas activities in order to improve scientific predictive capabilities;

3. Experimental studies for better understanding of environmental processes or the

causes and effects of OCS activities;

4. Projects which design or establish mechanisms or protocols for sharing of data or

scientific information regarding marine or coastal resources or human activities to

support prudent management of oil & gas and marine mineral resources; and

5. Synthesis studies of scientific environmental or socioeconomic information

relevant to the OCS oil & gas program.

Projects funded through CMI are directed toward providing information which can be used by

MMS and the state for management decisions specifically relevant to MMS mission responsibilities.

Projects must be pertinent to either the OCS oil and gas program or the marine minerals mining

program. They should provide useful information for program management or for the scientific

understanding of potential environmental effects of resource development activities in arctic and

subarctic environments.

Initial guidelines given to prospective researchers identified Cook Inlet and Shelikof Strait, as well as

the Beaufort and Chukchi seas, as areas of chief concern to MMS and the state. Primary emphasis has

subsequently shifted to the Beaufort Sea, and to the Chukchi Sea as it relates to the Beaufort Sea.

However, a strong interest in Cook Inlet and Shelikof Strait remains.

The proposal process is initiated each summer with a request for letters of intent to address one or

more of the Framework Issues. This request is publicized and sent to researchers at the University of

Alaska and to various state agencies, and to relevant profit and non-profit corporations. The CMI

technical steering committee then decides which of the proposed letters of intent should be developed

into proposals for more detailed evaluation and possible funding.

Successful investigators are strongly encouraged to publish their results in peer-reviewed journals

as well as to present them at national meetings. In addition, investigators report their findings at the

CMI’s annual research review, held at UAF in February. Some investigators present information

directly to the public and MMS staff in seminars.

Alaskans benefit from the examination and increased understanding of those processes unique to

Alaskan OCS and coastal waters because this enhanced understanding can be applied to problems

other than oil, gas, and mineral extraction, such as subsistence fisheries and northern shipping.

Many of the CMI-funded projects address some combination of issues related to fisheries,

biomonitoring, physical oceanography, and the fates of oil. The ultimate intent of CMI-related

research is to identify the ways in which OCS-related activities may affect our environment, and

potential economic and social impacts as well.
3

Correction Factor for Ringed Seal Surveys in

Northern Alaska

Brendan P. Kelly <brendan.kelly@uas.alaska.edu>

School of Arts and Sciences

University of Alaska Southeast

11120 Glacier Highway

Juneau, AK 99801

Task Order 15162

Abstract

The proportion of radio-tagged ringed seals visible on the ice surface from April to June in 1999

(n = 8) and 2000 (n = 10) was used to estimate correction factors for aerial surveys. Radio tracking

proved effective for determining when seals were available to be counted; monitoring lair

temperatures was less effective for that purpose.

The transition period, defined as the period during which the majority (75%) of the tagged seals

began resting outside of lairs, was longer in 2000 (24 days) than it was in 1999 (7 days). The

midpoint of the transition period, the day by which 50% of the tagged seals began resting in the open,

was 31 May in both years. Only once each year was a lair used subsequent to each seal’s first

appearance outside of a lair. Changes in the number of seals counted during ground-based, visual

surveys of seals resting on the ice corresponded to changes in the number of radio-tagged seals

basking. Tagged seals spent approximately 20% of the time out of the water before appearing outside

of lairs and approximately 30% of the time out of the water after they began to abandon lairs. The

transition from lair use to resting in the open appeared related to measurable characteristics of the

snow, and backscatter radar, sensitive to the liquid moisture content of snow, offers promise for

remotely determining when seals have abandoned their subnivean lairs.

Aerial surveys underestimated actual ringed seal densities by factors ranging from 2.33 to >13

because the proportion of seals visible during the survey periods changed rapidly from day to day.

Interannual comparisons of seal densities based on aerial surveys have been further compromised by

a shift in survey dates from mid-June in the 1970s to late May in the 1990s. The proportion of seals

visible on the ice was more stable between 12:00 and 18:00 (Alaska Daylight Saving Time [ADT])

than between 10:00 and 16:00, the current standard for aerial surveys of ringed seals in Alaska.

In April, May, and early June, most radio-tagged ringed seals remained close to their capture and

release sites, with 88% of their home ranges measuring less than 500 ha in area. One seal, however,

had a home range of almost 600 ha and another of almost 3,000 ha.
4

Circulation, Thermohaline Structure, and Cross-Shelf

Transport in the Alaskan Beaufort Sea

Thomas J. Weingartner <weingart@ims.uaf.edu>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775-7220

Knut Aagaard <aagaard@apl.washington.edu>

Applied Physics Laboratory

University of Washington

1013 NE 40th Street

Seattle, WA 98105-6698

In collaboration with:

Taketoshi Takazawa

Japan Marine Science and Technology Center (JAMSTEC), Yokosuka, Japan

Eddy C. Carmack

Department of Fisheries and Oceans, Institute of Ocean Sciences, Sidney, British Columbia, Canada

The Canadian and Japanese partners are providing Task Order 15163

in-kind (matching) support to this project through ship time

(Japan and Canada) and instrumented moorings (Japan).

Abstract

This program collected hourly time series of ocean velocity, temperature, and salinity properties from

moored instruments deployed along the outer shelf and slope of the Alaskan Beaufort Sea for a period

of one year. The goals are to: 1) quantify the vertical and cross-shore spatial and temporal scales of

variability in the circulation and the density (thermohaline) field in this region and 2) estimate the

transport within the eastward flowing subsurface undercurrent. The flow and the density structure on

the outer shelf and slope affect the cross-shelf transfer of momentum, water properties (heat, salt,

nutrients, etc.), contaminants, and pollutants. The region is also an important migratory corridor for

marine mammals, particularly bowhead whales that feed here during part of the year. Previous

measurements showed that the near surface flow (< ~50 m depth) here, and over the inner shelf, is

westward and forced by the winds. However, flow reversals are common and often a result of

upwelling of the undercurrent. Further, the pressure field responsible for the undercurrent must

influence the dynamics of the inner shelf. The undercurrent originates in the eastern Arctic as a result

of inflow through Fram Strait and is fed by outflows from the Eurasian shelf seas and the Chukchi

Sea. Hence it is circumpolar in extent and carries with it a variety of water masses. The flow could

thus transport pollutants from these regions to the Alaskan shelf. The observations will provide

information crucial in guiding model development and evaluating the performance of pollution

transport models. The study site is practical (from the resource manager’s perspective and for

logistical reasons) and optimal from a scientific perspective, for measurements here will capture

the integrated effects of the circumpolar forcing which we believe force the undercurrent.
5

Alaska Sea Ice Atlas

Orson P. Smith <afops@uaa.alaska.edu>

William J. Lee <afwjl3@uaa.alaska.edu>

School of Engineering

University of Alaska Anchorage

3211 Providence Drive

Anchorage, AK 99508-8054

Task Order 15177

Abstract

A GIS-based atlas of sea ice conditions in the territorial waters of Alaska is in the final stages of

preparation. It updates previously printed ice atlases and provides risk analysis information for

engineers and resource managers. The Alaska Sea Ice Atlas includes a comprehensive collection of

georeferenced digital historical data on Alaska sea ice and other environmental factors that bear

directly on ice processes and conditions. Historical ice reports of the U.S. National Ice Center form

the foundation of the database of ice conditions. This information is supplemented by ice and related

climatological data from the U.S. National Weather Service and other archives. Areas of uniform ice

concentration, stage, and form are portrayed as polygons and superimposed on a 5-km-square grid.

Grid cell statistics over the period of record for each week of the calendar year include distribution

parameters, reported extremes, combined probabilities of concentration and stage, and related

atmospheric variables. Hindcast wind stress divergence is applied as an analog of ice compression

and ridge formation. These statistics allow derivation of a navigability index for assessing difficulties

in navigating ice-covered waters in various classes of vessels. The preliminary version of the Alaska

Sea Ice Atlas is accessible via a customized implementation of GIS tools at the public website

http://holmes-iv.engr.uaa.alaska.edu. The final version is scheduled for public access in March 2003.
6

A Nowcast/Forecast Model for the Beaufort Sea

Ice–Ocean–Oil Spill System (NFM-BSIOS)

Jia Wang <jwang@iarc.uaf.edu>

International Arctic Research Center–Frontier Research System for Global Change

University of Alaska Fairbanks

Fairbanks, AK 99775-7340

Meibing Jin <ffjm@uaf.edu>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775-7220

Task Order 15178

Abstract

A nested coupled ice–ocean model was developed under the CMI/MMS project titled, “A Nowcast/

Forecast Model for the Beaufort Sea Ice–Ocean–Oil Spill System (NFM-BSIOS).” A transportconserved scheme was used to pass information from the coarse 27.5 km resolution model to the

nested fine 3.4375-km resolution model. The fine-resolution ocean model was run for a seasonal

cycle, longer than would be needed for the operational purpose.

The high-resolution coupled ice–ocean model was validated using available observations. The fine

structure of the ice and ocean motion was investigated to provide a precise simulation of the

ice–ocean–oil spill system. The modeled circulation revealed the observed Beaufort Sea coastal

current and the Beaufort Gyre. The vertical temperature and salinity profile demonstrated the

observed dense water sinking process on shelf areas. The model reproduces a reasonable seasonal

cycle for sea ice concentration, temperature, salinity, water masses, and other variables. The nested

model reproduces mesoscale eddies, consistent with satellite images taken in the same region. Some

important processes, such as winter halocline ventilation and dense water formation, are captured in

the model.

The surface circulation follows the wind direction (to the west), while the slope current along the

Beaufort Sea slope is reproduced below 100 m, flowing to the east, opposite to the surface current.

There are some mesoscale eddies in the fine-resolution model. Neither the slope current nor the

mesoscale eddies are captured in the coarse model. Thus, this fine-nested model captures some

dynamic features in the Beaufort Sea and its shelves.

Using station wind data and simulated surface current, sea ice velocity and ice concentration from

the coupled ice–ocean model, we conducted a series of simulations of oil spills released at different

times, with and without sea ice cover. The results show significant seasonal and interannual

variability of the oil spill trajectory under simulated ice conditions. Sea ice cover can affect oil spill

trajectory by reducing wind effects on sea surface current, sea ice flow and oil spill velocity. Ice flows

dominate oil spill movement in the winter months, and wind has a larger effect on oil spill movement

during the summer.
7

Satellite Tracking of Eastern Chukchi Sea Beluga

Whales in the Beaufort Sea and Arctic Ocean

Robert S. Suydam <robert.suydam@north-slope.org>

Department of Wildlife Management

North Slope Borough

P.O. Box 69

Barrow, AK 99723-0069

Lloyd F. Lowry <llowry@eagle.ptialaska.net>

Kathryn J. Frost <kjfrost@eagle.ptialaska.net>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775–7220

Task Order 15179

Abstract

At least five stocks of beluga whales (Delphinapterus leucas) occur in Alaska. One of these, the

eastern Chukchi Sea stock, is most commonly seen in coastal waters near Kasegaluk Lagoon in

northwestern Alaska during June and July. Despite protection under the Marine Mammal Protection

Act and their importance to many Alaska Native hunters for subsistence, relatively little is known

about the movements and seasonal distribution of these whales during the rest of the year. In

1998–2002 we instrumented 23 belugas with satellite-linked depth recorders (SDRs), including

12 adult males, 5 immature males, 2 adult females and 4 immature females. SDRs provided location

information for an average of 67 (range 5–154) days. Saddle mount tags averaged 52 days, spider

mounts 68 days and side mounts 81 days, although there was no statistical difference in longevity

among attachment types. Animals moved north and east into the northern Chukchi and western

Beaufort seas after capture. During July–September, movement patterns differed by age and/or sex.

All belugas that moved north of 75°N in the Beaufort Sea and Arctic Ocean were males. Adult males

tended to use deeper water and to remain there for most of the summer. Five of nine adult males

tagged from all-male groups early in their northward migration traveled through 90% pack ice cover

to reach 79–80°N by late July/early August. Adult males captured from groups that included adult

females also moved into deep water but apparently for shorter periods of time. In all years, adult and

immature females remained at or near the shelf break throughout summer and early fall. Immature

males moved farther north than immature females, but not as far north as adult males based on our

small sample size. Belugas of all ages and both sexes were most often found in water deeper than

200 m along and beyond the continental shelf break. They rarely used the inshore waters within the

Outer Continental Shelf lease sale area of the Beaufort Sea. Heavy ice apparently did not inhibit the

movements of large adult males in summer since they traveled through and were often located in

>90% ice cover. Only 3 tagged belugas transmitted data after October. Those animals migrated

south through Bering Strait into the northern Bering Sea north of Saint Lawrence Island.
8

Timing and Re-Interpretation of Ringed Seal Surveys

Brendan P. Kelly <brendan.kelly@uas.alaska.edu>

Oriana R. Harding

Mervi Kunnasranta

John R. Moran, Jr. <ftjrm2@uaf.edu>

University of Alaska Southeast

11120 Glacier Highway

Juneau, AK 99801

Task Order 15180

Abstract

We used radio telemetry to estimate the proportion of ringed seals (n = 61) visible resting on the

ice surface during the spring months in 1999, 2000, 2001, 2002, and 2003. Changes in the number

counted during ground-based, visual surveys of seals resting on the ice corresponded to changes in

the number of radio-tagged seals basking. Tagged seals spent 17% (95% CL = 12–21%) of their time

out of the water while using subnivean lairs and 40% (95% CL = 35–45%) of their time out of water

after emerging from those lairs. Haulout bouts were more frequent (median interval = 14 hr) and

longer (median duration = 9 hr) when seals were basking than when they were using subnivean lairs

(median interval = 27 hr, median duration = 6 hr). Before lair abandonment was complete, the mean

proportion of tagged seals basking was 0.19 (CV = 117.92). After lair abandonment was complete,

the mean proportion of tagged seals basking was 0.75 (CV = 27.93).

Emergence from the lairs was related to structural failure of the snow pack, and passive microwave

emissions, indicative of liquid moisture in the snow, predicted lair abandonment (r2 = 0.982,

p = 0.001). In addition, active microwave (Ku-band) backscatter detected snow melt, but also

responded to an anomalous rain event.

Interannual and spatial comparisons of seal densities based on aerial surveys have not adequately

accounted for the proportion of seals concealed within subnivean lairs. Previous models of the

factors influencing the density of seals detected in aerial surveys have been based on densities

observed during surveys conducted under limited ranges of conditions. We used the proportion of

radio-tagged seals visible as our response variable to determine which environmental factors were

most important in explaining the availability of seals to be counted. The most important variables

were date, time of day, wind speed, and days before snow melt as determined by passive microwave

emissions. A model including those terms explained 72% of the variance in the proportion of tagged

seals visible. We recommend timing future surveys to take place after substantial snow melt (detected

by passive microwave emissions) indicates lair abandonment is complete.

Radio-tagged ringed seals remained close to their capture and release sites in April, May, and early

June. The mean home range size during the breeding season was 1.73 km2 (SD = 4.19), and 94% of

the home ranges were less than 3 km2

. Interannual fidelity to breeding sites was observed in 3 adult

ringed seals (2 males and 1 female) and suggests fitness costs to displacement of seals and an

unexpected level of population structuring.
9

Archiving of Shelikof Strait Sediment Samples

at the University of Alaska Museum

A. Sathy Naidu <ffsan@uaf.edu>

John J. Kelley <ffjjk@uaf.edu>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775-7220

Task Order 15181

Abstract

In 1997–98, 288 surface sediment samples were collected from lower Cook Inlet and Shelikof Strait

as part of a research project on trace elements and hydrocarbons by Arthur D. Little, and funded by

the Minerals Management Service. After completion of the project MMS made the residual samples

available for supplemental studies or archiving. These sediment samples are very valuable, as they

were collected at considerable cost and effort and from an area where few sediment samples are

available.

Sathy Naidu, Institute of Marine Science, University of Alaska Fairbanks, is in the process of

transferring his sediment samples from marine regions of arctic and subarctic Alaska (collected over

the last 33 years) to the Earth Sciences Collection at the University of Alaska Museum (UAM). The

goal of the UAM repository is to be the primary marine and freshwater sediment archiving center for

the arctic region. This transfer is to take place in spring–summer 2003, and archiving of the samples

will be accommodated in the museum repository as part of an effort funded by the National Science

Foundation to the museum. This brief proposal requests that the Cook Inlet–Shelikof Strait samples

be transferred to Dr. Naidu who, in turn, will arrange for their archiving at UAM. For these samples,

Dr. Naidu has been offered walk-in cooler space (maintained at 3.6°C) with a duplicate refrigeration

system. The latter is wired into an alarm system that alerts the university power plant and all curators

if there is a power failure. Backup electrical generators are housed next door to the refrigerator.

Additionally, all samples will be maintained in a contaminant-free atmosphere. The Cook Inlet–

Shelikof Strait sample suite will be integrated into Dr. Naidu’s sediment collection, labeled, and

arranged systematically for easy retrieval. To ensure proper use of a split of a sample, a potential

user will be required to go through the protocol developed by the Museum User’s Committee, of

which Dr. Naidu is a member. Records (location, coordinates, water depth of collection, available

analytical data and their quality) on all samples will eventually be made available on an easily

accessible website.
10

CODAR in Alaska

Hank Statscewich <stats@ims.uaf.edu>

David L. Musgrave <musgrave@ims.uaf.edu>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775-7220

Task Order 74261

Abstract

Surface currents measured by High Frequency RADAR instrumentation obtained near the mouth

of the Kenai River in Cook Inlet and sea level height measurements from Nikiski are analyzed for

tidal constituents over a 6-month sampling interval. Harmonic analyses of sea level height records

reveal a form ratio of 0.28 which classifies this region as having mixed, mainly semi-diurnal tides.

A significant portion of energy is contained in the tidal currents of this region, accounting for

approximately 90% of the total current variance. Currents were dominated by the M2 tidal

constituents, whose magnitudes ranged from 30 to 200 cm s–1 and tended to be aligned with local

topography. Mean subtidal currents were variable (0.5–100 cm s–1) with the strongest currents

occurring over the deepest sections of the channel. Particle excursions, due to tides, in this section of

Cook Inlet are between 4 and 32 km and are comparable to the channel width. Predicted, barotropic

tidal current ellipses agree quite well in magnitude and phase with the tidal model of Foreman et al.

Computations of horizontal divergence indicate persistent regions of up and downwelling which are

aligned with the edges of locally documented tidal rips.
11

Observations of Hydrography in Central Cook Inlet,

Alaska, During Diurnal and Semidiurnal Tidal Cycles

Stephen R. Okkonen <okkonen@alaska.net>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775–7220

Task Order 85243

Abstract

Surface-to-bottom measurements of temperature, salinity, and transmissivity, as well as

measurements of surface currents (vessel drift speeds) were acquired along an east–west section

in central Cook Inlet, Alaska during a 26-hr period on 9–10 August 2003. These measurements are

used to describe the evolution of frontal features (tide rips) and physical properties along this section

during semidiurnal and diurnal tidal cycles. The observation that the amplitude of surface currents

is a function of water depth is used to show that strong frontal features occur in association with

steep bathymetry. The positions and strengths of these fronts vary with the semidiurnal tide. The

presence of freshwater gradients alters the phase and duration of tidal currents across the section.

Where mean density-driven flow is northward (along the eastern shore and near Kalgin Island), the

onset of northward tidal flow (flood tide) occurs earlier and has longer duration than the onset and

duration of northward tidal flow where mean density-driven flow is southward (in the shipping

channel). Conversely, where mean density-driven flow is southward (in the shipping channel), the

onset of southward tidal flow (ebb tide) occurs earlier and has longer duration than the onset and

duration of southward tidal flow along the eastern shore and near Kalgin Island.
12

Importance of the Alaskan Beaufort Sea to King Eiders

(Somateria spectabilis)

Abby N. Powell <ffanp@uaf.edu>

Laura M. Phillips <fslmp@uaf.edu>

Eric A. Rexstad <ffear@uaf.edu>

Institute of Arctic Biology

University of Alaska Fairbanks

Fairbanks, AK 99775-7000

Eric J. Taylor <eric_taylor@fws.gov>

U.S. Fish and Wildlife Service

101 12th Avenue

Box 19, Room 110

Fairbanks, AK 99701

Task Order 15182

Abstract

During the molt migration (early July–August) and fall migration (mid-August–October), king eiders

move west along the Beaufort Sea coast to areas in the Chukchi and Bering seas, but information

on distance offshore and the frequency and location of potential staging areas is lacking. This study

was begun to better understand use (timing, location, duration) of nearshore (barrier island to

the mainland coast) and offshore (seaward of the barrier islands) habitats of the Beaufort Sea

by migrating, staging, and molting adult king eiders. We trapped 27 pre-breeding king eiders

(13 females and 14 males) in 2004 using mist nets and decoys at the Kuparuk oil field on the Arctic

Coastal Plain of Alaska. An experienced veterinarian surgically implanted satellite transmitters into

the body cavity of each eider. The satellite platform transmitting terminal (PTT) transmitters send

location information via satellites every 48 hours during late summer and fall migration. This report

includes late summer location data from the 27 eiders transmittered in 2004 and molt, winter, and

summer location data of 33 king eiders implanted with satellite transmitters during the 2002 and

2003 field seasons. Three of the 2003 transmittered eiders were still sending location data as of

31 July 2004. Areas the 2002 and 2003 eiders used for molting included the Chukotka Peninsula, the

Kamchatka Peninsula, St. Lawrence Island, Kuskokwim Bay, the Alaska Peninsula, and the Beaufort

Sea. These results are comparable to those of Dickson et al. [2000]. Winter locations were along the

Kamchatka Peninsula, the Chukotka Peninsula, the Alaska Peninsula, Kvichak Bay, Chirikof Island,

and the Kenai Peninsula. All 2002 and 2003 females still transmitting returned to the Kuparuk study

site during the 2003 and 2004 breeding seasons. Males went to Canada, the North Slope of Alaska,

and Russia during the 2003 and 2004 breeding seasons. As of 31 July 2004, most 2003 and 2004

males were heading south on molt migration, while most females remained in the Beaufort Sea.
13

Introduction

King eiders (Somateria spectabilis) migrate east along the Beaufort Sea during spring (May–June) to

arctic nesting areas in Russia, Alaska and Canada. During the molt migration (early July–August) and

fall migration (mid-August–October), eiders move west along the Beaufort Sea coast to areas in the

Chukchi and Bering seas; however, some adult male king eiders molt in the Beaufort Sea. Although

the timing and route of the offshore spring migration is likely determined by the availability of open

water in the pack ice, information on distance offshore and the frequency and location of potential

staging areas is lacking. Little is known about the migration corridor and staging and molting areas of

non-breeders. This study was begun to better understand use (timing, location, duration) of nearshore

(barrier island to the mainland coast) and offshore (seaward of the barrier islands) habitats of the

Beaufort Sea by migrating, staging, and molting adult king eiders. Because eiders congregate in large,

dense flocks during migration and molt, they may be particularly vulnerable to an offshore oil spill in

the Beaufort Sea. An apparent decline in the western Canadian and eastern Alaskan populations of

king eiders has increased concern for this species [Suydam et al. 2000]. This study will identify when,

where, and how long adult king eiders use the Beaufort Sea; consequently, results may be overlapped

with trajectories from modeled oil spills to better assess impacts from permitted or planned oil and

gas developments.

Objectives

1. Document movements and locations of spring, summer and fall migrating adult

king eiders (successful and unsuccessful breeders) marked on breeding areas in

Kuparuk, Alaska;

2. Describe potential staging areas used during spring and fall migration;

3. Determine if adult female king eiders (successful and unsuccessful breeders)

molt in the Beaufort Sea prior to fall migration to overwintering areas; and

4. Based on satellite imagery, describe sea ice and open water conditions of the

nearshore and offshore of the Beaufort Sea relative to observed locations of

satellite transmitter implanted king eiders.

Study Area

This study has two main sites on the North Slope of Alaska: Teshekpuk Lake and the Kuparuk oil

fields. The Teshekpuk Lake study site was added to the project this year and is located 10 km inland

from the southeast shore of the lake and has experienced very little human impact. The Kuparuk

study site is an area on the Arctic Coastal Plain between the Colville and Kuparuk rivers leased by

ConocoPhillips Alaska, Inc. for oil development. Both areas are characterized by numerous thaw

lakes, ponds and basins. Wetland community types include wet sedge (Carex spp.) meadows, moist

sedge–dwarf shrub (Salix spp.) meadows, and emergent Carex spp. and Arctophila fulva on the

margins of lakes and ponds [Anderson et al. 1999]. Figure 1 shows the locations of the study areas

and sites mentioned in the report.
14

Figure 1. Locations of study area and place names mentioned.

Methods

Transmitters were deployed at the Teshekpuk Lake field site 13 June–16 June and at the Kuparuk

field site 18 June–20 June. King eiders were captured using mist net arrays set up in ponds with

relatively large concentrations of eiders. Once captured, the eiders were placed in a secure, dark

kennel and transported to an indoor facility or weatherport equipped for surgery. A veterinarian

(Cheryl Scott, DVM) and one assistant surgically implanted a 35-g satellite platform transmitting

terminal (PTT) transmitter into the abdominal cavity of each eider following the techniques described

in our previous annual report [Powell et al. 2004]. We weighed each eider while in captivity, took
15

tarsus and culmen measurements, and fitted it with a U.S. Fish and Wildlife Service (USFWS) band.

The birds were held until fully awake and recovered from anesthesia (usually about 2–3 h), and then

released near the area where they were captured.

The 35-g PTTs used in this study have an expected battery life of 800 h. To obtain the greatest

number of locations during periods of active migration, the transmitters were programmed to

have 4 different duty cycles. They transmit for 6 h every 48 h for the first 4 months (June through

September) to increase the likelihood of collecting location data in the Beaufort Sea during molt

migration. The transmitters then transmit for 6 h every 84 h for 3 months (October through

December), every 168 h for 3 months (January through March) and every 84 h until the end of

the battery life. The battery is projected to last about 1 year.

Migration was defined as an individual remaining in an area less than 1 week, with sequential

locations indicating movement in a single direction [Petersen et al. 1999]. Assuming king and

spectacled eiders have a similar behavior during molt, a bird was considered molting if it remained

within a restricted area and moved less than 1.5 km h–1. Staging birds were identified from clusters

of locations from several birds or a bird remaining in an area for at least 10 d [Petersen et al. 1999].

Location data were filtered for accuracy using PC-SAS Argos Filter V5.0 (Dave Douglas, U.S.

Geological Survey/Alaska Science Center). The filtering program removes implausible locations

based on location redundancy and tracking paths. Locations were then plotted using ArcView GIS.

Results

When we arrived at the Teshekpuk field site on 8 June 2004, most of the ponds in the study area were

still partially frozen over and some snow remained on the ground. We trapped eiders at 2 wetland

locations. Five female and seven male pre-breeding king eiders were captured and implanted with

satellite transmitters between 13 and 16 June. Fifteen king eiders (8 female and 7 male) were trapped

and implanted with transmitters at the Kuparuk field site between 18 and 20 June. There were no

complications during the surgeries and all of the birds appeared healthy when released.

This report includes location data from 1 September 2002 through 31 July 2004 for the 33 king eiders

transmittered in 2002 and 2003 and from capture date through 31 July 2004 for the 27 king eiders

transmittered in 2004. Of the 33 transmitters deployed in king eiders in 2002 and 2003, all 21 from

2002 have stopped transmitting and of 12 transmitters deployed in 2003, 3 are still transmitting

location information (Table 1). All 27 king eiders implanted during the 2004 field season were alive

and transmitting as of 31 July 2004. The results and analysis of location data presented here are

preliminary and may be subject to change at a later date based on new information received.

2002 satellite transmitter birds

Males (n = 10) staged 7–17 d (mean = 10) in the Beaufort Sea prior to fall molt migration at a mean

distance from shore of 17 ± 6 km (SD) and a mean water depth of 11 ± 7 m, n = 94 location data

points. Females (n = 11) staged 9–32 d (mean = 20) in the Beaufort Sea prior to molt migration at

a mean distance from shore of 14 ± 3 km and a mean water depth of 8 ± 5 m, n = 174 location data

points. Males reached molting areas along the Chukotka Peninsula and Kamchatka Peninsula, Russia

and St. Lawrence Island and Kuskokwim Bay, Alaska from 22 July through 12 August. Females

reached molting areas along the Chukotka Peninsula and Kamchatka Peninsula and St. Lawrence

Island, the Arctic Coastal Plain and the Alaska Peninsula from 11 August through 18 September

(Table 1, Figure 2).
16

Table 1. September 2002 through July 2003 molting, wintering, summering and current locations of

33 king eiders fitted with satellite transmitters at Kuparuk, Alaska.

ID PTT # Year

Deployed Sex TransmitterStatus

Molt

Location

Wintering

Location

Summer

Location

KNG01 33933 2002 F Failed NA NA NA

KNG02 33934 2002 F Indicated

bird dead

Cape Chaplin,

Chukotka Peninsula,

Russia

NA NA

KNG03 33935 2002 F Failed Alaska Peninsula Alaska Peninsula Kuparuk, AK

KNG04 33936 2002 M Failed

Cape Chaplin,

Chukotka Peninsula,

Russia

Chukotka Peninsula,

Russia

Beaufort Sea

off coast of

Canada

KNG05 33937 2002 F Failed Kvichak Bay, AK Kenai Peninsula, AK NA

KNG06 33938 2002 F Failed NA NA NA

KNG07 33939 2002 M Failed Kuskokwim Bay, AK Kvichak Bay, AK Banks Island,

Canada

KNG08 33940 2002 M Failed

Anadyr Bay,

Chukotka Peninsula,

Russia

Kvichak Bay &

Alaska Peninsula

Beaufort Sea

off coast of

Canada

KNG09 33941 2002 F Failed Alaska Peninsula Kvichak Bay &

Alaska Peninsula Kuparuk, AK

KNG10 33942 2002 M Failed Kamchatka

Peninsula, Russia

Kamchatka

Peninsula, Russia Inland Russia

KNG11 33943 2002 M Indicated

bird dead

Karagin Bay,

Kamchatka

Peninsula, Russia

Kamchatka

Peninsula, Russia NA

KNG12 33944 2002 F Failed

Karagin Bay,

Kamchatka

Peninsula, Russia

Karagin Bay,

Kamchatka

Peninsula, Russia

Kuparuk, AK

KNG13 33945 2002 F Failed

Anadyr Bay,

Chukotka Peninsula,

Russia

Chirikof Island, AK Kuparuk, AK

KNG14 33946 2002 F Failed

Cape Nygchigen,

Chukotka Peninsula,

Russia

Cape Chukotka,

Chukotka Peninsula,

Russia

Kuparuk, AK

KNG15 33947 2002 M Failed St. Lawrence

Island, AK Togiak Bay, AK Inland Russia

KNG16 33948 2002 M Failed

Karagin Bay,

Kamchatka

Peninsula, Russia

Kamchatka

Peninsula, Russia

South of

Barrow, AK

KNG17 33949 2002 F Failed St. Lawrence

Island, AK Alaska Peninsula Kuparuk, AK

KNG18 33950 2002 M Failed

Mechigmen Bay,

Chukotka Peninsula,

Russia

Chirikof Island &

Kvichak Bay, AK

Beaufort Sea

off coast of

Canada

KNG19 33952 2002 M Failed St. Lawrence

Island, AK

Alaska Peninsula

& Togiak Bay Kuparuk, AK

KNG20 33953 2002 M Failed

Anadyr Bay,

Chukotka Peninsula,

Russia

Meynypil’gyno,

Russia

Cape Bathurst,

Canada

KNG21 33954 2002 F Failed

Cape Chukotka,

Chukotka Peninsula,

Russia

Cape Chaplin,

Chukotka Peninsula,

Russia

NA
17

Table 1. continued

ID PTT # Year

Deployed Sex Transmitter

Status

Molt

Location

Wintering

Location

Summer

Location

KNG22 40898 2003 M Failed Russian Coast near

Meynypil ́gyno

Kamchatka

Peninsula, Russia NA

KNG23 40899 2003 M Failed

Anadyr Bay,

Chukotka Peninsula,

Russia

Cape Chukotka,

Chukotka Peninsula,

Russia

NA

KNG24 40900 2003 F Alive

Mechigmen Bay,

Chukotka Peninsula,

Russia

Cape Chukotka,

Chukotka Peninsula,

Russia

Kuparuk, AK

KNG25 40901 2003 M Indicated

bird dead Bristol Bay, AK Alaska Peninsula Victoria Island,Canada

KNG26 40902 2003 M Failed

Mechigmen Bay,

Chukotka Peninsula,

Russia

Chukotka Peninsula,

Russia NA

KNG27 40903 2003 F Alive

Mechigmen Bay,

Chukotka Peninsula,

Russia

Alaska Peninsula Kuparuk, AK

KNG28 40904 2003 M Failed

Mechigmen Bay,

Chukotka Peninsula,

Russia

Alaska Peninsula Inland Russia

KNG29 40905 2003 M Failed

Mechigmen Bay,

Chukotka Peninsula,

Russia

Chukotka Peninsula,

Russia

Beaufort Sea

off coast of

Canada

KNG30 40906 2003 F Failed

Anadyr Bay,

Chukotka Peninsula,

Russia

Olyutor Bay, Russia Kuparuk, AK

KNG31 40907 2003 M Failed

Anadyr Bay,

Chukotka Peninsula,

Russia

Olyutor Bay, Russia Banks Island,

Canada

KNG32 40908 2003 M Failed

Anadyr Bay,

Chukotka Peninsula,

Russia

Olyutor Bay, Russia Inland Russia

KNG33 40909 2003 M Alive

Anadyr Bay,

Chukotka Peninsula,

Russia

Alaska Peninsula

Beaufort Sea

off coast of

Canada

Wintering locations for males included areas along the Chukotka Peninsula, Kamchatka Peninsula,

and Meynypilgyno, Russia and Kvichak Bay, the Alaska Peninsula, Chirikof Island, and Togiak Bay,

Alaska. Wintering locations for females included areas along Karagin Bay and the Chukotka

Peninsula and the Kenai Peninsula, Kvichak Bay, Chirikof Island, and the Alaska Peninsula (Table 1,

Figure 3).

The 6 females from 2002 still transmitting in June of 2003 returned to the Kuparuk study site. Of the

9 males from 2002 still transmitting into summer of 2003, 1 returned to Kuparuk, 1 spent some time

south of Barrow, 3 stayed offshore of Canada near Cape Bathurst, 2 spent time onshore in Canada and

2 went onshore in Russia (Table 1, Figure 4).
18

Figure 2. Molting locations of 2002 () and 2003 ( ) king eiders fitted with satellite transmitters

at Kuparuk in 2002.

2003 satellite transmitter birds

Males (n = 9) dispersed from the breeding area 24 June through 13 July 2003 and arrived in molt

areas 18 July through 17 August 2003. Females (n = 3) left the breeding area between 7 July and

30 July and arrived at molt sites 22 August through 4 September. Molt areas included the Chukotka

Peninsula for both males and females and Bristol Bay and the coast of Russia for two males. (Table 1,

Figure 2)

Wintering locations for males included areas along the Chukotka, Kamchatka, and Alaska peninsulas,

Meynypilgyno and Olyutor Bay, Russia. Females wintered along the Alaska and Chukotka

peninsulas and in Olyutor Bay. (Table 1, Figure 3)

Nine of the twelve birds transmittered in 2003 continued to transmit into June, providing 2004

summering locations. All 3 females returned to the capture site at Kuparuk, Alaska. Males summered

in Russia, Canada and the Beaufort Sea. (Table 1, Figure 4)

At the end of July 2004, only 3 of the 2003 eiders (2 females and 1 male) were still transmitting

location information. Only 1 female remained at the Kuparuk study area, 1 was off the coast of

Alaska in the Beaufort Sea and the male was located in the Beaufort near Banks Island, Canada.

(Figure 5)
19

Figure 3. Wintering locations of male () and female (°

) king eiders fitted with satellite

transmitters at Kuparuk in 2002 and 2003.

2004 satellite transmitter birds

By 31 July, all 2004 transmittered male king eiders had left the study areas and moved into the

Chukchi and Bering seas, with 7 males located along the Chukotka Peninsula, 4 south of St.

Lawrence Island, 2 near Meynypilgyno, Russia and 1 in the Chukchi Sea near Icy Cape. All but

2 females had left the study areas by the end of July, with 9 located in the Beaufort Sea and 2 near

Icy Cape, Alaska. (Figure 5)
20

Figure 4. Summer breeding location of male () and female (°

) king eiders fitted with satellite

transmitters at Kuparuk in 2002. All females returned to Kuparuk and are indicated by a

single marker.

Discussion

Molting areas for king eiders implanted at Kuparuk in 2002 and 2003 are similar to those found for

eiders implanted at Victoria Island and Prudhoe Bay, 1997–1999 [Dickson et al. 2000]. Data on

wintering locations of king eiders is limited. Dickson et al. [2000] had a small sample of king

eiders (n = 9) with satellite transmitters that were transmitting location data into December. Their

information suggested that similar to eiders breeding at Kuparuk, eiders breeding at Victoria Island

and Prudhoe Bay wintered along the Kamchatka Peninsula, the Chukotka Peninsula, the Alaska

Peninsula, and Kodiak Island. Ours is the first account of satellite transmitters in king eiders lasting

into the next breeding season. Females transmittered in 2002 returning to Kuparuk for the 2003

breeding season suggests some fidelity to nesting areas.
21

Figure 5. Location of thirty 2003/2004 male () and female (°) satellite-tagged king eiders the last

week of July 2004.

Future Plans

We will continue to collect and map location information as it becomes available. Location data will

be analyzed to detect staging, molting and wintering areas. We will also begin to overlay bathymetric

and ice coverage maps to evaluate the impacts of these factors on migration paths.

Acknowledgements

This project would not be possible without logistic support from ConocoPhillips Alaska, Inc. and

financial support from the UA Coastal Marine Institute and Minerals Management Service. Support

was also provided by the North Slope Borough, the Alaska Cooperative Fish and Wildlife Research

Unit, and the U.S. Fish and Wildlife Service. We would also like to thank Chuck Monnett (MMS);

Declan Troy (TERA, Inc.); Betty Anderson, John Shook, and Michael Knoche (ABR, Inc.); Philip

Martin and Tim Obritschkewitsch (USFWS); Caryn Rea, Anne Lazenby, Leigh McDaniel, and Justin

Harth (ConocoPhillips Alaska, Inc.); Dave Douglas (U.S. Geological Survey); Robert Suydam (North

Slope Borough); Paul and Chris Howey (Microwave Telemetry); our field technicians: Eric Duran,

Lori Gildehaus, Amanda Prevel, Keith Roby, and Ben Soiseth; volunteers: Stacia Backensto and

Corey Adler; and our veterinarian Cheryl Scott and vet technicians Kim Adams and Mindy Carlson.
22

References

Anderson, B.A., C.B. Johnson, B.A. Cooper, L.N. Smith and A.A. Stickney. 1999. Habitat

associations of nesting spectacled eiders on the Arctic Coastal Plain of Alaska, p. 27–32.

In R.I. Goudie, M.R. Peterson and G.J. Robertson [eds.], Behavior and Ecology of Sea Ducks.

Can Wildl. Ser. Occas. Pap. 100.

Dickson, D.L., R.S. Suydam and G. Balogh. 2000. Tracking the movements of king eiders from

nesting grounds at Prudhoe Bay, Alaska to their molting and wintering areas using satellite

telemetry. Canadian Wildlife Service 1999/2000 Progress Report.

Petersen, M.R., W.W. Larned and D.C. Douglas. 1999. At-sea distribution of spectacled eiders:

A 120-year-old mystery resolved. Auk 116:1009–1020.

Powell, A.N., L.M. Phillips, E.A. Rexstad and E.J. Taylor. 2004. Importance of the Alaskan

Beaufort Sea to king eiders (Somateria spectabilis), p. 33–47. In University of Alaska Coastal

Marine Institute Annual Report No. 10. OCS Study MMS 2004-002, University of Alaska

Fairbanks and USDOI, MMS, Alaska OCS Region.

Suydam, R.S., D.L. Dickson, J.B. Fadely and L.T. Quakenbush. 2000. Population declines of King

and Common Eiders of the Beaufort Sea. Condor 102(1):219–222.
23

Population Structure of Common Eiders Nesting on

Coastal Barrier Islands Adjacent to Oil Facilities in the

Beaufort Sea

Sarah A. Sonsthagen1 <ftsas@uaf.edu>

Kevin G. McCracken1 <fnkgm@uaf.edu>

Richard B. Lanctot2 <richard_lanctot@fws.gov>

Sandra L. Talbot3 <sandy_talbot@usgs.gov>

Kim T. Scribner4 <scribne3@msu.edu>

1

Institute of Arctic Biology, University of Alaska Fairbanks, Fairbanks, AK 99775-7000 2

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

1011 East Tudor Road, MS 201, Anchorage, AK 99503 3

U.S. Geological Survey, Biological Resources Division, Alaska Science Center,

1011 East Tudor Road, MS 701, Anchorage, AK 99503 4

Department of Fisheries and Wildlife, 13 Natural Sciences Building,

Michigan State University, East Lansing, MI 48824

Task Order 85239

Abstract

Common Eider populations have experienced declines through most of their range. These birds are

arctic nesters, primarily on barrier islands. Females and young exhibit a high degree of nest site

fidelity to island groups, which may create genetically unique groups relative to neighboring islands.

Because banding and recapture data are difficult to collect in this species, we used molecular

techniques to assess population structure of Common Eiders breeding in North America and Canada.

Based on preliminary results of this study, there were likely two refugia during the last Pleistocene

glaciation. Birds breeding on the North Slope of Alaska were probably colonized from one refugium,

separate from other eider populations and therefore genetically distinct. Eiders breeding in the

Yukon–Kuskokwim Delta, Canada, and Scandinavia appear to have been colonized from the other

refugium. Differences in levels of population structuring among mitochondrial and nuclear DNA

suggest that recent gene flow has occurred between these populations mainly through male dispersal.

Introduction

Common Eider (Somateria mollissima) populations have exhibited declines throughout part of their

range, including Alaska, western Canada, southern Hudson Bay, and eastern Finland. Populations

residing in Alaska and western Canada have declined approximately 53% since the mid-1970s

[Suydam et al. 2000], while birds breeding on the Belcher Islands in southern Hudson Bay have

dimished approximately 75% since the mid-1980s [Robertson and Gilchrist 1998]. Others, however,

are exhibiting stable or increasing numbers, for example, some populations breeding in the Baltic

Sea region and northern Hudson Bay [Tiedemann and Noer 1998; Hipfner et al. 2002]. While the

reasons for the declines are unknown, factors such as increased gull and arctic fox predation, and

development or other anthropogenic effects may be factors [Robertson and Gilchrist 1998; Suydam

et al. 2000].
24

Common Eiders nest primarily on barrier islands and in coastal brackish waters [Goudie et al. 2000].

Data suggest that adult females and young exhibit a high degree of nest site fidelity to island groups,

potentially creating genetically unique geographic areas relative to neighboring islands [Cooch 1965;

Reed 1975]. In a sedentary population of Common Eiders breeding in the Netherlands, 641 females

were studied [Swennen 1990]. Nine dispersed from their natal sites and all were found breeding in

nearby colonies. Of the 17 juvenile males studied, 15 dispersed on average 1270 km from their natal

site. For adult males, about 15% of those breeding dispersed each year, which was consistent with

other research. Additionally, studies conducted in the Baltic Sea region concluded that Common

Eiders breeding in that area are not randomly mating, i.e., that males were found to mate more often

than expected with females from the same colony [Tiedemann et al. 1999]. Preferential mating among

eiders of the same origin may be an artifact of colonies favoring different areas within a common

winter range or early formation of pair bonds between birds that arrive early on the winter grounds

from the same origin. In addition to high female philopatry exhibited in this species [Baillie and

Milne 1989; Swennen 1990], non-random pair formation on the wintering grounds provides an

additional avenue of limited gene flow between populations.

While some populations of Common Eiders are sedentary, birds breeding in the northern portions of

their range are migratory to varying degrees: Eiders in the Beaufort Sea and western Canada have

been reported to migrate 2700 km from nest sites to their winter range near the Chukotka Peninsula

in Russia [Petersen and Flint 2002; Lynne Dickson, pers. comm.]. Yukon–Kuskokwim River Delta

(Y–K Delta) eiders winter there or approximately 325 km south in Bristol Bay [Petersen and Flint

2002]. One population breeding on the Belcher Islands in Hudson Bay is sedentary, whereas other

populations in Hudson Bay winter in ice-free waters south of their breeding sites [Robertson and

Gilchrist 1998]. Scandinavian populations migrate to wintering areas in the Baltic Sea, where mixing

with other European colonies likely occurs [Swennen 1990; Tiedemann and Noer 1998; Tiedemann

et al. 1999]. Since pair formation takes place on the wintering grounds [Spurr and Milne 1976],

populations that share winter ranges are more likely to be genetically similar, as male dispersal

between those populations will have a homogenizing effect.

Objectives of this study are to assess: 1) population structure among Common Eiders breeding on the

coastal barrier islands in the Beaufort Sea, 2) population structure of Common Eiders breeding at four

sites in the Y–K Delta, and 3) population structure and post-glacial colonization of Common Eiders

breeding throughout North America and Canada.

Methods

Sample collection

Blood, feather, or tissue samples were collected in the following localities from 17 populations that

represent all five subspecies (Figure 1):

Somateria mollissima nigrum

2 sites in the Y–K Delta, Alaska (N = 113)

2 sites from the North Slope, Alaska (N = 109)

Kent Peninsula, Canada (N = 26)

S.m. sedentaria

Belcher Islands, Canada (N = 20)

S.m. dresseri

New Brunswick, Canada (N = 39)

Nova Scotia, Canada (N = 39)

S.m. borealis

Baffin Island, Canada (N = 14)

Hudson Strait, Canada (N = 28)

Southampton Island, Canada (N = 52)

Mansel Island, Canada (N = 3)

S.m. mollissima

Svalbard, Norway (N = 37)

Tromsø, Norway (N = 37)

Söderskär, Finland (N = 26)
25

To better assess population structure among barrier islands in the Beaufort Sea, additional feather

samples were extracted in an attempt to obtain approximately 30 individuals from each island for the

microsatellite DNA analyses. In total, samples from 350 individuals were collected. Each of the 14

islands are represented by 10 to 38 individuals, with one sample from an additional island.

Figure 1. Sampled sites of Common Eiders used in this study. Each dashed line oval represents

locations with the same subspecies: S.m. nigrum – Y–K Delta, North Slope and Kent

Peninsula; S.m. borealis – Baffin Island, Southampton Island, Hudson Strait and

Mansel Island; S.m. sedentaria – Belcher Islands; S.m. dresseri – New Brunswick

and Nova Scotia; and S.m. mollissima – Svalbard, Tromsø and Söderskär.

Molecular marker data collection

Three types of molecular markers—microsatellite, mitochondrial DNA (mtDNA), and nuclear gene

introns—were used to assess population differentiation. These genetic markers differ in their mode

of inheritance and rate of evolution. Microsatellite alleles are inherited from both parents and have

a high rate of mutation, mitochondrial DNA is maternally inherited and has a moderate rate of

mutation, and nuclear gene introns are inherited from both parents with the slowest rate of mutation.

Because we are using three different markers with different modes of inheritance and mutation rates,

we will be able to determine if the populations are structured due to male or female breeding behavior

and the relative time when these populations diverged from each other.
26

Microsatellites are short tandem repeats located in non-coded regions of nuclear DNA. Because these

repeats are in non-coded regions and are not under selection pressures, they are highly polymorphic.

Fifty-six microsatellite loci were screened for variability in Common Eiders, of which 36 were

polymorphic. We have chosen 12 informative loci from autosomal regions of the genome to assess

macrogeographic population structure and two additional loci for the microgeographic population

structure (North Slope and Y–K Delta). Preliminary data presented will be based on genotypes of

687 individuals, with an analysis of 12–14 microsatellite loci using a LI-COR automated sequencer:

Y–K Delta (N = 125), North Slope (N = 202), Kent Peninsula (N = 41), Baffin Island (N = 15),

Southampton Island (N = 52), Hudson Strait (N = 28), Mansel Island (N = 3), Belcher Islands

(N = 22), New Brunswick (N = 40), Nova Scotia (N = 40), Svalbard (N = 37), Tromsø (N = 38), and

Söderskär (N = 27). Data were analyzed in FSTAT [Goudet 1995, 2001] and GENEPOP [Raymond

and Rousset 1995] genetic data analysis programs to calculate FST values.

FST is a measure of population structure: values range from zero to one, where zero reflects panmixia

and one indicates complete population isolation.

We have developed primers to sequence approximately 550 to 565 base pairs from the mitochondrial

DNA control region, with preliminary data based on sequences from 423 individuals. We sequenced

250 base pairs of Lamin A intron 3 and 350 base pairs of GAPDH (glyceraldehyde-3-phosphate

dehydrogenase) intron 11, and preliminary data are based on sequences from 546 individuals for

Lamin A and 474 individuals for GAPDH. Sequences were collected using an ABI 3100 automated

sequencer. Unique haplotypes were determined by hand and PHASE was used to reconstruct alleles

for nuclear intron data [Stephens et al. 2001; Stephens and Donnelly 2003]. Data were analyzed in

Arlequin 2.0 [Schneider et al. 1999] and maximum parsimony trees were constructed in PAUP* 4.0

[Swofford 1998].

Results

Microsatellite DNA analysis

Mean observed heterozygosity per locus ranged from 8.5 to 90.4%, with an overall mean of 49.3%.

The overall FST (0.06) was significantly greater than zero, suggesting population subdivision. There

were significant differences in pairwise FST values between North Slope, Kent Peninsula, and

Y–K Delta populations and all others (FST values ranging from 0.0001 to 0.188); little significant

population structuring was observed among Canadian and Scandinavian populations (Table 1).
27

Table 1. Pairwise FST values with significant P-values shown in bold for each population pair for

12–14 microsatellite loci.

North Slope –

Kent Peninsula 0.002 –

Y–K Delta 0.000 0.001 –

Southampton 0.086 0.088 0.071 –

Baffin Island 0.099 0.098 0.090 0.054 –

Hudson Strait 0.071 0.077 0.064 0.033 0.010 –

Belcher Islands 0.100 0.093 0.092 0.050 0.011 0.005 –

Nova Scotia 0.063 0.075 0.060 0.042 0.052 0.025 0.034 –

New Brunswick 0.066 0.079 0.061 0.038 0.051 0.027 0.043 0.001 –

Svalbard 0.104 0.116 0.097 0.054 0.031 0.006 0.021 0.030 0.040 –

Tromsø 0.137 0.160 0.129 0.079 0.074 0.037 0.037 0.063 0.076 0.013 –

Söderskär 0.162 0.188 0.156 0.098 0.083 0.051 0.051 0.082 0.099 0.023 0.004

Mitochondrial DNA analysis

Two haplotype groups are present in these sequences and frequencies of individuals from each

locality differ between the two groups (2

=127.2, df 2, P<0.0001, Figure 2). Individuals from the

North Slope are predominately represented in the lower haplotype group and individuals from the

Y–K Delta, Canada, and Scandinavia are predominately represented in the upper haplotype group.

For pairwise population comparisons, individuals from Bodfish and Flaxman Islands (North Slope)

were pooled as one population and all of the Y–K Delta sites were pooled because there were no

significant differences between them. Mansel Island was removed from the analysis because of the

small sample size (N = 3). There is significant population structure among all populations except

between Hudson Strait and Southampton Island and Baffin Island, and between Southampton Island

and Belcher Islands and Baffin Island (Table 2). High FST values are observed between North Slope

populations and all other populations, with values ranging from 0.215 to 0.496, between Nova Scotia

and all other populations, with values ranging from 0.361 to 0.664, and between Söderskär and all

other populations, with values ranging from 0.241 to 0.664.
28

Figure 2. Mitochondrial DNA control region haplotype tree constructed from sequences collected

from 17 populations. Sample size and subspecies of each population is listed on left side

of tree.
29

Table 2. Pairwise FST values with significant P-values shown in bold for each population pair for

545–563 base pairs of mitochondrial DNA sequences.

North Slope –

Kent Peninsula 0.217 –

Y–K Delta 0.303 0.139 –

Southampton 0.215 0.068 0.088 –

Baffin Island 0.262 0.084 0.138 0.018 –

Hudson Strait 0.253 0.080 0.169 0.021 0.000 –

Belcher Islands 0.336 0.175 0.191 0.034 0.081 0.058 –

Nova Scotia 0.496 0.402 0.389 0.366 0.439 0.370 0.496 –

New Brunswick 0.315 0.126 0.086 0.081 0.129 0.144 0.190 0.407 –

Svalbard 0.252 0.084 0.124 0.049 0.040 0.038 0.153 0.361 0.117 –

Tromsø 0.239 0.082 0.098 0.049 0.061 0.076 0.141 0.367 0.093 0.062 –

Söderskär 0.465 0.353 0.387 0.310 0.371 0.337 0.444 0.664 0.405 0.241 0.337

Nuclear DNA intron analysis

Like the mitochondrial DNA, two allele groups are also present within the Lamin A intron, but

neither group appears to be associated with a particular locality (Figure 3). However, the two allele

groups may reflect the same historical process that generated the two haplotype groups observed in

mtDNA. We also pooled individuals from Bodfish and Flaxman Islands and all of the Y–K Delta sites

and removed Mansel Island from the analysis. All S.m. nigrum populations are not significantly

different from each other but are different from almost all other populations, with FST values ranging

from 0.008 to 0.137 (Table 3). S.m. borealis populations have some structure between localities,

with values ranging from 0.039 to 0.046. Belcher Islands’ eiders are significantly different from

all populations except Baffin Island, with FST values ranging from 0.010 to 0.151. S.m. dresseri

populations also are not differentiated from each other but are significantly different from all other

populations except for those in New Brunswick and the Y–K Delta. S.m. mollissima populations are

showing structure between two localities, with a small FST of 0.019. Additionally, there are some

insignificant FST values between the S.m. mollissima and S.m. borealis populations.

A majority of the individuals have been sequenced for nuclear intron GAPDH, which is exhibiting

similar population structuring as Lamin A.
30

Figure 3. Nuclear intron Lamin A allele tree constructed from sequences collected from 17

populations. Sample size and subspecies of each population is listed on left side of tree.
31

Table 3. Pairwise FST values with significant P-values shown in bold for each population pair for

nuclear intron Lamin A sequences.

North Slope –

Kent Peninsula 0.002 –

Y–K Delta 0.000 0.001 –

Southampton 0.086 0.088 0.071 –

Baffin Island 0.099 0.098 0.090 0.054 –

Hudson Strait 0.071 0.077 0.064 0.033 0.010 –

Belcher Islands 0.100 0.093 0.092 0.050 0.011 0.005 –

Nova Scotia 0.063 0.075 0.060 0.042 0.052 0.025 0.034 –

New Brunswick 0.066 0.079 0.061 0.038 0.051 0.027 0.043 0.001 –

Svalbard 0.104 0.116 0.097 0.054 0.031 0.006 0.021 0.030 0.040 –

Tromsø 0.137 0.160 0.129 0.079 0.074 0.037 0.037 0.063 0.076 0.013 –

Söderskär 0.162 0.188 0.156 0.098 0.083 0.051 0.051 0.082 0.099 0.023 0.004

Conclusions

North Slope birds are genetically distinct from other eider populations as seen from the mitochondrial

DNA analysis. They represent a unique haplotype group, and both allelic (for microsatellite DNA)

and haplotypic (for mtDNA) frequencies are significantly different in North Slope populations

relative to all other populations analyzed. The existence of two allele and haplotype groups suggests

that Common Eiders were historically subdivided in two refugia during the last Pleistocene

glaciation. The North Slope may have been colonized by birds expanding out of a different refugium

from those that colonized the rest of North America and Scandinavia. Eiders breeding in the Y–K

Delta, Southampton Island, Baffin Island, Hudson Straits, Mansel Island, Belcher Islands, New

Brunswick, Nova Scotia, Svalbard, Tromsø, and Söderskär locations appear to have a ring

distribution [Newton 2003; Avise 2004]; whereas those from Kent Peninsula have haplotypes from

both groupings, consistent with a hypothesis that this area represents a contact zone between the two

potential Pleistocene refugia [Newton 2003]. These finds are in agreement with studies conducted in

Europe, which suggest that the Baltic Sea region was colonized by Common Eiders inhabiting a

single Pleistocene refugium [Tiedemann and Noer 1998; Tiedemann et al. 2004]. However, shared

alleles and haplotypes among localities indicate that recent gene flow via female and male dispersal

has occurred. Differences in levels of population structuring between maternally and bi-parentally

inherited markers suggest that gene flow in Common Eiders is most likely mediated by males.
32

Current Work

Data collection has been completed for the microgeographic components of this study, and the

findings are now being analyzed and written up for submission to a peer-reviewed scientific journal.

Additional samples have been received from Greenland (N = 20) and Gotland Island, Sweden

(N = 50), and data collection has begun on these samples.

Acknowledgments

Funding sources: Alaska EPSCoR (Experimental Program to Stimulate Competitive Research)

Graduate Fellowship (NSF EPS-0092040), Angus Gavin Migratory Bird Research Fund, BP

Exploration Alaska Inc., UA Coastal Marine Institute, Minerals Management Service, UAF, and

the U.S. Geological Survey.

Samples: Lotte Andersen, Mans Hjernquist, Bill Barrow, Fred Broerman, J.O. Bustness, Kathy

Dickson, Lynne Dickson, Paul Flint, Grant Gilchrist, Martti Hario, D. Kellet, Mikael Kilpi, Lynne

Noel, Kim Mawhinney, Margaret Petersen, Kim Scribner, Robert Suydam, and Pam Tuome.

References

Avise, J.C. 2004. Molecular Markers, Natural History, and Evolution, Second Edition. Sinauer

Associates, Inc., Sunderland, Massachusetts.

Baillie, S.R., and H. Milne. 1989. Movements of Eiders Somateria mollissima on the east coast of

Britain. Ibis 131:321–335.

Cooch, F.G. 1965. The breeding biology and management of the northern eider (Somateria

mollissima borealis) in the Cape Dorset area, Northwest Territories. Can. Wildl. Serv., Wildl.

Manage. Bull., Ser. 2, No. 10.

Goudet, J. 1995. FSTAT (version 1.2): A computer program to calculate F-statistics. J. Heredity

86:485–486.

Goudet, J. 2001. FSTAT, a program to estimate and test gene diversities and fixation indices

(version 2.9.3). Available from http://www.unil.ch/izea/softwares/fstat.html. Updated from

Goudet [1995].

Goudie, R.I., G.J. Robertson and A. Reed. 2000. Common Eider (Somateria mollissima), The Birds

of North America, No. 546 [A. Poole and F. Gill, Eds.]. The Birds of North America, Inc.,

Philadelphia, 32 p.

Hipfner, J.M., H.G. Gilchrist, A.J. Gaston and D.K. Cairns. 2002. Status of Common Eiders,

Somateria mollissima, nesting in the Digges Sound region, Nunavut. Can. Field-Nat.

116(1):22–25.

Newton, I. 2003. Speciation and Biogeography of Birds. Academic Press, 656 p.

Petersen, M.R., and P.L. Flint. 2002. Population structure of Pacific Common Eiders breeding in

Alaska. Condor 104(4):780–787.

Raymond, M. and F. Rousset. 1995. GENEPOP (version 1.2): Population genetics software for

exact tests and ecumenicism. J. Heredity 86:248–249.
33

Reed, A. 1975. Migration, homing, and mortality of breeding female eiders, Somateria mollissima

dresseri, of the St. Lawrence Estuary, Quebec. Ornis Scand. 6:4147.

Robertson, G. J., and H.G. Gilchrist. 1998. Evidence of population declines among Common Eiders

breeding in the Belcher Islands, Northwest Territories. Arctic 51(4):378–385.

Schneider S., D. Roessli and L. Excoffier. 1999. Arlequin ver. 2.0: A software for population genetic

data analysis. Genetics and Biometry Laboratory, University of Geneva, Geneva, Switzerland.

Spurr, E. and H. Milne. 1976. Adaptive significance of autumn pair formation in the Common Eider

Somateria mollissima (L.). Ornis Scand. 7:85–89.

Stephens, M., and P. Donnelly. 2003. A comparison of Bayesian methods for haplotype

reconstruction from population genotype data. Am. J. Hum. Genet. 73(5):1162–1169.

Stephens, M., N.J. Smith and P. Donnelly. 2001. A new statistical method for haplotype

reconstruction from population data. Am. J. Hum. Genet. 68(4):978–989.

Suydam, R.S., D.L. Dickson, J.B. Fadely and L.T. Quakenbush. 2000. Population declines of King

and Common Eiders of the Beaufort Sea. Condor, 102(1):219–222.

Swennen, C. 1990. Dispersal and migratory movements of eiders Somateria mollissima breeding in

the Netherlands. Ornis Scand. 21(1):17–27.

Swofford, D.L. 1998. PAUP*: Phylogenetic Analysis Using Parsimony (and Other Methods),

Version 4. Sinauer Associates, Inc., Sunderland, Massachusetts.

Tiedemann, R., and H. Noer. 1998. Geographic partitioning of mitochondrial DNA patterns in

European Eider Somateria mollissima. Hereditas 128(2):159–166.

Tiedemann, R., K.B. Paulus, M. Scheer, K.G. von Kistowski, K. Skirnisson, D. Bloch and M. Dam.

2004. Mitochondrial DNA and microsatellite variation in the eider duck (Somateria mollissima)

indicate stepwise postglacial colonization of Europe and limited current long-distance dispersal.

Mol. Ecol. 13(6):1469–1480. doi: 10.111/j.1365-294X.2004.02168x

Tiedemann, R., K.G. von Kistowski and H. Noer. 1999. On sex-specific dispersal and mating tactics

in the Common Eider Somateria mollissima as inferred from the genetic structure of breeding

colonies. Behaviour 136(9):1145–1155.
34

Breeding Biology and Habitat Use of King Eiders

on the Coastal Plain of Northern Alaska

Abby N. Powell <ffanp@uaf.edu>

Rebecca L. McGuire <ftrlm@uaf.edu>

Institute of Arctic Biology

University of Alaska Fairbanks

Fairbanks, AK 99775-7000

Robert S. Suydam <robert.suydam@north-slope.org>

Department of Wildlife Management

North Slope Borough

P.O. Box 69

Barrow, AK 99723-0069

Task Order 85240

Abstract

Relatively little is known about the nesting ecology of king eiders (Somateria spectabilis). As a

species that appears to be in decline, and which breeds in an area of potential oil development,

it is important that we gain knowledge of their breeding ecology. During the summer of 2004 we

continued the study of nesting king eiders at Teshekpuk and Kuparuk on Alaska’s North Slope. This

was the third year of a four-year project. We found approximately the same number of nests at both

sites as we have found in previous years. Nest success didn’t differ between the two sites in 2004,

although Teshekpuk had slightly higher success than the previous year while Kuparuk’s was

somewhat lower. Both sites had lower apparent nest success than they experienced during the high

in 2002.

Introduction

Little is known about the breeding biology of king eiders (Somateria spectabilis), partly because they

typically nest in remote areas in low densities. The western North American population of king eiders

declined by more than 50% between 1976 and 1996 for unknown reasons [Suydam et al. 2000].

Additionally, the National Petroleum Reserve–Alaska (NPR-A) is being leased for oil and gas

exploration and may potentially be developed. Within the northeast planning area of NPR-A is the

highest known density of nesting king eiders on the North Slope of Alaska [Larned et al. 2003].

During the summers of 2002, 2003, and 2004 we studied king eiders in an area to the southeast of

Teshekpuk Lake, and in the Kuparuk oil fields on the North Slope to evaluate the potential impacts

of development and to provide information on their basic breeding biology and habitat use. We will

examine and compare timing of nesting, clutch size, reproductive success, and habitat use between a

relatively undisturbed site at Teshekpuk Lake and the active oil field at Kuparuk. This report

summarizes the results of the third field season of data collection.
35

Objectives

1. Document the timing of nest initiation of king eiders.

2. Document nest success and apparent causes of failure.

3. Document nest site characteristics.

4. Compare data collected from above objectives between Teshekpuk Lake

and the Kuparuk oil fields.

Study Areas

This study has two main sites on the North Slope of Alaska—Teshekpuk Lake and the Kuparuk oil

fields. The Teshekpuk Lake study site is 10 km inland from the southeast shore of the lake and has

experienced very little human impact: There is no sign of anthropogenic disturbance and no people

other than those connected with this project have been observed over the past four summers. The

Kuparuk study site, located on the Arctic Coastal Plain between the Colville and Kuparuk rivers, is

leased by ConocoPhillips Alaska, Inc. and actively being developed for oil production. Both areas

are characterized by numerous thaw lakes, ponds and basins. Wetland community types include wet

sedge (Carex spp.) meadows, moist sedge–dwarf shrub (Salix spp.) meadows, and emergent Carex

spp. and Arctophila fulva on the margins of the lakes and ponds [Anderson et al. 1999]. Some

wetlands at Kuparuk are intersected by roads and/or created with the closure and rehabilitation of

gravel pits.

Methods

Accessible areas around Teshekpuk Lake and Kuparuk were searched for pre-nesting and nesting

king eiders during the summers of 2002, 2003, and 2004. We marked nests with a tongue depressor

placed 1 m from the nest in vegetation so as to be concealed from potential predators. We measured

length and width, and weighed and candled each egg to determine incubation stage. Latitude and

longitude were recorded for each nest using a hand-held GPS unit. Habitat type within 50 m of each

nest was classified post-hatch as to type using Bergman’s classification system [1977]. Vegetation

types and frequency were recorded for all nests as well as for random locations within the 2 study

areas. Additionally, we recorded island size, distance to the mainland and depth of the water if the

nest occurred on an island.

Data loggers (HOBO Temp) were placed in randomly selected nests at Teshekpuk (n = 10) and

Kuparuk (n = 8) in both 2002 and 2003 to determine nest attendance and abandonment; they were

set to record temperature every 2 min, and downloaded every 10 d. In 2004 an additional 5 loggers

per site were deployed. We anchored the probe in the nest, which allowed for a quick response to any

change in temperature since the probe had only an eggshell between it and the incubating female

(protocol follows Quakenbush et al. [2004]).

King eiders typically incubate for 22–24 days and all nests were monitored weekly throughout this

period during 2002–2004. Hatch success was determined by the presence of eggshells with detached

membranes [Girard 1939] or the presence of ducklings. If there were eggshells with no membranes or

if the entire egg was absent, the nest was considered depredated. Nesting success was defined as the

percentage of all nests initiated in which at least one egg hatched. We attempted to determine cause

of failure for nests that did not succeed.
36

We placed digital cameras (n = 12/site) at nests in 2004 to attempt to identify nest predators.

Two camera systems were used: the Cuddeback Digital Scouting Camera by Non Typical, Inc.

and the Digital Motion Detector Scouting Camera by Stealth Cam, LLC. Cameras were employed

simultaneously on different nests and were moved from depredated nests to new nests as soon as

possible. The cameras were either set to record 3 pictures/activation or 1 picture + a 10 sec video

clip/activation. Females were not flushed during camera placement unless measurements, candling,

or HOBO placement took place simultaneously. Females were not usually flushed during nest visits

where camera batteries and memory cards were changed.

We also used a video system at Kuparuk through a grant from Sandpiper Technologies, Inc. The

system consisted of a weatherproof miniature video camera attached via a cable to a time-lapse

videocassette recorder (Panasonic AG-1070) housed in a weatherproof case powered by a 12-volt

deep-cycle marine battery. The VCR and the battery were placed about 15 m from the nest. The video

system was set to record 24 h of video on standard T160 VHS videotapes.

Results

Teshekpuk 2004

We found 37 active king eider nests in the study area at Teshekpuk Lake. We also found 22 nests

post-depredation that were likely king eider nests. However, because spectacled eider (Somateria

fischeri) nests look very similar they were not included in estimates of apparent nest success.

Initiation of incubation ranged from 14 to 27 June (Figure 1). Apparent nest success was 24.3% in

2004 (Table 1); however, three of the hens abandoned after camera placement, probably due to the

presence of a camera at the nest. Excluding these females from the analysis raised the apparent

nesting success to 26.5%. King eider nests at Teshekpuk hatched between 12–20 July. Mean clutch

size was 4.25 ± 0.2 (SE, n = 28). Egg length was 65.5 ± 0.21 mm (n = 121), width 43.6 ± 0.13 mm

(n = 121) and mass of fresh eggs 67.6 ± 0.6 g (n = 90). In general, nests at Teshekpuk occurred in low

marshy areas or on islands (n = 37) and not on the barren, dry ridges. Only 3 of the 9 successful nests

occurred on islands (33.3%), although 45.9% of nests found (n = 37) occurred on islands. This pattern

is the opposite of that seen in previous years (Table 1).

The digital cameras proved to be problematic. The Stealth Cam motion detector had only one

sensitivity level and often did not detect events. The females on the nest did not set off the camera

when moving around at the nest, and sometimes didn’t even activate it when leaving the nest. This

problem could be alleviated by placing the camera closer; however, 3 females abandoned after

camera placement. The females that are known to have abandoned were all flushed at the time of

camera placement and 2 of them never returned to the nest. Females did not abandon if cameras were

placed without flushing; however, 1 female that was flushed at camera placement did not abandon. At

least 1 additional female that was flushed at camera placement subsequently failed; it is unknown

whether she abandoned the nest prior to depredation.

Similar low sensitivity problems plagued the Cuddeback cameras. This camera has three levels of

sensitivity to events and eventually we found that a higher setting worked better. The female on

the nest triggered the camera when she moved around at the higher sensitivity. However, we were

expecting the cameras to be too sensitive so that by the time these problems were resolved to some

extent, all the remaining females had hatched. No pictures of actual predation events were recorded

with either camera system.
37

Figure 1. Initiation of incubation by king eiders at two sites on Alaska’s North Slope, Kuparuk

and Teshekpuk, in 2004.

Table 1. Summary of sample size, apparent nest success and percentage of total and

successful nests that occurred on islands at Kuparuk and Teshekpuk from 2002

through 2004.

Year # Nests Apparent Nest

Success

% of All Nests

on Islands

% of Successful

Nests on Islands

2002 44 33.30% 68.20% 92.90%

Teshekpuk 2003 40 17.50% 52.50% 85.70%

2004 37 24.30% 45.90% 33.30%

2002 42 42.90% 44.70% 72.20%

Kuparuk 2003 39 35.10% 51.30% 61.50%

2004 31 25.80% 58.00% 62.50%

Kuparuk 2004

We found 31 active nests at Kuparuk and 23 more nests were found post-depredation. Initiation of

incubation ranged from 9 to 29 June (Figure 1). The period of time in which hens began incubating

was similar between Kuparuk and Teshekpuk; however the peak was a few days later at Teshekpuk.

Apparent nest success was 25.8% (n =31, Table 1). King eider nests at Kuparuk hatched between

6–20 July. Mean clutch size was 4.67 ± 0.21 (SE, n = 24). Egg length was 66.3 ± 0.27 mm (n = 83)

and width 44.5 ± 0.14 mm (n = 84). Fifty-eight percent of nests found (n = 31) occurred on islands

(Table 1).
38

The digital nest cameras experienced similar problems at Kuparuk. No pictures of depredation

events were recorded; however, no females are known to have abandoned there. There is always the

potential that a nest was abandoned prior to predation without our detection. The HOBOs allow us to

determine if the female returned to the nest after flushing, but we did not place HOBOs in every nest.

The video system was only placed on one nest at Kuparuk, because the nest did not fail until the

end of incubation. This did not leave us time to move the system to another nest. We did, however,

manage to record the depredation event at this nest. It was depredated by glaucous gulls (Larus

hyperboreus), presumably the pair that was nesting about 40 m away. The video shows that the

female eider fought off the gulls for at least 12 h prior to taking an incubation break during a period

when the gulls had been out of the picture for 1 or 2 h. She had been on the break for about 7 min

before they appeared and depredated the nest. As far as we can tell the female never came back for

a close look at the nest after this happened.

Future work

Additional funding was obtained through the U.S. Geological Survey to extend this study (Ph.D.

dissertation) at no cost to CMI. The work on nesting success will continue through the field season

of 2005.

Program MARK will be used to estimate nest success (± SE) and to test for site-, year-, and

island/mainland–specific differences in nest survival and to investigate the importance of three spatial

covariates (distance to the nearest conspecific nest, distance to the nearest larid nest, and distance to

the mainland) and habitat covariates on daily nest survival rates [White and Burnham 1999; Dinsmore

et al. 2002]. We will investigate any effects of nesting associations between king eider and associated

nesting larids, both within and between the two sites. The data from the HOBO temperature recorders

will be analyzed for incubation constancy.

We intend to use landcover databases (National Petroleum Reserve–Alaska landcover inventory

database of the Bureau of Land Management and Ducks Unlimited, and the Beechey Point landcover

inventory database of the U.S. Army Cold Regions Engineering and Research Laboratory) and the

random sites to investigate distribution and availability of habitats within the study areas and thus

determine if selection of particular habitats has occurred and how the study sites compare.

Acknowledgements

This study would not be possible without the financial support of the UA Coastal Marine Institute,

ConocoPhillips Alaska, Inc., and the North Slope Borough. We thank Dolores Vinas, Liza DelaRosa,

April Brower, Benny Akootchook, Justin Harth, and Anne Lazenby for logistical help; and Amanda

Prevel, Torsten Bentzen, Rita Frantz, Miranda Wright, and Keith Brady for their help as field

technicians. We also thank ABR, Inc. for cooperation at Kuparuk.
39

References

Anderson B.A., C.B. Johnson, B.A. Cooper, L.N. Smith and A.A. Stickney. 1999. Habitat

associations of nesting spectacled eiders on the Arctic Coastal Plain of Alaska, p. 27–32.

In R.I. Goudie, M.R. Peterson and G.J. Robertson [eds.], Behavior and Ecology of Sea Ducks.

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Bergman, R.D., R.L. Howard, K.F. Abraham and M.F. Weller. 1977. Water birds and their wetland

resources in relation to oil development at Storkersen Point, Alaska. U.S. Fish and Wildl. Serv.

Resour. Publ. 29, Washington D.C., 38 p.

Dinsmore, S.J., G.C. White and F.L. Knopf. 2002. Advanced techniques for modeling avian nest

survival. Ecology 83(12):3476–3488.

Girard, G.L. 1939. Life history of the shoveler. Trans. N. Am. Wildl. Conf. 4:364–371.

Larned, W.W., R. Stehn and R. Platte. 2003. Eider breeding population survey Arctic Coastal Plain,

Alaska 2002. U.S. Fish and Wildlife Service, Anchorage.

Quakenbush, L.T., R.S. Suydam, T. Obritschkewitsch and M. Deering. 2004. Breeding biology of

Steller’s Eiders (Polysticta stelleri) near Barrow, Alaska, 1991–99. Arctic 57(2):166–182.

Suydam, R.S., D.L. Dickson, J.B. Fadely and L.T. Quakenbush. 2000. Population declines of King

and Common Eiders of the Beaufort Sea. Condor 102(1):219–222.

White, G.C., and K.P. Burnham. 1999. Program MARK: Survival estimation from populations of

marked animals. Bird Study 46 Suppl.:120–138.
40

King and Common Eider Migrations Past Point Barrow

Lori T. Quakenbush <lori_quakenbush@fishgame.state.ak.us>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775–7220

Robert S. Suydam <robert.suydam@north-slope.org>

Department of Wildlife Management

North Slope Borough

P.O. Box 69

Barrow, AK 99723-0069

Task Order 85241

Abstract

King (Somateria spectabilis) and common eiders (S. mollissima v-nigra) are important resources

for Native people of northern Alaska and Canada. Both species pass Point Barrow, Alaska twice

annually—during their northward migration in the spring and their southward migration in the fall.

In 1996, we conducted spring and fall counts and compared our results with standardized data from

other counts. The results indicated that both populations had declined by approximately 50% between

1976 and 1996. We have repeated the fall migration counts (July–October 2002 and 2003) and the

spring counts (May 2003 and 2004) in order to update the population trends and gather information

on the behavior of eiders during migration to provide a context for the behavior of individual eiders

instrumented with satellite transmitters (CMI Project: Importance of the Alaskan Beaufort Sea to

King Eiders [Somateria spectabilis]). The counts will also determine if the age composition of eiders

migrating during late fall can be used as an index to annual productivity.

Introduction

King (Somateria spectabilis) and common eiders (S. mollissima v-nigra) wintering in the Bering Sea

and north Pacific Ocean migrate north to nesting areas in Russia, Alaska, and Canada. Most of the

eiders nesting in Alaska and Canada pass by Point Barrow, Alaska when entering and leaving the

Beaufort Sea. At Point Barrow the migration transits very close to shore and the spring passage can

be spectacular. Woodby and Divoky [1982] estimated 113,000 eiders passed in 30 minutes in the

spring of 1976. Murdoch [1885], Bailey [1948], Brueggeman [1980], and others have commented

on the spring passage of eiders, but the magnitude of the spring migration has been estimated only

on a few occasions [Woodby and Divoky 1982; Suydam et al. 1997, 2000a]. By standardizing the

analysis of spring migration counts conducted at Barrow in 1953 [Thompson and Person 1963], 1970

[Johnson 1971], 1976 [Woodby and Divoky 1982], 1987, 1994 [Suydam et al. 1997], and 1996

[Suydam et al. 2000a, b] we determined that the king eider population appeared to remain stable

between 1953 and 1976, but declined by 53% between 1976 and 1996 [Suydam et al. 2000a]. The

common eider population declined by 56% during the same time period [Suydam et al. 2000a].
41

King and common eiders are an important source of fresh protein after a long winter for local

residents of Alaska and Canada [Braund et al. 1993; Fabijan et al. 1997]. Residents of Barrow harvest

more king and common eiders than any other species of waterfowl [Fuller and George 1997]. While

the reasons for the declines are unknown they are of concern.

It appears from our previous work and the reports of others [Thompson and Person 1963; Johnson

1971; Timson 1976] that the number of eiders returning after the end of August may be indicative of

the number of young produced that year. Therefore, we are investigating the age composition of the

fall passage from July into October in 2002 and 2003 to explore the use of late migration numbers as

an indicator of annual productivity. By collecting detailed fall migration data we will be able to

address timing, behavior, molt, and weather conditions related to migration, with the result of a better

understanding of the timing and use of the Beaufort Sea marine and coastal environment by king and

common eiders. The data will provide information that will aid in predicting the number of eiders

using the outer continental shelf area by time of year.

Methods

Summer/fall migration counts in 2002 and 2003 were conducted from land at the base of the Point

Barrow spit (71° 21N, 156° 36W; Figure 1). In 2002, one to two observers counted eiders for up

to 10 h per day from 11 July until 15 October. In 2003 counts will be conducted from 8 July to

15 October. In October the count becomes limited to 2 h per day due to decreasing day length.

Figure 1. Fall eider migration count locations.

Beaufort Sea

Chukchi Sea

Elson Lagoon

Point Barrow

City of

Barrow

Fall Observers

Spring Observers
42

For the spring counts, we established an observation site on the shorefast ice southwest of Point

Barrow (Figure 1). In 2003, the observation site was on a large pressure ridge approximately

20 m high (71° 21N, 156° 43W; Figure 1). The distance to the nearshore lead ranged from 50 to

1500 m depending upon sea ice conditions. In 2004, the observation site was on a pressure ridge

approximately 10 m high (71° 23N, 156° 41W; Figure 1), and the distance to the nearshore lead

ranged from 50 to 1000 m. Both sites allowed a view of eiders migrating along the lead as well as

along the beach. Two observers conducted spring counts for 12 h each day (i.e., 2 h out of every 4)

from 30 April to 2 June 2003 and 2004.

In each count period, regardless of season, data were collected on weather conditions (temperature,

wind speed, wind direction, cloud cover, visibility). For each flock sighted we recorded time,

direction of travel, species composition, number in flock, ratio of males to females for each species,

and other comments on behavioral observations. Observers were trained on species identification and

flock estimation by being paired with an experienced observer. They made independent estimates of

the size of each flock. Estimates between trained observers were generally within 10% or less of each

other.

We calculated daily projected passage estimates and point estimates for total passage of king and

common eiders using methods similar to Suydam et al. [1997]. These estimates include eiders

counted but not identified to species, which are divided between king and common eider categories

in proportion to the king and common eiders that were identified that day. We assumed a constant

movement throughout each 24-h period. The projected total passage was estimated by summing the

daily passage estimates. We also calculated 95% confidence intervals using a procedure for stratified

sampling [Thompson 1992] that treated each day as a separate stratum [Suydam et al. 2000b]. Point

estimates for two time periods are presented. A point estimate for 11 July to 7 September is a

standardized time period which we can compare with counts prior to 1996 [Suydam et al. 2000a].

This shorter time period represents the period prior to when young produced that year have returned.

Therefore, it may be a better estimate of the adult breeding population. The 11 July to 15 October

time period can be compared with our estimate in 1996 only and includes the eiders produced that

year.

Results

Summer/Fall 2002

Some king eiders migrated past Point Barrow before we began counting, as indicated by the king

eiders implanted with satellite transmitters in the Kuparuk oil field by Powell and Phillips (CMI

project: Importance of the Alaskan Beaufort Sea to King Eiders [Somateria spectabilis] [hereafter

Powell et al. CMI project]; Figure 2). Five of ten male king eiders with transmitters passed by Point

Barrow before our count began in 2002. None of the tagged females passed prior to 29 July. The daily

estimated passage of king eiders, however, shows low numbers passing until 15 July (Figure 3) and

large numbers passing on 27 and 29 July, 19 and 24 August, and 12 September.

Our preliminary estimate for the passage of king eiders for the early migration period between 11 July

and 7 September is 493,248 (95% CI 74,332; Table 1). For the later period (11 July–15 October) our

preliminary estimate is 529,271 (95% CI 78,742). Young-of-the-year do not begin to migrate until

September; therefore, fall migration counts after early September provide an index to productivity.

Our estimate of passage of king eiders after 7 September was 36,023 in 2002 or 7.3% of what is

estimated to be the adult projected total passage.
43

Figure 2. Dates that 10 male and 10 female king eiders with satellite transmitters passed

Barrow during summer/fall 2002. Arrow denotes date migration count began

(11 July). Data from A. Powell, unpublished.

Figure 3. Projected daily passage of king and common eiders during summer/fall 2002

at Point Barrow.

Individual King Eiders

Date Past Barrow Fall 2002

27 Aug

23 Aug

19 Aug

15 Aug

11 Aug

7 Aug

3 Aug

28 Jun

24 Jun

30 Jul

10 Jul

2 Jul

6 Jul

26 Jul

22 Jul

18 Jul

14 Jul

012 5 3 4 6 7 8 9 10 11

Males 

Females

King Eider 

Common Eider

50000

40000

30000

20000

10000

0

Jul Aug Sep Oct Nov

Date

Estimated Number

27 Jul

29 Jul

19 Aug

13 Oct

24 Aug

12 Sep

11 Oct
44

Our preliminary estimate for the passage of common eiders for the early migration period was 51,622

(95% CI 21,784) and 176,109 (95% CI 42,390) for the late period. Only 29% of the estimated total

of common eiders passed by before 7 September. The remaining 71% passed by in October, with

the largest numbers passing on 11 and 13 October when 26,592 and 41,143 were estimated to pass,

respectively (Figure 3). Because the majority of common eiders migrate later in the fall, overlapping

with the migration of young-of-the-year, we do not have an index of production for this species. King

eiders comprised 75% of all eiders identified and commons made up 25% (Table 1).

Table 1. Preliminary numbers, projected total passage, and 95% confidence interval of king

and common eiders seen during two time periods of the summer/fall 2002 and 2003

and spring 2003 migrations, and preliminary numbers of eiders seen during the

spring 2004 migration.

King Eider Common

Eider Eider1 TOTAL

Early Summer/Fall 2002 2

Number seen3 60,177 4,810 114,164 179,151

Projected total passage4 493,248 51,622 544,870

95% confidence interval 74,331 21,784

Late Summer/Fall 2002 5

Number seen3 61,881 13,864 134,073 209,818

Projected total passage4 529,271 176,109 705,380

95% confidence interval 78,742 42,390

Spring 2003 6

Number seen3 76,420 24,091 99,902 200,413

Projected total passage4 362,237 119,809 482,046

95% confidence interval 88,851 26,668

Early Summer/Fall 2003 5

Number seen3 50,259 4,629 88,541 143,429

Projected total passage4 405,820 67,195 705,380

95% confidence interval 76,598 11,909

Late Summer/Fall 2003 5

Number seen3 50,559 10,462 94,451 155,472

Projected total passage4 434,057 107,770 541,827

95% confidence interval 76,781 15,222

Spring 2004 7

Number seen3 106,546 23,237 133,512 263,295

Projected total passage4

95% confidence interval

1

Unidentified eiders

2

Early period from 11 July to 7 September (458 hours counted in 2002)

3

Net number of eiders migrating southwest (summer/fall) or northeast (spring)

4

Sum of daily projected passage estimates

5

Late period from 11 July to 15 October (650 hours counted in 2002)

6

30 April–2 June (340 hours counted in 2003)

7

30 April–1 June (280 hours counted in 2004)
45

Summer/Fall 2003

When we began counting on 8 July, 3 of 15 (17%) of the male king eiders with satellite transmitters

had already left the Beaufort Sea (data from Powell et al. CMI project; Figure 4). The earliest female

left on 2 August 2003 and the latest left on 18 September (Figure 4).

Our preliminary estimate for the passage of king eiders for the early migration period between 11 July

and 7 September was 405,820 (95% CI 76,598) and 434,057 (95% CI 76,781) for the later period

(11 July–15 October; Table 1). Our estimate of passage of king eiders after 7 September was 28,237

in 2003 or 7% of what is estimated to be the adult projected total passage.

Our preliminary estimate for the passage of common eiders for the early migration period was 67,195

(95% CI 11,909) and 107, 770 (95% CI 15,222) for the late period. In 2003, 62% of the estimated

total number of common eiders passed by before 7 September. The remaining 38% passed by in early

October (Figure 5). King eiders comprised 80% of all eiders identified and commons made up 20%.

Spring 2003 count

In spring 2003, our preliminary projected passage for king eiders was 362,237 (95% CI 88,851)

and for common eiders it was 119,809 (95% CI 26,668) (Table 1). King eiders comprised 76% and

commons 24% of all eiders identified. Spring appeared to be several weeks early in 2003 and eiders

were seen east of Barrow prior to our start date of 30 April. None of the king eiders with satellite

transmitters implanted in 2002 passed by prior to 30 April (Figure 6).

Spring 2004 count

The spring 2004 estimates are in progress. Our preliminary numbers for birds counted were 106,546

king eiders, 23,237 common eiders, and 133,512 eiders not identified to species. King eiders comprised

82% of all eiders identified and commons made up 18% (Table 1). None of the king eiders with

satellite transmitters implanted in 2003 passed by prior to 30 April (Figure 7).

Carbon and nitrogen isotopes in feathers

We amended our project to incorporate a study designed by Michael Knoche, a Master’s student at

the University of Alaska Fairbanks. His project proposed to use carbon isotope ratios in feathers

collected from king eiders passing Barrow to determine areas used by the king eiders during the fall

molt when the birds are flightless. Feathers grown in one geographical region will retain the isotopic

signature of the region because feathers are metabolically inert after they are grown. The objective

of this amendment is to use carbon and nitrogen isotopes to investigate diet, sexual segregation, and

geographical distribution during wing molt. Molt is energetically taxing, thus molting areas are

important habitats for king eiders and understanding distribution during molt may provide insight

into potential mortality factors.

Mr. Knoche studied feather samples from 23 captive eiders on a known diet to learn how the isotopic

composition of the diet fractionated in the grown feathers and whether differences occur with sex and

age. We obtained permits from the U.S. Fish and Wildlife Service and Alaska Department of Fish and

Game to collect 60 king eiders during 2003, however only 17 king eiders were shot for the project.

Due to the support and generosity of subsistence hunters, the majority of the primary feathers were

donated from 258 king eiders taken for subsistence near Barrow. Primary feathers were also collected

from 39 eiders live captured for a satellite telemetry study (Powell et al. CMI project). In addition to
46

primary feathers, muscle tissue was also taken from the 17 eiders collected for the study. All other

samples were obtained from birds found dead.

Because the literature included several methods and chemicals for cleaning feathers prior to

determining carbon isotope ratios and no data was available on whether the different cleaning

methods affected the results, Mr. Knoche tested each method for differences prior to preparing his

samples. Preliminary results of his study with captive eiders include no difference between sexes for

how isotopes from diet are incorporated into feathers, however hatch-year eiders had lower nitrogen

fractionation than adults. Results indicate that diet during molt for some female king eiders comes

from both marine and freshwater habitats while male diet is marine based. Muscle tissue was not

different from feathers in the spring, but muscle was different in the fall due to the incorporation

of isotopes from a freshwater summer diet during the breeding season. Stable carbon isotopes were

found to be highly correlated with longitude of wing molt location for male king eiders. Mr. Knoche

is writing his thesis as two publishable papers. He is scheduled to defend in November 2004.

Discussion

When comparing the early summer/fall migration period (11 July–7 September) between 2002 and

1996, the total point estimate appears to be higher for king eiders in 2002 (Table 2). Comparing this

early time period among years avoids comparing estimates that may include large numbers of young

from highly productive years with low numbers in poorly productive years. In 1996, there were large

numbers of king eiders passing in September and October; this pattern was not seen in 1994 [Suydam

et al. 1997] or in 2002 or 2003 and may indicate that 1996 was a good reproductive year. Comparing

the early time periods may be most appropriate and may indicate that the king eider population has

increased or at least not decreased. We will use all of the spring and summer/fall estimates in our final

report to better interpret this result.

Although 50% and 17% of the male king eiders fitted with satellite transmitters passed by Point

Barrow prior to the beginning of our counts in fall 2002 and 2003, respectively, we do not think those

percentages represent the overall timing of male king eider migration (Figures 2 and 4). The projected

daily passage rates for the early season (Figure 3) did not indicate any large numbers of eiders passing

until 15 July. The early departure of the male king eiders with implants may have been a reaction to

surgery or it may be that males on the western portion of the breeding range, nearer to Barrow, arrive

there early each year.

The bulk of the common eider summer/fall migration occurs in October; therefore, we will only

compare the late time period estimates. The projected total passage of common eiders was higher in

summer/fall 2002 than in 1996, but lower in 2003 (Table 2). The spring 2003 and 2004 estimates will

help us evaluate this result.

Students

Michael Knoche’s research activities were reported above. He was also involved in conducting the

migration counts, scheduling observers, and ensuring quality counts and data collection. Rita Frantz,

an undergraduate student from Ilisagvik College in Barrow has been an observer for all of the

migration counts, she streamlined the data entry process, entered and proofed the data and has been

assisting in data queries for analysis.
Addendum: King and Common Eider Migrations Past Point Barrow

Lori T. Quakenbush and Robert S. Suydam

Figure 4. Dates 15 male and 8 female king eiders with satellite transmitters passed Barrow during

summer/fall 2003. Arrow denotes date migration count began (8 July). Data from A.

Powell.

Figure 5. Daily passage of king and common eiders during summer/fall 2003 at Point Barrow.
Figure 6. King eider spring 2003 migration.

Figure 7. King eider spring 2004 migration.
47

Table 2. Estimated passage for king and common eiders including 95%

confidence intervals for the early (11 July–7 September) and late

(11 July–15 October) time periods in 1996, 2002, and 2003.

King Eider Common Eider

Early Summer/Fall 1996 1

Projected total passage2 330,248 N/A

95% confidence interval 70,725 N/A

Projected total range3 259,493–400,943 N/A

Early Summer/Fall 2002

Projected total passage2 493,248 N/A

95% confidence interval 74,332 N/A

Projected total range3 418,916–567,580 N/A

Early Summer/Fall 2003

Projected total passage2 405,820 N/A

95% confidence interval 76,598 N/A

Projected total range3 329,222–482,418 N/A

Late Summer/Fall 1996 3

Projected total passage2 507,667 111,635

95% confidence interval 84,680 42,440

Projected total range3 422,987–592,347 69,195–154,075

Late Summer/Fall 2002

Projected total passage2 529,271 176,109

95% confidence interval 78,742 42,390

Projected total range3 450,667–608,013 133,719–218,499

Late Summer/Fall 2003

Projected total passage2 434,057 107,770

95% confidence interval 76,781 15,222

Projected total range3 357,276–510,838 92,548–122,922

1

From Suydam et al. [2000a]

2

Sum of daily projected passage estimates

3

Upper and lower limits of passage using confidence interval

Presentations

Lori Quakenbush made the annual CMI presentation of this study in February 2004 in Fairbanks. In

addition to presenting his work at the CMI annual presentations, Michael Knoche made a presentation

at the Alaska Cooperative Fish and Wildlife Research Unit review, also in February, and at the

American Ornithologist Union in August 2004. Robert Suydam gave an oral presentation in

Anchorage at the Alaska Bird Conference titled, “Status of King and Common Eiders Migrating Past

Point Barrow, Alaska”.
48

Acknowledgements

This project is funded by the University of Alaska Coastal Marine Institute (Minerals Management

Service) with logistic support provided by the North Slope Borough, Department of Wildlife

Management. We are grateful to Lynne Dickson of the Canadian Wildlife Service for providing

support and travel for Andrea Hoover and Garnet Raven to assist us with counts. The U.S. Fish

and Wildlife Service was generous in allowing their employees, Dominic Bachman, Tim

Obritschkewitsch, and Nate Pamperin to assist our project. Quaiyaan Aiken, Paula Earp, Quaiyaan

Opie, Bruce MacTavish, Rebecca McGuire, Leslie Pierce, and Yumiko Uchiro assisted in the

migration counts. In spring 2003, Michael Wald found the spring perch, made a trail to get there, and

conducted counts. We thank Craig George for helping us getstarted in the spring and Ben Akootchook

for mechanical talent. We are especially grateful to the subsistence hunters of Barrow who allowed

us to look at their birds, graciously donated samples, and shared their knowledge of eiders with us.

Quyanaq. We also appreciate the data sharing that has occurred with Abby Powell and Laura Phillips

and we look forward to more in the future.

The migration portion of this project wasreviewed and approved by the Institutional Animal Care and

Use Committee (IACUC) of the University of Alaska Fairbanks(#02-05). The portion of this study

using stable isotopes to identify molting areas of king eiders was also reviewed and approved by the

IACUC (#03-15).

References

Bailey, A.M. 1948. Birds of Arctic Alaska. Colorado Museum of Natural History. Pop. Ser. No. 8.

317 p.

Braund, S.R., K. Brewster, L. Moorhead, T.P. Holmes and J.A. Kruse. 1993. North Slope

Subsistence Study, Barrow, 1987, 1988, and 1989. Techn. Rep. No. 149, OCS Study, MMS 91-

0086. Anchorage.

Brueggeman, J.J. 1980. Coastal occurrence of birds at Point Barrow, Alaska, in spring. Murrelet

61:31–34.

Fabijan, M., R. Brook, D. Kuptana and J.E. Hines. 1997. The subsistence harvest of King and

Common eiders in the Inuvialuit Settlement Region, 1988–1994, p. 67–73. In D.L. Dickson [ed.],

King and Common Eiders of the Western Canadian Arctic. Occ. Pap. No. 94. Can. Wildl. Ser.,

Ottawa.

Fuller, A.S., and J.C. George. 1997. Evaluation of Subsistence Harvest Data from the North Slope

Borough 1993; Census for Eight North Slope Villages: For the Calendar Year 1992. Unpubl.

report, North Slope Borough, Department of Wildlife Management, Barrow, Alaska.

Johnson, L.L. 1971. The migration, harvest and importance of waterfowl at Barrow, Alaska. M.S.

thesis. University of Alaska, Fairbanks.

Murdoch, J. 1885. Birds, p. 104–128, In P.H. Ray [ed.], Report of the International Polar Expedition

to Point Barrow, Alaska. Part 4. Government Printing Office, Washington, D.C.

Suydam, R., L. Quakenbush, M. Johnson, J.C. George and J. Young. 1997. Migration of King and

Common eiders past Point Barrow, Alaska, in spring 1987, spring 1994 and fall 1994, p. 21–28.

In D.L. Dickson [ed.], King and Common Eiders of the western Canadian Arctic. Occ. Pap. No.

94. Can. Wildl. Ser., Ottawa.
49

Suydam, R.S., D.L. Dickson, J.B. Fadely and L.T. Quakenbush. 2000a. Population declines of king

and common eiders of the Beaufort Sea. Condor 102:219–222.

Suydam, R.S., L.T. Quakenbush, D.L. Dickson and T. Obritschkewitsch. 2000b. Migration of King,

Somateria spectabilis, and Common, S. mollissima v-nigra, Eiders past Point Barrow, Alaska,

during spring and summer/fall 1996. Can. Field-Naturalist 114:444–452.

Thompson, D.Q. and R.A. Person. 1963. The eider pass at Point Barrow, Alaska. J. Wildl. Mgmt.

27:348–356.

Thompson, S.K. 1992. Sampling. John Wiley & Sons, Inc., New York. 343 p.

Timson, R.S. 1976. Late summer migration at Barrow, Alaska, p. 354-400. In Environmental

Assessment of the Alaskan Continental Shelf. Principal Investigators’ Reports, April–June 1976,

Vol. 1. U.S. Dept. Comm., NOAA, Boulder, Colorado.

Woodby, D.A., and G.J. Divoky. 1982. Spring migration of eiders and other waterbirds at Point

Barrow, Alaska. Arctic 35:403–410.
50

Susceptibility of Sea Ice Biota to Disturbances in the

Shallow Beaufort Sea. Phase 1: Biological Coupling of

Sea Ice with the Pelagic and Benthic Realms

Rolf R. Gradinger <rgradinger@ims.uaf.edu>

Bodil A. Bluhm <bluhm@ims.uaf.edu>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775-7220

Task Order 85242

Abstract

The abundance, composition and isotopic signatures of biological communities in sea ice, in the

water column and on the sea floor were studied in near-shore fast ice-covered waters close to

Barrow, Alaska. During this project, we collected field samples in April 2002 and February, April

and May/June 2003 using ice corers, water samplers, plankton nets and sediment corers at two

locations which differed mainly in terms of sea ice sediment load. A strong relationship between

sediment load and biological properties was observed: The spring ice algal bloom was most

pronounced in the sediment-free fast ice, while it remained up to two orders of magnitude lower in

the sea ice with high sediment load. The bloom in clean ice was largely produced by diatoms 20 m,

while these were rare in the dirty sea ice or in the plankton samples. Linked to the dissimilarity in

algal abundances, low abundance of ice meiofauna was observed in sediment-loaded ice whereas

abundances were high in sediment-free ice. Stable isotope analysis of ice fauna, zooplankton and ice

and pelagic particulate organic matter (POM) suggests, through seasonally progressive enrichment

of ice POM and ice fauna, that sea ice POM is used as a food source by ice meiofauna and iceinhabiting benthic polychaete juveniles. Although effects were previously assumed, our study provides

the first quantitative evidence that sea ice sediments play a pivotal role in structuring Arctic sea ice

ecosystems. Secondly, this study showed that cryo-pelagic–benthic coupling is tight in this shallow

fast ice covered area. Some of the results of our study were presented at several scientific meetings

and are currently in the publication process.

Background and Scientific Goals

Sea ice is a crucial habitat for a diverse community of bacteria, protists and metazoans. While the offshore multi-year sea ice has a high fraction of endemic species, the biology of fast ice has close links

to water and benthic communities, including organism migration from the benthos into the sea ice.

Specifically, benthic polychaetes spend part of their life cycle as juveniles and larvae within the sea

ice, while presumably feeding on the high ice algal biomass accumulating during spring.

Light has previously been identified as the main controlling factor governing the seasonality of the

ice biota. Various studies revealed that snow is the major modifying factor, other than season, for
51

alterations of the light availability and ultimately growth of ice algae. However, the effects of

sediment load, that can be substantial in Arctic sea ice, had not been investigated so far. Our project

targeted this question by comparing the seasonal development of the ice biota at two sites, which

mainly differed in the amount of incorporated sea ice sediments (Figure 1). We hypothesized that

increasing sediment load would cause a reduction of the biological production in the sea ice and more

specifically, reduce the abundance of ice fauna including ice-associated larvae and juveniles of

benthic polychaetes.

Figure 1. Location of project sampling sites. Site 1: sediment-free (clean) ice, Site 2:

sediment-loaded (dirty) ice. BASC: Barrow Arctic Science Consortium

facilities.

Summary of Previously Reported Results

The major part of our results was presented in detail in CMI Annual Report No. 10 [Gradinger and

Bluhm 2004]. In summary, our major findings based on the field-work in Barrow were:

a) The observed sea ice sediment load in so-called “dirty sea ice” (102 mg L–1)

caused a reduction of available light for algal growth in the sea ice and the sea

water by two orders of magnitude relative to clean ice with only a 6 mg L–1

particle load.
52

b) Within patches of dirty sea ice, the abundance of ice algae and ice meiofauna

were one to two orders of magnitude below the abundances in clean sea ice

during the maximum spring bloom (late May).

c) 13C stable isotope values for sea ice algae were progressively enriched with the

advancing season in clean ice, which can be explained by carbon limitation of

algal growth in the biomass-rich bottom layers of clean ice. Ice algal values in

dirty ice remained rather constant over time.

d) Variability of ice and pelagic parameters within each sampling location and

sampling period were considerably lower than variability between sites and

months.

The first manuscript entitled, “The pivotal role of sea ice sediments for the seasonal development of

near-shore Arctic fast ice biota off Barrow, Alaska” was submitted to Marine Ecology Progress Series

(authors: Gradinger, Bluhm and Nielson) and was accepted with revisions. Referees requested the

incorporation of additional data for which additional analyses are currently underway

—resubmission is expected this year.

Results Previously Not Reported

Algal counts

Currently, the particle analysis is being finalized. The major tool for processing the particle samples

taken from the sea ice and water column was the FlowCAM, an image analysis–based flow

cytometer. Three digital pictures per second are recorded from the continuous sample stream and

are simultaneously analyzed for particle abundance and size (Figure 2a–f). Absolute particle

abundances were calculated based on the mean number of particles per image and a minimum count

of 400 particles per size fraction and sample. The digital images of the individual particles were used

to identify the particle composition within each selected size fraction (5 to <10 m, 10 to <20 m,

and 20 m). Four samples per site, realm and sampling period were analyzed.

Examples of recorded pictures in the three size classes are presented in Figure 2. Mean values

(± standard deviations) for February and May 2003 are presented in Figure 3. Large single-celled

diatoms dominated the size fraction 20 m in May 2003 in all clean ice cores (Figure 2f), while

they were nearly absent from all other samples (e.g., Figure 2c). The growth of diatoms caused the

significant increase in particle numbers in this size fraction between February and May, while no such

change was seen in the dirty sea ice at site 2 or in the plankton samples (Figure 3). This observation is

consistent with the relative changes in chlorophyll a and particulate organic carbon concentrations,

which we described in the previous CMI annual report [Gradinger and Bluhm 2004].

Stable isotope analysis of ice meiofauna and zooplankton

Stable isotope analysis of more than 300 samples was conducted as described in Annual Report

No. 10 [Gradinger and Bluhm 2004]. For a single sample, 0.2–0.4 mg dry mass of faunal tissue

are required at current equipment sensitivity. Depending on the body mass of a species/taxon,

this amount required pooling up to several hundred individuals, e.g., 30–50 turbellarians, 50–80

polychaete juveniles, and 150–300 copepod nauplii or nematodes. Due to the considerable effort

required to pick this amount from a sample and low faunal densities in the winter and/or at the

sediment-loaded site, samples could only be obtained from taxa that were reasonably common at

a site at any given time.
53

Figure 2. Examples of FlowCAM images from sea ice sites 1 and 2 collected in May 2003.

Individual particles are consecutively numbered and labeled as they flow through the

system (in situ, which explains the number and sequence).
54

Figure 3. Abundance (n/ml) (mean ± SD) of particles larger than 20 m in sea

ice and water samples from sites 1 and 2 in 2003. The increase at site

1 is due to growth of sea ice diatoms (see Figure 2f).

Mean 13C and 15N values (± standard deviations) of all analyzed ice meiofauna and zooplankton for

all sampling periods are compiled in Tables 1 and 2, respectively. Figure 4 shows the distribution of

the 13C values of the more common taxa by taxonomic groups and sampling periods. At the clean

ice site (Figure 4a), several ice meiofauna taxa showed progressively enriched 13C signatures with

the ongoing season, e.g., by approximately 7‰ in polychaete juveniles, nematodes and turbellaria.

These taxa apparently followed the ice particulate organic matter (POM) 13C signatures, suggesting

that they are more tightly linked to ice algal production for food than the sympagic amphipod

species Gammaracanthus loricatus and Onisimus litoralis. The lack of isotopic enrichment in these

amphipods from February 2003 to April 2003, when ice algae became progressively enriched, may

support Carey and Boudrias’ [1987] observation from stomach content analysis that O. litoralis

utilized ice algae when abundant while also feeding on different prey at other times. Faunal densities,

and therefore the number of isotope samples, were too low to observe any trends at the dirty ice site.

However, the lack of isotopic turnover rates makes solid conclusions premature.

A slight enrichment in 13C occurred in several zooplankton taxa at the clean ice site (Figures 4b and

5b), e.g., in nauplii and cyclopid copepods, while the other sampled taxa revealed no obvious trends.

Partial feeding on enriched particles that were released from the sea ice during first melting processes

might explain the enrichment in selected zoopankton taxa late in the sea ice season. Pelagic POM, in

contrast, remained rather stable over the time sampled with respect to its carbon isotopic signature

and could, therefore, not trigger any isotopic enrichment in its grazers. At site 2, pelagic POM isotope

values were more variable between sampling periods while zooplankton signatures showed very little

variability. The rather depleted pelagic POM signatures at both sites in April 2003 (means –27.1‰

and –28.3‰) might originate from terrestrial material that may for unknown reasons have been

abundant at that time.
55

Table 1. 13C isotopic signatures for sea ice meiofauna and zooplankton from sampling sites 1 and 2 for 2002 and 2003. A: M

for n = 2 to n = 9 and individual 13C values for n = 1 . B: Standard deviations (‰) for n  3 only. The species/taxa ar

order. No samples were taken at site 2 in May 2002. Note that site 2 had clean ice in 2002 and dirty ice in 2003. POM 

organic matter.

ASite/Realm Site 1: Chukchi Sea, sea ice Site 2: Beaufort Sea, sea ice Site 1: Chukchi Sea, water column Site 2Date Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 Apr 03 May 03 Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 0POM –20.42 –13.39 –24.92 –23.83 –15.52 –21.50 –25.54 –25.83 –24.17 –25.95 –24.24 –24.69 –27.14 –24.30 –24.5Planula –21.82Ctenophora –21.36Turbellaria –19.30 –17.08 –21.40 –15.36 –20.16 –16.86 –21.52 Rotifers –21.95 –22.98Nematoda –16.85 –23.25 –15.63Polychaeta –22.17 –16.20 –22.73 –22.31 –19.59 –18.02 –23.36 Trochophora –20.08 –21.77Nauplii –25.19 –23.21 –24.05 –20.37 –25.61 –22.28 Calanoidea –24.32 –22.11 –21.19 –23.57 –25.21 –21.33 –21.8Calanus glacialis –24.71 –23.4Harpacticoidea –22.58 –17.01 –20.47 –22.87 –23.2Cyclopoidea –24.15 –21.65 –19.77 –21.37 Onisimus litoralis –19.34 –22.02Gammaracanthus loricatus –19.24 –20.40Chaetognatha –21.27 BSite/Realm Site 1: Chukchi Sea, sea ice Site 2: Beaufort Sea, sea ice Site 1: Chukchi Sea, water column Site 2Date Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 Apr 03 May 03 Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 0POM 0.20 2.15 1.64 0.95 0.76 0.11 0.44 0.19 0.15 3.79 0.17 1.85 1.24 2.72 0.1Turbellaria 1.44 0.38Nematoda 0.62Polychaeta 0.66 0.33 1.09Nauplii 0.76 Calanoidea 2.88 0.46 0.1Calanus glacialis 2.02 0.9Harpacticoidea 1.06 1.44 Cyclopoidea 0.21Onisimus litoralis 1.78 1.42Gammaracanthus loricatus 0.97 0.47

55
56

Table 2. 15N isotopic signatures for sea ice meiofauna and zooplankton from sampling sites 1 and 2 for 2002 and 2003. A: Me

n=2 to n=9 and individual 15N values for n = 1. B: Standard deviations (‰) for n  3 only. The species/taxa are sor

No samples were taken at site 2 in May 2002. Note that site 2 had clean ice in 2002 and dirty ice in 2003. POM = partThis table contains fewer data than Table 1 since some samples had sufficient mass for 13C measurements, but not fo

ASite/Realm Site 1: Chukchi Sea, sea ice Site 2: Beaufort Sea, sea ice Site 1: Chukchi Sea, water column Site 2Date Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 Apr 03 May 03 Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 0POM 7.74 7.52 6.34 7.90 10.07 8.43 5.61 11.90 10.19 5.92 8.07 15.14 7.2Planula 11.50Ctenophora 12.96Turbellaria 8.50 9.69RotifersNematoda 6.00 12.00 9.13 11.66Polychaeta 7.74 5.71 13.89 8.24 8.49 10.27 7.44 14.63 10.44 Trochophora 11.22 11.39Ostracoda 13.97Nauplii 10.70 9.62 14.04 10.04 10.48 15.42 Calanoidea 12.95 12.56 10.45 15.47 13.6Calanus glacialis 11.40 11.8Harpacticoidea 7.60 7.39 9.95 11.85 9.5Cyclopoidea 11.84 10.58 10.88 10.57 Onisimus litoralis 12.90 12.73Gammaracanthus loricatus 12.59 11.34Chaetognatha 14.44 Copepoda 9.93 9.18BSite/Realm Site 1: Chukchi Sea, sea ice Site 2: Beaufort Sea, sea ice Site 1: Chukchi Sea, water column Site 2Date Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 02 Feb 03 Apr 03 May 03 Apr 02 May 02 Feb 03 Apr 03 May 03 Apr 0POM 0.38 1.03 1.73 0.65 0.56 0.41 0.61 1.89 0.98 0.39 1.30 0.43 0.9Turbellaria 0.78Nematoda 1.54Polychaeta 2.03 2.03 1.92 1.14Trochophora 0.89Nauplii 1.90 Calanoidea 0.77 0.78 0.4Calanus glacialis 1.62 0.9Harpacticoidea 0.91Cyclopoidea 0.34Onisimus litoralis 1.24 0.54Gammaracanthus loricatus 1.73 0.95

56
57

Figure 4. Distribution of 15C values of sea ice fauna and zooplankton from the Barrow area by

taxonomic groups. Site 1: Clean ice – sea ice fauna (a) and zooplankton (b). Site 2: Dirty

ice – sea ice fauna (c) and zooplankton (d).

Figure 5 shows the 13C isotopic signatures for sea ice meiofauna and zooplankton as dependent

on the 13C values of sea ice and pelagic POM, respectively. Figure 5a demonstrates a significant

positive correlation between sea ice fauna 13C signatures and ice POM 13C signatures (Kendall rank

correlation test, p = 0.04), which, again, suggests a strong dependence of ice fauna on sea ice POM as

a food source. At the dirty ice site and for the zooplankton of two sites (Figure 5), there may also be a

positive correlation of faunal 13C signatures with POM 13C signatures, at least for zooplankton, but

the progressive enrichment of both components with season is not obvious. The extreme enrichment

in 13C in a sea ice environment has previously been documented for ice algae [Hobson et al. 1995;

Schubert and Calvert 2001], but this study is the first to document isotopic signatures in general,

(in sea ice meiofauna and sympagic amphipods) and their ice POM–dependent enrichment in

particular. Further analysis is currently focused on quantifying the utilization of the different carbon

sources ‘pelagic POM’ and ‘sea ice POM’ in the different realms and sites at different stages in

phytoplankton and ice algal blooms. As yet, differences in taxon-specific turnover rates are unknown

and complicate the interpretation of the data; this has led to a related thesis project for School of

Fisheries and Ocean Sciences (SFOS) graduate student, Mette Nielson (see below).
58

Figure 5. 13C isotopic signatures for sea ice meiofauna and zooplankton from sampling sites 1

and 2 for 2002 and 2003 as dependent on the 13C values of sea ice and pelagic POM,

respectively. Site 1: Clean ice – sea ice meiofauna (a) and zooplankton (b). Site 2: Dirty

ice – sea ice fauna (c) and zooplankton (d). Only the values collected in 2003 are presented

in (c) as this was the time of dirty ice at this location.

A paper presenting the stable isotope results, preliminarily entitled, “Trophic dynamics and food

sources in land fast ice off Barrow, Alaska: Lessons learned from stable 13C and 15N isotope analysis”

is currently being prepared and will be submitted to the journal Marine Biology in December 2004.
59

Outreach and Presentations

We established a website presenting our project on the UAF School of Fisheries and Ocean Sciences

server which has been accessible since January 2003. As of mid-September 2004, the site had

received close to 700 hits.

Mette Nielson, an SFOS graduate student, reported on the project’s progress at the CMI Annual

Research Review in February 2004 in Fairbanks. Results from this study were also presented at the

SEARCH (Study of Environmental Arctic Change) Open Science Meeting in Seattle (26–30 October

2003).

Mette Nielson started as a graduate student at SFOS in spring 2003. She has worked and will continue

working on part of the collected material. She received CMI funding to participate in the Barrow field

phase and data analysis. She is currently working on her master’s thesis, which is based on data and

experiences collected during this CMI project.

References

Carey, A.G., Jr., and M.A. Boudrias. 1987. Feeding ecology of Pseudalibrotus (=Onisimus) litoralis

Kroyer (Crustacea:Amphipoda) on the Beaufort Sea inner continental shelf. Polar Biol. 8:29–33.

Gradinger, R.R., and B.A. Bluhm. 2004. Susceptibility of sea ice biota to disturbances in the shallow

Beaufort Sea. Phase 1: Biological coupling ofsea ice with the pelagic and benthic realms, p. 70–78.

In University of Alaska Coastal Marine Institute Annual Report No. 10. OCS Study MMS 2004-

002, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region.

Hobson, K.A., W.G. Ambrose, Jr. and P.E. Renaud. 1995. Sources of primary production,

benthic–pelagic coupling, and trophic relationships within the Northeast Water Polynya: Insights

from 13C and 15N analysis. Mar. Ecol. Prog. Ser. 128:1–10.

Schubert, C.J., and S.E. Calvert. 2001. Nitrogen and carbon isotopic composition of marine and

terrestrial organic matter in Arctic Ocean sediments: Implications for nutrient utilization and

organic matter composition. Deep-Sea Res. I 48:789–810.
60

Role of Grazers on the Recolonization of Hard-Bottom

Communities in the Alaska Beaufort Sea

Brenda Konar <bkonar@guru.uaf.edu>

Global Underwater Research Unit

University of Alaska Fairbanks

Fairbanks, AK 99775-7220

Task Order 85244

Abstract

This project is expanding on the recovery work completed by Dunton [1985] to determine the

importance of grazing to recolonization rates of sessile organisms at the Boulder Patch. A simple

manipulative study was set up in the summer of 2002 to test the suggestion that invertebrate grazing

is associated with the slow recovery of Boulder Patch communities. This study was sampled in 2003

and 2004. No recruitment was seen on any of the cleared rocks and little difference was seen in the

control rocks. As the experiment is still running, it would be very beneficial to resample again in

2005 to look for recruitment. This study strongly suggests that any perturbations causing scouring

of hard substrate in the Beaufort Sea will result in very slow recovery of the community.

Background

Alaska’s Beaufort Sea shelf is typically characterized by silty sands and mud and as having an

absence of macroalgal beds and associated organisms [Barnes and Reimnitz 1974]. In 1971, a diverse

kelp and invertebrate community was discovered near Prudhoe Bay in Stefansson Sound, Alaska.

Since its discovery, the Boulder Patch has been subject to much biological and geological research

[Dunton et al. 1982; Dunton and Schell 1987; Dunton and Jodwalis 1988; Dunton 1990; Martin and

Gallaway 1994; MMS 1996, 1998; Dunton and Schonberg 2000]. This research stems from a need to

protect sensitive biologically-productive regions, while allowing oil exploration in the surrounding

areas [Wilson 1979].

The Boulder Patch contains large numbers of cobbles and boulders that provide a substrate for

attachment for a diverse assortment of invertebrates and several species of red and brown algae. The

predominant alga is the brown Laminaria solidungula, which constitutes 90% of the brown algal

biomass [Dunton et al. 1982]. This alga is an important food source to many benthic and epibenthic

organisms [Dunton and Schell 1986]. Differences in infaunal abundance and biomass between the

Boulder Patch and peripheral sediment areas demonstrate the importance of this unique habitat

[Dunton and Schonberg 2000]. In the Boulder Patch, algae and epilithic invertebrates cover nearly

all exposed substrate, with the exception of recently upturned rocks [Dunton and Schonberg 2000].

A recolonization experiment in the Boulder Patch has shown that recovery of denuded areas is slow

[Dunton et al. 1982]. In temperate systems, algal communities can recover to previous densities

within one year of denuding [Foster 1975], but in the Boulder Patch 50% of the substrate was still
61

bare three years after an initial disturbance [Dunton et al. 1982]. One of the primary reasons

suggested for the slow recolonization is grazing by invertebrates [Dunton et al. 1982]. Motile

herbivorous, omnivorous, and carnivorous invertebrates such as chitons, snails, seastars and

polychaetes have been frequently observed in the Boulder Patch [Dunton et al. 1982]. Many studies

have shown that grazers can be very important in structuring communities [Johnson et al. 1997;

Worm and Chapman 1998; Jenkins et al. 1999; Ojeda and Muñoz 1999; Morton 1999; Wilson et al.

1999; Konar 2000].

To achieve the goal of determining if grazing/predation is associated with the slow recruitment in the

Boulder Patch, various comparisons were set up using exclusion cages, cage controls, and natural rock.

Objective and Hypothesis

Objective:

Determine if grazing is limiting the rate of recruitment of hard substrate communities

in the Boulder Patch.

The specific hypothesis is:

H1 There is no significant difference between recruitment of sessile organisms on

bare boulders with and without cages to exclude mobile invertebrates.

Experimental Methods

This experiment was set up in summer of 2002. The boulders used in this study were collected

from the Boulder Patch (DS11, Figure 1). Because of the difficulty in removing living material from

rocky substrata underwater, all boulders were brought to the surface and cleared. After five days, the

denuded rocks (with and without cages and cage controls) were placed back into the field.

Figure 1. Chart of Boulder Patch showing Dive Site 11 (DS11). Hatched

polygons are areas with high boulder/cobble density.

Alaska
62

Six cages were deployed to exclude large mobile invertebrates at each of three locations within

DS11, totaling 18 cages. The cages in this experiment were constructed of stainless steel mesh

(Figure 2). All cages were coated with a non-toxic antifouling compound to inhibit growth of sessile

invertebrates and algae. Eighteen cage controls were also deployed to detect any artifacts caused by

the cages, such as decreased light levels or increased sedimentation. These controls were cages that

had holes cut into the sides so that invertebrates could easily pass through them (Figure 3).

Figure 2. Cage rock in situ. Figure 3. Cage control in situ.

For comparison, 18 cleared rocks were deployed with no cages to determine natural recruitment.

As a control for natural changes in the community during the time period of this experiment, 18

non-cleared boulders were also examined.

In 2004, surveys were conducted at DS11 to determine grazer composition and abundance. For this,

five random 0.25-m2

 quadrats were surveyed along three different 30-m transects. From each quadrat,

all large mobile invertebrates and boulders were collected and placed in a fine mesh bag. Bags were

brought to the boat and grazers were sorted and identified.

Preliminary Results

Cages

No recruitment of any sessile organisms was seen during this study on any of the cleared boulders

(caged, partially caged and uncaged). Variation in the community composition of the uncleared

boulders did not significantly fluctuate over time (Figure 4). The majority of uncleared boulders were

covered with encrusting coralline algae and foliose algae, while very little (a mean of 2% or less) was

bare. Foliose algae were primarily Phycodrys rubens, averaging 21% over the three year period.

Other species whose overall averages ranged from 3 to 8% included Phyllophora truncata, Dilsea

integra and Odonthalia dentata.

Grazer surveys showed that two different species of chitons (Amicula vestita and Ischnochiton spp.)

and seastars were the most abundant grazers in this area (Figure 5). A few gastropods also were seen

but their densities were very low.
63

Figure 4. Mean percent cover (±1 s.e.) of bare rock, encrusting coralline

algae, total foliose algae and total invertebrates on uncleared

control boulders from 2002, 2003 and 2004.

Figure 5. Mean percent cover (±1 s.e.) of various grazers on uncleared

control boulders from 2004.

Light

Although light attenuation was consistently lower in 2003 than in 2004 (Figure 6), bottom irradiance

was similar between years. Light readings were taken on two separate days at DS11 in 2003 and five

days in 2004.

Sediments in the water column cause this lack of light. These sediments also could be seen on the

encrusting organisms and rocks. Since there was no growth on the cleared rocks in this first year and

sediments were found on the substrate, a pilot experiment was set up to test a method to eliminate

sediments from a settling surface. In 2003, two sets of plexiglass settling plates were deployed in the

middle of DS11 (Figure 7).
64

Figure 6. Mean light attenuation (±1 s.e.) at DS11 in 2003 and 2004.

n=5 in 2004 and n=2 in 2003.

Figure 7. Plexiglas settling plates used to minimize sediments

on a settling surface. Arrow points to second plate

where less sedimentation will occur and settlement

is more likely.

These settling plates are made of clear plexiglas so that light will penetrate through both plates and

recruitment will not be hindered by sedimentation on the bottom plate because it is protected by the

top plate. The plates were revisited in 2004 and were found to be relatively free of sediments but also

had no growth on them. Because they appeared to minimize sedimentation on a surface, I set up two

sets of plates in each of the three experimental areas in DS11, totaling six sets. It is hoped that these

plates will be resampled in 2005.
65

Preliminary Conclusions and Recommendations

No growth occurred on any cleared substrate during the course of this experiment. This does not

necessarily mean that grazing does not have any effects on recruitment of sessile organisms at the

Boulder Patch but it does imply that recruitment is a slow and tenuous process. Grazers were seen at

DS11 in 2004 and there was evidence of grazing on many adult Laminaria solidungula plants (Figure 8).

Figure 8. Grazing marks can be seen on these adult Laminaria solidungula plants. Arrows point

to specific bite marks.

This study strongly suggests that any perturbations causing scouring of hard substrate in the Beaufort

Sea will result in very slow recovery of the community. It is recommended that this caging experiment

and the new settling plates be monitored for another year. This will give a full three years of

recruitment data that will make comparisons to other areas very insightful.

References

Barnes, P.W., and E. Reimnitz. 1974. Sedimentary processes on arctic shelves off the northern coast

of Alaska, p. 439-476. In J.C. Reed and J.E. Sater [eds.], The Coast and Shelf of the Beaufort Sea,

Arctic Institute of North America, Arlington, VA.

Dunton, K.H. 1985. Trophic dynamics in marine nearshore systems of the Alaskan high arctic. Ph.D.

dissertation. University of Alaska Fairbanks.

Dunton, K.H. 1990. Growth and production in Laminaria solidungula: Relation to continuous

underwater light levels in the Alaskan High Arctic. Mar. Biol. 106:297–304.

Dunton, K.H., and C.M. Jodwalis. 1988. Photosynthetic performance of Laminaria solidungula

measured in situ in the Alaskan High Arctic. Mar. Biol. 98:277–285.

Dunton, K.H., and D.M. Schell. 1986. A seasonal carbon budget for the kelp Laminaria solidungula

in the Alaskan high arctic. Marine Ecology Progress Series 31:57–66.

Dunton, K.H., and S.V. Schonberg. 2000. The benthic faunal assemblage of the Boulder Patch kelp

community, Chapter 18. In The Natural History of an Arctic Oil Field. Academic Press.

Dunton, K.H., E. Reimnitz and S. Schonberg. 1982. An Arctic kelp community in the Alaskan

Beaufort Sea. Arctic 35:465–484.
66

Foster, M.S. 1975. Algal succession in a Macrocystis pyrifera forest. Mar. Biol. 32:313–329.

Jenkins, S.R., S.J. Hawkins and T.A. Norton. 1999. Interaction between a fucoid canopy and limpet

grazing in structuring a low shore intertidal community. J. Exp. Mar. Biol. Ecol. 233:41–63.

Johnson M.P., M.T. Burrows, R.G. Hartnoll and S.J. Hawkins. 1997. Spatial structure on moderately

exposed rocky shores: Patch scales and the interactions between limpets and algae. Mar. Ecol.

Prog. Ser. 160:209–215.

Konar, B. 2000. Seasonal inhibitory effects of marine plants on sea urchins: Structuring

communities the algal way. Oecologia 125:208–217.

Martin, L.R., and B.J. Gallaway. 1994. The effects of the Endicott Development Project on the

Boulder Patch, an Arctic kelp community in Stefansson Sound, Alaska. Arctic 47:54–64.

Minerals Management Service [MMS]. 1996. Beaufort Sea planning area oil and gas lease sale 144.

Final Environmental Impact Statement. MMS OCS EIS/EA MMS 96-0012. U.S. Dept. of

Interior, MMS, Alaska Outer Continental Shelf Region, Anchorage.

Minerals Management Service [MMS]. 1998. Arctic Kelp Workshop Proceedings, Anchorage,

Alaska. T.D. Newbury [ed.], OCS Study MMS 98-0038. U.S. Dept. of Interior, MMS, Alaska

Outer Continental Shelf Region, Anchorage.

Morton, B. 1999. Competitive grazers and the predatory whelk Lepsiella flindersi (Gastropoda:

Muricidae) structure a mussel bed (Xenostrobus pulex) on a southwest Australian shore. J.

Mollus. Stud. 65:435–452.

Ojeda, F.P., and A.A. Muñoz. 1999. Feeding selectivity of the herbivorous fish Scartichthys viridis:

effects on macroalgal community structure in a temperate rocky intertidal coastal zone. Mar.

Ecol. Prog. Ser. 184:219–229.

Wilson, H.M. 1979. Beaufort Sea high bids top $1 billion. Oil Gas J. 77:26–29.

Wilson, W.G., C.W. Osenberg, R.J. Schmitt and R.M. Nisbet. 1999. Complementary foraging

behaviors allow coexistence of two consumers. Ecology 80:2358–2372.

Worm, B., and A.R. Chapman. 1998. Relative effects of elevated gazing pressure and competition

from a red algal turf on two post-settlement stages of Fucus evanescens C. Ag. J. Exp. Mar. Biol.

Ecol. 220:247–268.
67

Water and Ice Dynamics in Cook Inlet, Alaska

Mark A. Johnson <johnson@ims.uaf.edu>

Stephen R. Okkonen <okkonen@alaska.net>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775–7220

Andrey Y. Proshutinsky <aproshutinsky@whoi.edu>

Physical Oceanography Department, MS #29

Woods Hole Oceanographic Institution

Woods Hole, MA 02543

Task Order 85308

Abstract

To better understand the circulation and dynamics of the water and ice in Cook Inlet, we use a high

resolution numerical model forced by tides that is being validated using satellite-tracked drifting

buoys and synthetic aperature radar (SAR) satellite imagery. The observational program using

drogued, drifting buoys focuses on mapping the tide rips to understand their temporal and spatial

variability. With more than 25 buoys now deployed, the complete position data set shows highest

energy in the historical tide rip locations. We are now investigating the temporal variability of the

rips. In the winter, satellite imagery allows us to track ice motion to further evaluate the rip locations.

Data Collection, Processing and Analyses

During the reporting period (October 2003–September 2004) we worked on data collection and tidal

modeling. Data collection included a search for additional and more precise bathymetry information

and particularly information about flooding/wetting areas or “mud flats”, which in the existing digital

bathymetry data archives are represented by a 0 m depth. This was not a trivial task because, as we

now understand, the requested information does not exist in a digital form and we have digitized

satellite images from Cold Regions Research and Engineering Laboratory report 76-25 [CRREL

1976] in order to improve our model bathymetry.

We have also collected river runoff data (major and small river locations, river discharge and its

seasonal variability). This information has been used to simulated circulation of Cook Inlet driven

by river runoff.

We are still working on the collection of wind fields and investigation of Cook Inlet wind-driven

circulation and its seasonal variability. Our tidal modeling experiments and comparison of simulated

buoy trajectories with observed trajectories allow us to conclude that wind-driven motion significantly

influences dynamics of Cook Inlet waters and can dominate when the wind is strong enough (Figure 1).
68

Figure 1. Trajectories of buoy 39907 simulated under various influences. Top left: M2 tidal currents

only, top right: M2 tides and observed wind, bottom left: M2+S2+K1+O1 tides and

observed wind, and bottom right: wind only for period 18–31 December 2003.

A substantial amount of time was dedicated to the collection of data for validation of our simulated

tidal sea level and current constituents. Approximately 100 moorings deployed by the National

Oceanic and Atmospheric Administration during a 1973–1975 circulatory survey of Cook Inlet

were analyzed, digitized and used for model validation and calibration purposes.

Drifting buoy data were transferred in model coordinates in order to be compared with model results

and used for model validation and calibration.
69

Modeling and Model Validation

We used our 2-D tidal model of Cook Inlet with a spatial resolution of 1 km to simulate eight major

tidal waves in this region (five semidiurnal and three diurnal—these are waves for which we obtained

boundary conditions from satellite-based archives of tidal constituents for the Gulf of Alaska and

northern Pacific Ocean). Model results presenting tidal elevations and phases of four major waves are

in Figures 2, 3, 4 and 5. Figures 6, 7, 8, 9, 10, 11, 12 and 13 show errors in tidal current (differences

between observed and simulated data). In general, simulated current velocities are larger than the

observed, but the direction of predicted currents coincides very well with observations. A similar

conclusion could be drawn about observed and simulated constituents for sea level elevations (see

Tables 1 and 2 where model validation results [observed/simulated] for semidiurnal and diurnal tidal

sea level elevation constituents are shown.) Simulated tidal elevations are larger than those observed

(except Anchorage), but the phase of wave propagation is in better agreement with observations.

Table 1. Observed/simulated amplitude and phase for semidiurnal constituents M2, S2, N2 and K2.

M2 Tidal Wave S2 Tidal Wave N2 Tidal Wave K2 Tidal Wave

Station Amplitude

cm

Phase

degree

Amplitude

cm

Phase

degree

Amplitude

cm

Phase

degree

Amplitude

cm

Phase

degree

Nikiski 251/292 030/039 087/105 062/072 049/050 359/017 025/017 054/076

Seldovia 223/265 324/339 082/088 359/007 047/042 297/304 023/019 353/349

Anchorage 353/287 108/109 100/148 150/150 060/098 082/085 027/048 142/151

Table 2. Observed/simulated amplitude and phase for diurnal constituents.

O1 Tidal Wave K1 Tidal Wave Q1 Tidal Wave

Station Amplitude

cm

Phase

degree

Amplitude

cm

Phase

degree

Amplitude

cm

Phase

degree

Nikiski 069/089 307/291 039/039 291/255 006/009 290/249

Seldovia 056/065 279/270 034/028 263/246 006/007 258/242

Anchorage 069/112 341/299 039/047 322/268 006/010 331/257

In order to simulate 3-D tidal currents driven by tides, river runoff, and winds we are going to employ

a 3-D finite volume model developed by Changsheng Chen from the University of Massachusetts

Dartmouth [2003]. A new model grid is shown in Figure 14. This grid has a resolution of

approximately 13 km along the southern and eastern boundaries of the model domain and

approximately 162 m in the shallow coastal regions. We expect that this model will allow us to

increase the accuracy of our simulations. We expect the first results by January 2005 and they will

be presented in the 2005 quarterly report.

Figure 1 shows the results of simulation of drifting buoy 32097 motion under the influence of

different forcing (tidal currents, winds, and a combination of tidal currents and winds). One sees

that in many cases wind forcing leads to a significant change in buoy motion. Wind and tidal forcing

results in numerous loops in buoy trajectories.
70

Figure 2. Left: Computed amplitude (in centimeters, thin lines) and phase (in degrees, thick lines) of

surface elevation for the semidiurnal constituent M2. Right: Computed ellipses of the M2

tidal wave.

Figure 3. Left: Computed amplitude (in centimeters, thin lines) and phase (in degrees, thick lines) of

surface elevation for the semidiurnal constituent S2. Right: Computed ellipses of the S2

tidal wave.
71

Figure 4. Left: Computed amplitude (in centimeters, thin lines) and phase (in degrees, thick lines) of

surface elevation for the semidiurnal constituent K1. Right: Computed ellipses of the K1

tidal wave.

Figure 5. Computed amplitude (in centimeters, thin line) and phase (in degrees, thick line) of surface

elevation for the diurnal constituent O1.
72

Figure 6. Left: Correlation between observed and computed M2 tidal velocities representing major

axis of tidal ellipse (cm s–1). Right: Correlation between observed and computed directions

(degrees) of major axis of M2 tidal ellipses.

Figure 7. Left: Difference (cm s–1) between observed and simulated M2 tidal velocities representing

major axis of tidal ellipses. Right: Difference (degrees) between observed and simulated

directions of major M2 tidal ellipses.
73

Figure 8. Left: Correlation between observed and computed S2 tidal velocities representing major

axis of tidal ellipse (cm s–1). Right: Correlation between observed and computed directions

(degrees) of major axis of S2 tidal ellipses.

Figure 9. Left: Difference (cm s–1) between observed and simulated S2 tidal velocities representing

major axis of tidal ellipses. Right: Difference (degrees) between observed and simulated

directions of major S2 tidal ellipses.
74

Figure 10. Left: Correlation between observed and computed K1 tidal velocities representing major

axis of tidal ellipse (cm s–1). Right: Correlation between observed and computed

directions (degrees) of major axis of K1 tidal ellipses.

Figure 11. Left: Difference (cm s–1) between observed and simulated K1 tidal velocities representing

major axis of tidal ellipses. Right: Difference (degrees) between observed and simulated

directions of major K1 tidal ellipses.
75

Figure 12. Left: Correlation between observed and computed O1 tidal velocities representing major

axis of tidal ellipse (cm s–1). Right: Correlation between observed and computed

directions (degrees) of major axis of )1 tidal ellipses.

Figure 13. Left: Difference (cm s–1) between observed and simulated O1 tidal velocities representing

major axis of tidal ellipses. Right: Difference (degrees) between observed and simulated

directions of major O1 tidal ellipses.
76

Figure 14. 3-D model grid covering Cook Inlet region with a spatial

resolution from 13 km along open boundaries and up to

162 m in the shallow coastal regions.

Buoys

More than 20 drifting buoys have been deployed in Cook Inlet by CISPRI (Cook Inlet Spill

Prevention and Response, Inc.) and other personnel since this project began. Position and time data

are relayed to the Argos satellite and emailed to Mark Johnson on a daily basis. Data are converted

to decimal data and velocities are computed using centered differences. Despite checksum error

detection of data relayed via Argos, some still show position jumps that do not appear real. For

obvious position changes outside of Cook Inlet and vicinity, those data are ignored. We are still

looking at smaller data jumps to determine the cause. We speculate that such small jumps may be

surprisingly large Cook Inlet velocities, or buoys that were snagged by local fisherman. The data

discussed in this report include only those without any data jumps that have been fully quality

checked for accuracy and trajectories that are visually “reasonable”.

Most buoys were deployed in the area south of the Forelands and northeast of Kalgin Island. All

buoys in year one were equipped with a drogue tethered to follow the water at depths between 5 and

10 m. In year two, the drogue depth was made shallower to track water at depths between 3 and 5 m.

No obvious differences between trajectories for different drogue depths have been noted. Within

Cook Inlet, the area covered by buoy trajectories is excellent (Figure 15).
77

Figure 15. All buoy trajectories to date in lower Cook

Inlet. First and last day of trajectory shown

by the blue (start) and red (end) “plus” signs.

To date, only a single buoy failed completely upon deployment, and it was replaced at no cost to this

project. All other buoys operated until grounding along the shore of Cook Inlet, or exiting to Shelikof

Strait and into the northern Gulf of Alaska. Several grounded buoys have been recovered and

redeployed by CISPRI and other personnel.

The “clean” velocity data were converted to kinetic energy (KE) and are characterized by a histogram

(Figure 16). All data were then gridded into a lat–lon (latitude–longitude) grid and contours of the KE

were made (Figures 17 and 18).

The highest KE values are east of Kalgin Island, along the middle rip. They appear to align along the

steep bathymetric slope.
78

Figure 16. Histogram of the buoy KE. The mean falls at the heavy red line, and

the thinner red lines mark standard deviation increments above the

mean. The values above 100 cm s–1 are contoured in Figures 17 and 18.

Figure 17. Kinetic energy contours. Only data valued above

100 cm s–1 have been contoured. The high values

of KE align with the bathymetry.
79

Figure 18. Kinetic energy as in Figure 16 showing

close-up around Kalgin Island.

Satellite Imagery for Front Identification

Fronts are typically associated with convergence zones. In Cook Inlet, drifting sea ice tends to collect

along tide rip fronts, thereby providing strong visual signatures for frontal locations. Radar

backscatter (brightness) from sea ice is typically larger than from open water. As a consequence, the

ice edge (frontal) location exhibits a relatively large spatial gradient in radar backscatter. Spatial

brightness gradients are computed from SAR (synthetic aperature radar) imagery and the locations

of the largest gradients (assumed to represent frontal locations) are written to file.

Preliminary Results

Figure 19 shows frontal locations identified from nine SAR images acquired in February 2002,

December 2003, January 2004, and February 2004. The color coding corresponds to the difference

between the image acquisition time and the time of high tide at the mouth of the Kenai River. The

greatest number of frontal features occurs in a zone extending southwestward from near the West

Foreland to along and beyond the eastern shore of Kalgin Island. This zone roughly corresponds to

the location of what is locally known as the West Rip. Another somewhat less prominent assemblage

of frontal features begins near the southwest end of Kalgin Island and extends southwestward along

the western side of Cook Inlet. The area between these two frontal zones south of Kalgin Island is

relatively shallow. The Middle Rip, which lies midway between Kalgin Island and the east side of

Cook Inlet, is not well-defined in the plot.
80

Figure 19. Front locations from SAR Imagery. Note

differences with the locations of high KE

from Figures 16 and 17.

Upcoming Work

In 2005, model results will be validated using buoy data. Another task for 2005 research is the

seasonal variability of Cook Inlet dynamics under the influence of all possible external forcing

(wind, river runoff, tides, and residual tidal circulation). Additional SAR images will be acquired

and analyzed, statistical analyses of frontal locations will be undertaken, and SAR results will be

integrated with buoy trajectory data.

References

Chen, C., H. Liu and R.C. Beardsley. 2003. An unstructured grid, finite-volume, three-dimensional,

primitive equations ocean model: Application to coastal ocean and estuaries. J. Atmos. Ocean.

Tech. 20(1):159–186.

CRREL (Cold Regions Research and Engineering Laboratory). 1976. Baseline data on the

oceanography of Cook Inlet, Alaska, 1976. Report 76-25 CRREL, Hanover, New Hampshire,
81

Foraging Ecology of Common Ravens (Corvus corax)

on Alaska’s Coastal Plain

Abby N. Powell <ffanp@uaf.edu>

Stacia A. Backensto <ftsab@uaf.edu>

Institute of Arctic Biology

University of Alaska Fairbanks

Fairbanks, AK 99775–7000

Task Order 85294

Abstract

Populations of common ravens (Corvus corax) on the North Slope of Alaska appear to be increasing

where anthropogenic resources are available. The oil fields provide abundant anthropogenic

resources in terms of infrastructure and food sources. Raven numbers are high in the oil fields

compared to other areas of human habitation on the North Slope. Ravens use the infrastructure for

nesting and foraging on human food. To assess the potential impact ravens may have as predators of

tundra-nesting birds we captured 10 breeding adult ravens, attached VHF and satellite transmitters,

and tracked their foraging movements during the breeding season. We collected pellet samples and

prey remains from nest areas, as well as conducting nest observations to evaluate food items brought

to the nest. We marked 28 fledglings to determine timing of dispersal and juvenile survival. To

 further investigate seasonal movements, dispersal, and anthropogenic resource use we engaged the

community of oil field personnel in an observation program targeting marked birds. Our preliminary

findings suggest that breeding adults maintain 1–2-km territories around facilities until late in the

chick stage and gradually increase until fledge. Adults shift use of food sources based on availability

throughout the breeding season. Juveniles remain with adults and siblings for a period of >4 weeks

after fledge. We emphasize the preliminary nature of the results and further analyses will be

necessary, as well as additional data collection for breeding adults and juveniles in the coming

years, to fully understand the relationship between ravens and human activity on the North Slope.

Introduction

Common raven (Corvus corax) populations have increased in many parts of their geographic range

[Boarman and Heinrich 1999]. In some areas of the U.S. it is common for high numbers of ravens to

be found in areas where anthropogenic resources (structures and food) are abundant. These subsidies

are often localized and spatial patterns of hyper-predation and spill-over predation by ravens on

local prey species assemblages can occur in these areas [Kristan and Boarman 2003]. In addition,

anthropogenic subsidies influence raven demography [Restani et al. 2001; Roth et al. 2004; Webb et

al. 2004]. In some areas outside of Alaska, breeding ravens establish core areas of space use around

their nest sites near these food subsidies [Roth et al. 2004]. Juvenile survival is also enhanced by nest

proximity to anthropogenic resources [Webb et al. 2004].
82

On the North Slope of Alaska where human activities are concentrated, such as the oil fields, raven

numbers have increased over the last 30 years [National Audubon Society; Day 1998]. The role of

the common raven as a scavenger and predator on the North Slope is not well understood. There

is a great deal of speculation that where ravens occur on the slope they exert significant predation

pressure on tundra-nesting birds. In the case of the oil fields, where it appears that ravens occur in

higher numbers and densities than elsewhere, it is important and necessary to evaluate how they use

the available anthropogenic resources in areas where human activity is high (e.g., Kuparuk and

Prudhoe Bay oil fields), moderate (e.g., villages of Barrow and Nuiqsut) and low (National Petroleum

Reserve–Alaska [NPR-A] Colville River Special Area).

In 2004 we established a marked population of ravens in the Kuparuk and Prudhoe Bay oil fields

to investigate how anthropogenic resources are influencing raven demography and movements. We

engaged the oil field community of personnel to participate in sharing information about marked

ravens during the summer of 2004. This is the first of several years of field work for this study.

Methods

Study area

The oil fields of the North Slope of Alaska are flanked by two major rivers: the Colville River to

the west and the Sagavanirktok to the east, and are characterized by extensive wetlands and tundra.

Temperatures range between –90°C and 25°C, with an annual average precipitation of 13–18 cm.

The ground remains frozen and snow covered for 8–9 months each year. Our work occurred

throughout the two largest producing oil fields on the North Slope: Kuparuk (104,514 ha) operated

by ConocoPhillips Alaska, Inc. and Prudhoe Bay (99,103 ha) operated by BP Exploration (Alaska)

Inc. We worked across the entire field of Kuparuk and in both the Eastern and Western Operating

Areas of Prudhoe Bay. Infrastructure consists primarily of gravel roads, pads, pipelines, production

facilities, and camps where personnel are housed and fed.

Raven foraging movements

Adult ravens were captured near their nests primarily during incubation. One member from each

breeding pair was captured. We trapped 10 adult breeding ravens from 21 April to 1 June 2004 using

remote control bownets, drop-in traps, and leg-hold traps (under UAF Institutional Animal Care and

Use Committee Protocol Number 02-59). Leg-hold traps were our most successful method, catching

8 of the 10 ravens. Nine adults were fit with 22-g, 1140 VHF transmitters (www.atstrack.com) and

1 adult was fit with a 24-g, bird-borne satellite transmitter (www.northstarts.com). Transmitters

were attached with a permanent, Teflon ribbon backpack style harness and an aluminum j-clip

fastener (pers. comm., Mark Pavelka, U.S. Fish and Wildlife Service [USFWS], Carlsbad, CA).

After transmitter attachment a colored vinyl, patagial-wing tag with 2 alpha codes was fixed to the

patagium area of the wing using a hog ear tag fastener (pers. comm., Mark Pavelka). All adults

received a USFWS band on the left leg and a series of morphological measurements was collected

(wing chord, culmen, tarsus length, tarsus width, bill depth). In some cases not all morphological

measurements were obtained, depending on the length of handling time and apparent signs of stress.

Blood samples were taken at the end of the handling period if the individual appeared to be in stable

condition. Three to five drops of blood were drawn from the brachial vein, placed in buffer solution,

and stored at 25°C. Birds were released at the capture site and monitored twice within 12 h of capture

to ensure that they would return to nests and normal flight behavior.
83

We began radio tracking these individuals from 3 June to 17 July 2004. Tracking sessions were

conducted between 0600 and 2000 depending on the time each transmitter was on. We attempted

to ensure that each bird had equal coverage during this time period. Tracking sessions consisted of

locating the individual’s signal first and then visually locating the bird. Once the bird was located

we attempted to observe it without influencing its behavior for a period of 30 min, and recorded

behavior during this interval. Behavioral observations were standardized for all observers and time

spent displaying a particular behavior was noted for the start and end of each behavior category. We

recorded GPS locations for foraging behaviors when possible, otherwise we estimated location with

distance and bearing from a structure or GPS point on a road when necessary. Currently, locations

are being entered into a geographic information system (ArcView 3.3) and will be analyzed with the

Animal Movement analysis extension [Hooge and Eichenlaub 2000].

Diet composition

Pellets were collected from a 10-m radius around all nest sites (n > 60) and heavily used perch areas

(n > 30) from 1 June to 17 July. Pellets were collected only after the area around the nest had been

cleared of old pellets. New prey remains were also collected during this time at the nest area (n > 40).

Nest observations were conducted for a period of 2 h in the morning and afternoon at a sub-sample

of nests (n = 8) to identify what food items were brought back to the nest.

Productivity and nest success

From 21 April to 10 June we surveyed for raven nests using a combination of local knowledge and

road surveys. The local knowledge assessment involved attending safety meetings (all supervisory

field production personnel attend these meetings) for all of the production facilities at Kuparuk and

most facilities at Prudhoe Bay. At these meetings production staff informed us where ravens had

nested in the past, where there were active nests, and where areas of raven activity were. We

subsequently investigated these areas to locate/confirm nest sites. Additionally we surveyed by

road all of Kuparuk and Prudhoe Bay’s Eastern and Western Operating Areas.

We described each nest’s characteristics, such as height above ground, aspect, materials and substrate,

as well as building features within the immediate area of the nest we thought might be important for

site selection. We checked for the presence of eggs or chicks using a mirror and extension pole. Nests

were then monitored every 5–7 d throughout the nest period until chicks fledged or nests failed. Not

all nests could be accessed via mirror and pole. In these cases we assumed birds were on eggs until

we observed chick-feeding behavior. In some situations facility personnel provided information as

to when nest building and/or incubation began.

Juvenile survival/dispersal

We captured fledglings within 1–3 d of leaving the nest. Each individual chick received a USFWS

band on the left leg and a white patagial tag with a 2-letter alpha code—on the left wing indicating

a Prudhoe Bay origin and on the right wing for Kuparuk origin. Morphological measurements (same

as the adults with the addition of the gape) and blood samples were also taken from these individuals.

Fledglings were released near their nest and monitored twice within 12 h. Locations of juveniles were

recorded during scheduled tracking sessions, and opportunistically while in the field from fledge date

through 18 July. From 13 to 16 August, juvenile locations were recorded once again to determine if

departure from the natal territory and adults had occurred. One additional juvenile dispersal

assessment will occur at the end of September 2004.
84

Local knowledge investigation

Initial interaction with facility/production personnel occurred at the safety meetings (20 April–

20 May). During these meetings Stacia Backensto gave a short informal presentation on the research

project and solicited information about nesting sites. Shortly thereafter posters were distributed to all

facilities summarizing the marking program and requesting sightings of marked birds from personnel

in the field. Throughout the field season Stacia Backensto identified several key informants for future

interviews focused on raven history, behavior, and perceptions. Most of the informants have been

approached and expressed interest in participation.

Results

Raven movements

At Kuparuk, we tracked 3 (2 female, 1 male) individuals regularly, and at Prudhoe Bay we tracked

5 (2 female, 3 male) individuals regularly, using VHF telemetry in both locations (Table 1). Female

“OE” was and continues to be tracked with a satellite transmitter. One individual, “OX”, was tracked

irregularly due to his lack of a breeding territory. Breeding ravens in the oil fields tended to stay close

to the nest during incubation and brood rearing. Most of the locations for our VHF birds fall within

500 m–2 km of the nest site. Toward the end of brood rearing and during fledging this distance

gradually increased. When fledglings were fully flight capable we located families >2 km from the

nest site. Though the family groups are highly mobile at this time we found them returning to roost at

the nest site facility or an adjacent facility. We will continue to analyze movement data in more detail

over the next several months.

Table 1. Number of sites for 3 types of location input for each raven tracked

at Kuparuk and Prudhoe Bay, 3 June–17 July 2004. GPS indicates

latitude/longitude recordings; Estimated locations correspond to visual

sightings with an estimated position from a known point; and Personnel

Sightings refer to e-mail, phone, or direct notification of sightings.

Individual P-tag Sex GPS Estimated Personnel

Sightings

AZ Prudhoe F 21 23 > 5

CZ Prudhoe M 2 28 > 2

EZ Prudhoe M 11 27 > 2

HZ Prudhoe F 5 26 > 7

JZ Prudhoe M 17 11 > 5

OZ Kuparuk F 9 28 > 5

JA Kuparuk M 17 20 > 4

EA Kuparuk F 13 21 > 4

OE Kuparuk F > 100 10 > 10

OX Kuparuk ? 2 10 > 2
85

Foraging observations

Anthropogenic food sources appear to be important to ravens when the tundra is snow covered in late

spring/early summer. The birds were active scavengers at sites where human activity was high when

obtaining this type of resource was likely. They also appeared to rely on cached food during this time.

We found caches buried on the ground near nest sites and on the facility structures themselves. When

the snow melted and tundra was exposed, before migrants returned and initiated nesting, we observed

ravens hunting brown lemmings (Lemmus trimuncronatus) and collared lemmings (Discrostonyx

groenlandicus). Ravens continued to hunt lemmings after geese, waterfowl, and shorebirds initiated

nests in June; later we observed ravens carrying eggs. By the time we concluded our tracking season

we observed ravens with various species of tundra-nesting bird chicks as well. Analyses of foraging

observations and food items will continue during the coming months.

Diet contents

A thorough pellet analysis has not been completed at this time. Pellets and prey remains collected at

the nest included egg shells, lemmings, bird remains, and anthropogenic food remains.

Productivity and nest success

Ravens in the oil fields layed 3–6 eggs in the early part of April and fledging occurred in mid- to late

June (Table 2). Nests were attended by both parents; females appeared to do most of the incubating.

Both parents were involved in brood rearing and they actively and aggressively defended their young

when nests were approached. Nest characteristics have not been summarized at this time, but it

appeared that most nests were 10 m above ground with 40–100% cover, and placed on human-made

structures to minimize the effects of prevailing winds. Nests consisted primarily of industrial

materials (welding rods, survey stakes, cable, electrical wire, pipe insulation fasteners, and various

plastics).

Juvenile dispersal

Departure from the natal territory for juveniles began in early August (Table 3). Family groups

maintained cohesion >4 weeks after fledge dates. In mid-August not all family groups had dissolved

8 weeks post-fledge. Juveniles learned to forage during this time. We observed family foraging arrays

>2 km from nest sites during the end of our tracking period. In mid-August juveniles from 4 different

nests at Prudhoe Bay were observed at the North Slope Borough landfill without parents or siblings.

This suggests departure from natal territory may have occurred. Additionally, we have recently

received sighting reports of juveniles having moved >40 km from natal territories.
86

Table 2. Final status of all nests found and actively monitored at Kuparuk and Prudhoe Bay

in 2004. Nests are referenced by facility where they were located.

Date Found End Date Chicks Young

Fledged

Kuparuk

CPF1 21 Apr 04 19 Jun 04 4 4

CPF2 20 Apr 04 14 Jun 04 6 5

CPF3 20 Apr 04 21 May 04 0 0

2T 21 Apr 04 19 Jun 04 2 2

2P 1 May 04 13 Jul 04 2 2

Tarn Bridge 24 Apr 04 31 May04 failed 0

KCS 25 Apr 04 30 Apr 04 destroyed 0

Prudhoe Bay

FS1 10 May 04 11 Jun 04 2 2

FS2 24 Apr 04 7 Jun 04 4 4

FS3 8 May 04 1 Jul 04 2 2

GC1 10 May 04 15 Jun 04 5 5

LPC 19 May 04 24 Jun 04 3 3

L5 20 May 04 22 Jun 04 4 4

M PAD 28 May 04 2 Jul 04 4 4

Deadhorse 1 Jun 04 21 Jun 04 4 4

DS16 8 May 04 21 Jun 04 5 5

Rig at North Star Logistics 10 Jun 04 14 Jun 04 6 6

Table 3. Number of tagged fledglings from monitored nests at Kuparuk

and Prudhoe Bay for 2004. The last column represents how many

young were found on the last tracking session, 16 August 2004.

No. Fledged No. Tagged No. Resighted

Kuparuk

CPF1 4 0 4

CPF2 5 4 3

2T 2 2 2

2P 2 2 2

Prudhoe Bay

FS1 2 2 2

FS2 4 4 1

FS3 2 1 1

GC1 5 4 0

LPC 3 1 0

L5 4 3 0

M PAD 4 2 1

Deadhorse 4 1 1

DS16 5 2 1

Rig at North Star Logistics 6 6 1
87

Discussion

Demographic parameters and behaviors of ravens are necessary to understand their role as subsidized

predators and to explain the apparent population increase in the arctic oil fields of Alaska. Our

preliminary data reveal that the ravens are as opportunistic and highly adaptable in their environment

as they are elsewhere in their geographic range. Their behavioral plasticity allows them to exploit

the unique features of this industrial environment year round. Facility structures provide nesting

substrates, heat, shelter, food caches, and the possibility of anthropogenic food subsidies.

Ravens established breeding territories approximately 1–2 km in size and actively defended these

territories against intruding ravens [Boarman and Heinrich 1999; Roth et al. 2004]. Many of these

territories include one major production facility and several smaller ones. In addition to acquiring

anthropogenic food subsidies, ravens actively hunted small mammals, and removed eggs and chicks

from waterfowl, geese, and shorebird nests. At this time we do not understand the degree to which

they select one over the other, but we have observed them with all of these prey items. We suspect

that within their breeding territories predation pressure exerted by ravens on other species is higher

than outside of these territorial boundaries during egg laying, incubation, and the first 3–4 weeks

of brood rearing. After this time, when chicks are fully feathered, foraging movements of adults

increased in duration and distance. Shortly after fledging, the territories expanded and family foraging

activities occured over a much wider area, >2 km. In 2004 we collected limited data on dispersal past

two weeks of fledging, but believe this time period is important for understanding survival,

recruitment and emigration from the local population.

Our field investigations, though valuable, can bridge only some of our knowledge gaps in how ravens

began and continue to exploit the infrastructure of the oil fields. A wealth of information about their

history and behavior resides in the community of people spending 50% of their lives living in and

working at these facilities. A considerable body of literature is growing on how indigenous and rural

people understand and relate to their landscape as well as how they identify and adapt to change in

their environment [Krupnik and Jolly 2002; Fienup-Riordan 1990]. Cultural geographers discuss this

type of understanding as “sense of place”. At Kuparuk and Prudhoe Bay many workers identify with

the ecological surroundings and portray themselves to a certain extent as stewards of this area. These

associations and meanings have often times been expressed through their feelings and thoughts about

ravens. While working closely with many of these individuals during the 2004 field season we found

they were keen observers and that a body of local knowledge of ravens exists. Though not typically

identified as stakeholders in issues pertaining to resource management on the North Slope, oil field

workers’ concerns about wildlife management and particularly raven management may illustrate

how sense of place or land ethic applies in a large industrial setting. This presents a truly unique

opportunity to further engage in an exchange of information with this group of people and document

the relationship between industry personnel and ravens.

Future Field Plans

Breeding adult foraging movements

The 2005 field season will continue with tracking VHF adults from 2004 and trapping new breeding

adults in the oil fields for satellite telemetry. Tracking methods for VHF birds will be slightly

modified from 2004 based on preliminary data analysis to minimize potential tracking biases inherent

in telemetry work. Additionally and most importantly we intend to cover a longer tracking period to

further investigate resource use changes based on availability and season.
88

Analysis of 2004 telemetry data will include: home range estimation, activity pattern definition

(foraging, caching, perching, etc.), prey characterization and quantification during foraging activities,

and distance to multiple spatial and temporal levels of anthropogenic food resources. Additionally we

hope to collaborate with the Wildlife Conservation Society where possible on predation patterns of

tundra-nesting birds.

Breeding adult seasonal movements

In 2005 we plan to work with the Bureau of Land Management in NPR-A to capture adult breeding

ravens in areas further away from concentrated human development and to fit them with satellite

transmitters. The comparison with satellite data for breeding adults in the oil fields will enhance our

understanding of how the use of anthropogenic resources varies between ravens that are closer to

subsidies and ravens that are not.

Diet contents

In 2005 we will continue to collect pellets and prey remains from nest sites at Kuparuk and Prudhoe

Bay, as well as within the NPR-A Colville River Special Area and the villages of Nuiqsut and

Barrow.

Productivity and nest success

We will continue to locate and monitor all nests at Kuparuk and Prudhoe Bay in 2005.

Juvenile dispersal

We will continue to attempt to tag all juveniles from nest sites at Kuparuk and Prudhoe Bay in 2005.

In addition, we will fit juveniles from a sub-sample of nests with VHF transmitters to monitor

dispersal activities and events.

Local knowledge

Pending permission from ConocoPhillips Alaska, Inc. and BP Exploration (Alaska) Inc., we will

conduct interviews with 10–15 willing oil field personnel on their knowledge of raven history,

behavior, and perception of raven management in 2004/2005. Additionally, focus group meetings

will be held at major facilities to document historical and current areas of raven use.

References

Boarman, W.I., and B. Heinrich. 1999. Common raven (Corvus corax), No. 476. In A. Poole and

F. Gill [eds.], The Birds of North America, Volume 12. The Academy of Natural Sciences,

Philadelphia and The American Ornithologists’ Union, Washington, D.C., 32 p.

Day, Robert H. 1998. Predator populations and predation intensity on tundra-nesting birds in

relation to human development. Unpub. rep. prepared for U.S. Fish and Wildlife Service,

Fairbanks by ABR, Inc., Fairbanks, 106 p.

Fienup-Riordan, A. 1990. Original ecologists?: The relationship between Yupik Eskimos and

animals, p. 167–191. In A. Fienup-Riordan [auth.], Eskimo Essays: Yupik Lives and How

We See Them. Rutgers University Press, New Brunswick, NJ.
89

Hooge, P.N., and B. Eichenlaub. 2000. Animal Movement extension to ArcView, Version 2.0.

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

Kristan, W.B., and W.I. Boarman. 2003. Spatial pattern of risk of common raven predation on desert

tortoises. Ecology 84(9):2432–2443.

Krupnik, I., and D. Jolly [eds.]. 2002. The Earth Is Faster Now: Indigenous Observations of Arctic

Environmental Change. Arctic Research Consortium of the United States, Fairbanks, 384 p.

National Audubon Society. www.audubon.org/bird/cbc/103rdsummary.html

Restani, M., J.M. Marzluff and R.E. Yates. 2001. Effects of anthropogenic food sources on

movements, survivorship, and sociality of common ravens in the Arctic. Condor 103(2):399–404.

Roth, J.E., J.P. Kelly, W.J. Sydeman and M.A. Colwell. 2004. Sex differences in space use of

breeding common ravens in western Marin County, California. Condor 106(3):529–539.

Webb, W.C., W.I. Boarman and J.T. Rotenberry. 2004. Common raven juvenile survival in a

human-augmented landscape. Condor 106(3):517–528.
90

High-Resolution Numerical Modeling of Near-Surface

Weather Conditions over Alaska’s Cook Inlet and

Shelikof Strait

Peter Q. Olsson <olsson@aeff.uaa.alaska.edu>

Alaska State Climate Center

University of Alaska Anchorage

2811 Merrill Field Drive

Anchorage, AK 99501

Task Order 73070

Abstract

Along the north Gulf of Alaska coast, terrain plays an important role in determining local weather.

The interaction of terrain with synoptic and mesoscale pressure gradients frequently produces

ageostrophic gap and channel winds, often called low-level jets (LLJ) in places like Cook Inlet

and Shelikof Strait. These winds may at times be quite strong, with gusts occasionally exceeding

50 m sec–1.

This three year project was successfully and productively conducted for the first year period.

Regional Atmospheric Modeling System (RAMS) parallel code was successfully migrated onto

a new high performance computer cluster which consists of 15 Dell Poweredge dual Xeon 3 Ghz

computers—15 nodes—with dual processors on each node. Domains with grid-spacing of 4 km,

16 km and 64 km were created, with domains being optimized through a series of sensitivity

experiments.

Initially we had intended on focusing the study on Cook Inlet, with Shelikof Strait being the focus

in later years. However, the acquisition of the computing cluster discussed above, under internal

support from the University of Alaska Anchorage (UAA) to the Alaska Experimental Forecast Facility

(AEFF), provided the additional computing power necessary to have the fine-mesh grid 3 cover both

Cook Inlet and Shelikof Strait from the beginning of the project onwards. Since December 2003, we

have been running the model on all three grids in real time as a forecast model, producing 36-hr

forecasts with output every hour. A climatology of low-level wind jets in Cook Inlet and Shelikof

Strait was also composed for the 2003 to 2004 winter.

Overview

The three year project, High-Resolution Numerical Modeling of Near-Surface Weather Conditions

over Alaska’s Cook Inlet and Shelikof Strait, has been successfully and productively conducted

for the first year period. Regional Atmospheric Modeling System (RAMS) parallel code has been

successfully migrated onto a new high performance computer cluster which consists of 15 Dell

Poweredge dual Xeon 3 Ghz computers—15 nodes—with dual processors on each node.
91

Domains with grid-spacing of 4 km, 16 km and 64 km (grids 1, 2, and 3 respectively; see Figure 1)

have been created, with domains being optimized through a series of sensitivity experiments. The

coarsest grid (grid 1) covers all of Alaska and vicinity and much of the surrounding oceans. This grid

is large enough to catch the synoptic weather patterns that force the smaller scale flows. The 16-km

domain is required to scale down the simulations from the synoptic-scale grid 1. The finest domain,

4-km grid 3, covers Cook Inlet and Shelikof Strait, which enables detailed simulation of mesoscale

phenomena such as low-level wind jets, orographic precipitation, mesoscale cyclones and fronts.

Initially we had intended on focusing the study on Cook Inlet, with Shelikof Strait being the focus

in later years. However, the acquisition of the computing cluster discussed above, under internal

support from the University of Alaska Anchorage (UAA) to the Alaska Experimental Forecast

Facility (AEFF), provided the additional computing power necessary to have the fine-mesh grid 3

cover both Cook Inlet and Shelikof Strait (Figure 1) from the beginning of the project onwards. This

required a realignment of the project to a certain extent to accommodate the larger data sets and extra

analysis of the enlarged domain. However, from the scientific viewpoint, this has been advantageous

and will allow for complete data sets for the full domain for the entire study period.

Since December 2003, we have been running the model on all three grids in real time as a forecast

model, producing 36-hr forecasts with output every hour. We also ran simulations on all three grids

retrospectively from 1 October until we started producing the routine forecasts in real time. This

makes available finest-mesh simulations from the beginning of the project onwards. All simulation

data sets are being archived at AEFF for future use and analysis by ourselves and other researchers.

Results from the high-resolution real-time simulations are presented in hourly-resolution graphics,

available to the public through the internet (aeff.uaa.alaska.edu/wx_brief.html) since December 2003.

A climatology of low-level wind jets in Cook Inlet and Shelikof Strait has been composed for the

2003 to 2004 winter. A manuscript about the low-level wind jet climatology over the study region

has been composed and accepted for presentation in the Seventh International Marine Environmental

Modeling Seminar (IMEMS 2004). The paper will hopefully be published in the international journal

Environmental Modelling and Software after the peer review process.

Topography of Cook Inlet and Shelikof Strait

Figure 2a is a 2-dimensional image showing the location of mountain ranges, channels and water

bodies of the Cook Inlet and Shelikof Strait region. Most of the mountain ranges are between 600

and 2000 m in height. Turnagain Arm, Tuxedni Bay, Iliamna Lake, Kaguyak and Puale Bay are

marked with text. These places have nearby gaps with elevations under 600 m, much lower than the

surrounding terrain. These gaps are the locations through which gap winds frequently blow. Cook

Inlet is bounded on the west by the massive Aleutian Range and on the east by the Chugach and

Kenai Ranges. Shelikof Strait has the Alaska Peninsula on the west and Kodiak Island on the east.

During the winter storm season, strong atmospheric pressure gradients occur across this region as

cyclones transit the north gulf. These pressure gradients are one of the main factors inducing strong

wind events in this region. Figure 2b is a 3-dimensional perspective of the topography of the areas of

interest. The perspective angle was chosen to emphasize the passes which produce major wind jets.

Besides the channels, the gaps at Iliamna Lake and the Puale Bay vicinity are clearly visible.
92

Figure 1. The RAMS domains for Cook Inlet and Shelikof Strait.

G1 has a grid spacing of 64 km, G2 16 km and G3 4 km.

Low-Level Wind Jets Regimes

Through the examination of the simulated surface winds, ten frequently appearing wind regimes are

recognized (Figure 3). These wind jets are classified into ten categories according to location and

orientation: Turnagain Arm (TGA), Tuxedni Bay (TNB), Cook Inlet up channel (CIu), Cook Inlet

down channel (CId), Iliamna (ILA), Iliamna reversed (ILAr), Kaguyak (KGY), Shelikof up channel

(SKFu), Shelikof down channel (SKFd) and the Puale Bay (PUB) jet. They are further divided into

four general groups according to their general wind direction: cross-channel westerly, easterly, and

up and down inlet flows. The westerly includes ILA, KGY, PUB and TNB; the easterly includes

ILAr and TGA; the down channel includes CId and SKFd; and the up channel includes CIu and

SKFu. (The details of the climatology of these wind jets can be found in the conference manuscript.)
93

Figure 2. Topography of Cook Inlet and Shelikof Strait.

Figure 3. Schematic of the low-level wind jets in Cook Inlet and Shelikof

Strait. The arrows indicate the locations and directions of the jets.

A jet may occupy the whole channel where the arrow indicates.

a b
94

Verification of RAMS Wind Forecasts in Cook Inlet and Shelikof Strait

with Point Values

As part of this project, we need to verify simulation results. The study region has a paucity of

observations, with the exception of the region around Anchorage and the northern Kenai Peninsula.

(Indeed, a major rationale for this project is to produce low-level wind fields to ameliorate this

situation). We have embarked on a program, largely focused on lower Cook Inlet and Shelikof Strait,

to systematically compare our simulations with observed values, mostly NDBC (National Data Buoy

Center) buoys and C-MAN (Coastal–Marine Automated Network) stations. We detail our approach

here.

When the station observations are used, two verification approaches can be employed: grid-toobservation-point (G-P) and observation-point-to-grid (P-G). While less satisfactory than the P-G

method, which requires a much greater density of observations than is available, G-P is the better

method for data-sparse regions such as Alaska. There are only a few C-MAN and buoy stations

in the Cook Inlet and Shelikof Strait region (see Figure 4) and several of these face instrumentsheltering issues for certain wind directions. Meteorological observations from these stations are

being collected and a bi-linear algorithm is being used for grid-to-observation interpolation.

Figure 4. C-MAN and buoy stations in Cook Inlet and Shelikof

Strait.
95

The statistical measures used to quantify model forecast errors are the bias (forecast-observation),

root-mean-square (rms) error, and error standard deviation. For interpretation of results, it is helpful

to recognize that the total model error includes contributions from both systematic and nonsystematic

sources. Systematic errors (model biases) are usually caused by a consistent misrepresentation of such

factors as orography, radiation, and convection. Nonsystematic errors are indicated by the error

standard deviation and represent the random error component caused by initial condition uncertainty

or inconsistent resolution of scales between the forecasts and observations. While it is possible to

partially correct for known systematic errors by subtracting the bias, the nonsystematic errors are

rather unpredictable in nature and may contribute to a degraded daily forecast product.

If  represents any of the parameters under consideration for a given time, then forecast error is

defined as = f – o, where the subscripts f and o denote forecast and observed quantities,

respectively. Given N valid pairs of forecasts and observations, the bias is computed as

 = 1

N



i

i=1

N

 (1)

the rms error is computed as

rmse = [mse]

1/2 = 1

N

 ( )i

2

i=1

N

 











	

	

1/2

(2)

the standard deviation (std) of the errors is computed as

 = 1

N



i ( ) 

2

i=1

N

 











	





1/2

(3)

The mean square error (mse) can be, at least theoretically, decomposed as follows:

mse = 

2 +2 (4)

Therefore, the total model error consists of contributions from model biases 

2 and random

variations in the forecast and/or observed data. Note that if the model bias or systematic error is small,

most of mse is due to random, nonsystematic type variability in the errors.

The surface wind bias, rms and std are calculated for each C-MAN and buoy station. The averages are

calculated over 5 days, 10 days, 30 days and 60 days. Both wind direction and speed show reasonably

good agreement with the observations. The longer the average period is, the better the result.

There is no doubt that grids 2 (16 km) and 3 (4 km) have more skill than grid 1 (64 km) in terms of

objective and subjective verification. Grid 3 (4 km) has advantages over grid 2 because it has higher

horizontal resolution and is able to resolve more details of the topography, which affects the weather.

However, the finer grid (grid 3) may not show skills over the coarser (grid 2) in objective P-G

verification, and the added value by the finer grid may not be evident from the P-G method. For an

example, verification over the C-MAN station in Augustine Island (AUGA2, see Figure 5) shows

that grid 2 is slightly better than grid 3. Unfortunately, AUGA2 is sheltered by the imposing upstream

terrain in many wind events, giving smaller wind-speed values than would be observed in unsheltered

areas, so we must use this station with some caution.
96

Figure 5. Comparison with the C-MAN station at Augustine Island for February and March 2004.
97

Subjectively, higher resolution (e.g., 4 km) appears to produce much more realistic structures when

compared with qualitative observations such as synthetic aperture radar (SAR) wind retrieval images.

We are investigating why the objective verification measures do not reflect this fact. Several issues

are confounding factors with this verification method: 1) timing and position errors are more heavily

penalized as resolution increases; and 2) as resolution increases, structures tighten and increase in

amplitude. As a result, most traditional verification scores will find greater error for higher resolution

simulations, even if the structures are more realistic. Timing errors (onset, duration and relaxation of

low-level jets) almost always occur, so this is an ever-present issue.

Other verification approaches are being examined, such as pattern recognition and observation-based

verification of phase and timing errors.

Case Simulations of Gap Wind in Lower Cook Inlet, Alaska

Another avenue of investigation for this project is the conducting of case studies to determine the

internal structure of low-level gap and channel wind jets. Gap winds are strong low-level winds

through a channel between two mountain ranges or a gap in a mountain barrier. Gap winds can be the

result of interaction of large-scale flows with underlying topography. They can also be induced by

thermal difference of the surfaces. Thermally induced gap winds, which are more moderate in speed,

are also observed. The synoptic and mesoscale thermal effects can combine to contribute to the

formation and enhancement of gap winds.

For a level channel, scale analysis shows that the momentum balances within gaps and their exit

regions are highly dependent on the length and width, and long gaps favor a nearly geostrophic balance

in the cross-gap direction, with the pressure gradient normal to the gap axis balancing the Coriolisforce

associated with the along-gap component of the winds. This geostrophic balance has been observed

in high-latitude gaps such as Shelikof Strait. With the pressure gradient along the gap, theoretical

analysis and observational studies, as well as results from hydraulic models and mesoscale models,

have shown a three-way ageostrophic balance between acceleration, the pressure gradient force, and

drag forces due to surface friction and entrainment at the top of the gap flow. Within a long gap, a point

is usually reached where there is an approximate balance between the pressure gradient force and drag.

In the case of several of the cross-channel gap winds, the bed of a gap may not be level. In the case of

presence of barriers in a gap, the flow in the gap may experience blocking and strength enhancement

after crossing barriers, for example the Iliamna or Kamishak gap flows. The effect of upstream

orography on offshore-directed winds depends on the characteristics of the incident flow. A

nondimensional similarity parameter, the internal Froude number Fr, is related to the tendency for

the flow to be constrained to horizontal planes.

If Fr = U

NH (where U is the incident speed, N is the static stability, and H is the height of the

obstacle) is small (Fr <1) as defined with respect to the height of the obstacle, the low-level flow is

largely blocked, except where it can flow around the obstacle or through gaps. When the cross-barrier

pressure gradient is substantial, strong low-level wind shears occur at and downstream of the lateral

boundaries of these obstacles. When Fr 1, the flow has more of a tendency to cross the barriers and

enhance in the lee of the topography.

An area of even more interest is the structure of the jet after it leaves the gap and emerges over

open water, where a new balance of forces occurs. After leaving the gap and reaching the open

water, the terrain-modulated flow accelerates due to the reduced surface drag over water and adjusts

to open water configuration and approaches quasi-geostrophic balance at roughly a Rossby radius
98

IR = NH

f off the coast, where N and H are as above, and f is the Coriolis parameter. (Note that f is

large in this high latitude setting). During conditions of flow characterized by a low Froude number

the steady-state response to terrain is a coastal ridge—the phenomenon known as damming. The topographically induced pressure fields produce along-ridge pressure gradients that can result in barrier jets.

We intend to conduct a Froude number analysis in several case studies as an index for the likelihood

of jet occurrence and intensity, as this is essentially a parameter diagnosable from model output.

As is the case in most realistic vs. idealized environments, the low-level wind jets in lower Cook Inlet

(LCI) are very complicated, with several confounding factors acting together. They can result from

composition of gap wind, cross barrier flow, and barrier jets as can be seen in a snapshot of a case

study (Figure 6) of an ILA jet on 13 December 2003. The gap between the Alaska Range and the

Alaska Peninsula is often a passageway of dense cold dry air from the Alaska continent to LCI. There

is a barrier of about 600 m at the gap end towards the sea. The elevation of the area west to the barrier

is less than 300 m. The blocking effect is clearly noticeable in the cross section of ILA (Figure 6b).

The wind maxima is close to the south side of the jet where a barrier (the Alaska Peninsula) extends.

The shift of the wind maxima is caused by the blocking of the barrier to the south side part of the jet.

Evidently a barrier jet takes place in this case in addition to the more generic gap flow.

Figure 6. A jet on 13 December 2003. a: two surface winds and mean sea level pressure; b: cross

section of the ILA corresponding to the line AA in a.

Conclusion

This project has progressed successfully for the first year. Graphics from the real-time high resolution

forecasts are being published over the internet and benefit the community. The results of this project

help and, in some ways, shape our understanding of the mesoscale weather phenomena over the Cook

Inlet and Shelikof region. The results are encouraging and a more sound and thorough understanding

of the severe weather situations in this region will be obtained through continuous study across the

duration of the project.

a

b
99

Trace Metals and Hydrocarbons in Sediments of

Beaufort Lagoon, Northeast Arctic Alaska

A. Sathy Naidu <ffsan@uaf.edu>

John J. Kelley <ffjjk@uaf.edu>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775-7220

Debasmita Misra <ffdm1@uaf.edu>

College of Engineering and Mines

University of Alaska Fairbanks

Fairbanks, AK 99775-5960

Task Order 74464

Abstract

There is a concern that anthropogenic contaminants discharged locally during petroleum-related

activities can accumulate in Beaufort Lagoon sediments. In response to this concern we analyzed

concentrations of 12 metals (Cu, Cr, Cd, Ni, V, Pb, Sn, Zn, As, Ba, Fe and Mn) in the mud fraction

and total Hg and hydrocarbons (saturated compounds such as normal and isoprenoid alkanes,

triterpanes, steranes, and polycyclic aromatic hydrocarbons) in gross sediments collected at 21

locations in the lagoon and in one natural oil seep.

This report presents the concentrations of sediment trace metals and hydrocarbons, andalso

discusses the sources of the hydrocarbons and provides a preliminary interpretation of the statistical

analysis on the trace metal data. The concentration levels of the metals and hydrocarbons are

generally similar to or below those reported for unpolluted nearshore regions. Comparison of

time-series metal data on sandy mud and mud collected in 1977 and 2003, respectively, shows a

significant decrease in V but an increase in Mn and Cu from 1977 to 2003. The differences are

likely due to disparities in the granulometry of the two sample sets. Correlation coefficient analysis

suggests that a large proportion of all the trace metals are adsorbed by clay or Fe oxyhydroxide.

The analysis also suggests that some of the metals (Zn, Cd and Sn) occur as metal–organic complexes

in the sediments. The hydrocarbon components in the sediments are biogenic with undetectable

petroleum inputs. There is no evidence of a contribution of petroleum hydrocarbons to the lagoon

sediments from oil seeps in the vicinity. The general composition of the hydrocarbon profiles is very

similar to those found in our previous studies in the sediments from Elson Lagoon and the nearshore

of the Colville Delta–Prudhoe Bay–Canning Delta region. We saw no impression of the natural oil

seep and anthropogenic activities on the sediment chemistry. The metal and hydrocarbon data will

serve as baselines, which will be critical for ecological risk management of the North Slope in context

of contaminant inputs, and to better understand the inorganic and organic geochemistry of arctic

sediments.
100

Introduction

The North Slope of arctic Alaska and the contiguous nearshore zone are oil- and gas-bearing regions

which have a high potential for commercial reserves. A number of onshore oil sites (Prudhoe, Alpine,

Kuparuk, Milne) and a few offshore units (Northstar, Endecott) are now producing, while some

offshore prospects (Liberty) are slated to be brought on line soon for drilling and production. As part

of the forthcoming oil and gas development, the U.S. Department of Interior’s Minerals Management

Service (MMS) has proposed several offshore units (186, 195 and 202) for lease sales. The

petroleum-related developmental activities (drilling operations laced with toxic metals and

hydrocarbons, marine and onshore traffic, housing) and associated urbanization of the Native villages

are bound to impact the nearshore environment, with a possible accumulation of anthropogenic

contaminants in sediments [NORTEC 1981; Snyder-Conn et al.1990]. These contaminants can be

transferred to benthic organisms and subsequently through the food chain to higher trophic levels.

Environmental contamination is of particular concern in the Arctic where marine organisms which are

lipid rich, with a relatively simple and short food chain and low biodiversity, are more vulnerable to

bioaccumulation of toxic metals and hydrocarbons. In response to these concerns and our long-term

interest in monitoring contaminants in the Alaskan Arctic nearshore we have investigated the timeseries changes in trace metals and hydrocarbons in nearshore sediments from urbanized and industrial

regions extending from Elson Lagoon adjacent to Pt. Barrow east to the Colville Delta–Prudhoe

Bay–Canning Delta region of the North Slope [Naidu et al. 2001, 2003a, b and references therein].

Other investigations of a similar nature are those of Sweeney [1984] and Trefry et al. [2003] on

trace metals and Steinhauer and Boehm [1992] on hydrocarbons. This report presents the progress

of investigations on a two-year project (2003–2005) which has the major objective to measure the

concentrations of a suite of trace metals and hydrocarbons in sediments of Beaufort Lagoon (Figure

1). This location is of particular interest for contaminant studies because it offers a range for

comparing sediment chemistries between site-specific sectors which have been exposed to 1) a longterm natural oil seep, 2) past activities relating to a DEW Line military station on the lagoon shore,

and 3) sites that presumably have remained relatively pristine. The results of this study will provide

baselines of trace metals and hydrocarbons for monitoring contaminants in the nearshore environment

off of the Arctic National Wildlife Refuge (ANWR), particularly off the refuge’s 1.5-million acre

coastal plain, which has potential petroleum reserves. The latter region is one of the most contentious

areas for oil drilling and production because of the variety of wildlife habitats there [Douglas et al.

2002] and possible impact on them.

Hypothesis and Objectives

We hypothesize that the compositions and concentrations of trace metals and hydrocarbons in

sediments of Beaufort Lagoon vary widely between the regions exposed to a long-term (prehistoric)

natural oil seep (enriched in weathered crude), recent anthropogenic activities (with a possible input

of refined petroleum products from activities at a former DEW Line site), and a pristine environment

(no input from the oil seep or refined petroleum).

The primary objective of this proposal is to distinguish the concentrations of 12 metals (V, Cr, Cu,

Ni, Zn, As, Cd, Pb, Sn, Ba, Fe and Mn) in the mud fraction (<63 m size), and total Hg [THg] and

hydrocarbons (polycyclic aromatics and saturated hydrocarbons such as normal and isoprenoid

alkanes, triterpanes and steranes) in gross sediments between regions of Beaufort Lagoon that are

exposed to long-term natural oil seep, anthropogenic activities with possible inputs of refined

petroleum and military-related products, and pristine conditions. The objective is also to establish

baselines on the above trace metals and hydrocarbons in the sediments of Beaufort Lagoon for

contaminant monitoring.
101

Materials and Methods

Samples

In August 2003, van Veen grab sediment samples were collected from 22 stations spread over three

arbitrarily defined location types in Beaufort Lagoon (Figure 1, Table 1). One suite of samples

(BL03-5, -12, -13, -14 and -15 [hereafter samples and stations are referred to by number only]) was

collected east of Angun Point adjacent to a known natural oil seep site (69.918°N and 142.395°W,

pers. comm. Jim Clough, 31 July 2003; refer also to USGS [1999]) and one sample (4B) off of a seep

site (OS) we discovered at the bank of a small stream located south of Nuvagapak Point (Figure 1,

Table 1). OS itself was also a site for sample collection for hydrocarbon comparisons. We had

intended to get closer to the shoreline at Angun Point to collect samples from that area, but it was not

possible because of extensive shoals in the region, which made it impossible to navigate. A third suite

of samples (4, 18, 19 and 20) was collected around Nuvagapak Point, a region that was impacted by

anthropogenic activities connected with an abandoned DEW Line site and associated landing strip.

A fourth suite of samples (1, 2, 3, 5B, 6B, 9, 9B, 10, 11A, 16 and 17) was obtained from areas east

and up current of Nuvagapak Point, which are presumed pristine. The coordinates for the sample

locations, fixed by GPS, are shown in Table 1. The surface oxidized 2–4-cm portion of each of the

grab samples was taken and split into three sets of subsamples using a Teflon spatula. Each of the

splits was transferred to three separate I-CAM glass jars, two of which were rinsed with acid and

deionized distilled water (for trace metal, grain size and organic carbon analyses). The third jar was

pre-baked and cap-lined with aluminum foil for hydrocarbon analysis. One set was sent to M.I.

Venkatesan, subcontractor at the University of California, Los Angeles, for hydrocarbon analysis. A

second set was sent to the subcontractor Frontier GeoSciences Inc. in Seattle for trace metal analysis.

The third set was retained by the principal investigators for analysis of the grain size, organic carbon

and nitrogen and their stable isotopes at the University of Alaska Fairbanks. All of these sediment

samples were stored frozen until analysis.

Laboratory analysis of samples

The methods for the analyses of trace metals and hydrocarbons on sediments were the same as those

adopted in our previous studies funded by CMI/MMS [Naidu et al. 2001, 2003b]. Brief descriptions

of the methods follow: We analyzed 12 metals (V, Cu, Cr, Ni, Zn, As, Cd, Pb, Sn, Ba, Fe and Mn) in

the mud fraction (<63 m size) of the sediment samples and THg in total sediment. The rationale for

choosing the mud fraction for the above 12 metals and gross sediments for THg has been discussed

in our two previously CMI/MMS-funded studies on the North Slope lagoon sediments [Naidu et al.

2001, 2003b]. A 5-g split of each sediment sample was suspended in deionized distilled water and the

resulting slurry was sieved through a 230-mesh nylon screen to obtain the mud fraction. The mud was

freeze dried, pulverized using an agate mortar and pestle, and an aliquot of the powder was dissolved

in a Teflon bomb by digesting in a HF–HNO3 mixture in a microwave oven. After removing HF and

diluting the digest to 100 ml, an aliquot of the acid solution was analyzed for the above 12 metals,

using either a direct injection Zeeman graphite furnace atomic absorption spectrometer, an

inductively coupled plasma/mass spectrometer (ICP/MS), or by Excaliber automated hydride

generation atomic fluorescence spectrometry (for As, Environmental Protection Agency [EPA]

method 1632). A split of the original gross sediment sample was taken for analysis of the total Hg.

The split was digested in aqua regia, and after SnCl2 reduction, pre-precipitation and dual gold

amalgamation, Hg was analyzed by cold vapor atomic absorption spectrometry (CVAAS), following

the method outlined in Bloom [1992] and Bloom et al. [1999]. The quality assurance/quality control

(QA/QC) protocol prescribed by EPA for trace metal analysis was followed, which included analysis

of spiked samples and reagent blanks, establishment of analytical precision through replicate runs,

and checking analytical accuracy via analyses of the Certified Reference Materials (CRM), namely
102

National Institute of Standards and Testing (NIST) 2709 and International Atomic Energy Agency

(IAEA) 405. Further, the analytical accuracy and precision of the trace metal analysis were checked

by inter-laboratory comparison performance evaluation, which consisted of successful participation

of our subcontractor (Frontier GeoSciences Inc., Seattle) in the1997 NIST/NOAA (National Oceanic

and Atmospheric Administration) round-robin inter-laboratory exercise NOAA/11 that was conducted

by the National Research Council of Canada (NRC). Frontier’s rating was good to excellent.

Figure 1. Study area showing locations of sediment samples in

Beaufort Lagoon..
103

Table 1. Sample locations, grain sizes, organic carbon (OC), nitrogen (N), OC/N, and stable isotopes of OC (13C ‰) and 

Beaufort Lagoon sediments.

Sample Lat °N Long °W Solids % Gravel % Sand % Silt % Clay % Mud % OC % N % OC/N 

BL03-1 69.87 142.23 53.4 0.00 6.43 77.39 16.18 93.57 2.39 0.22 10.86 BL03-2 69.87 142.26 67.9 0.00 42.28 36.63 21.09 57.72 2.33 0.20 11.60 BL03-3 69.89 142.33 51.9 0.00 18.11 51.87 30.02 81.89 2.17 0.20 10.85 BL03-4 69.91 142.24 79.5 0.00 95.55 0.09 4.36 4.45 1.72 0.17 10.11 BL03-4B 69.89 142.33 91.5 92.87 2.54 1.92 2.66 4.59 3.29 0.32 10.28 BL03-5 69.93 142.36 99.10 NA NA NA NA NA NA NA NA BL03-5B 69.86 142.18 52.9 0.00 13.94 50.36 35.70 86.06 2.00 0.18 11.11 BL03-6B 69.87 142.22 56.8 0.00 6.56 50.22 43.22 93.44 2.02 0.19 10.63 BL03-9 69.88 142.27 61.2 0.28 33.60 42.13 23.98 66.12 3.96 0.36 11.00 BL03-9B 69.87 142.19 65.0 0.00 27.86 48.44 23.69 72.14 2.27 0.19 11.94 BL03-10 69.91 142.33 74.9 0.00 81.25 13.24 5.50 18.75 2.38 0.20 11.90 BL03-11A 69.87 142.20 61.9 0.00 22.51 55.19 22.30 77.49 1.55 0.13 11.92 BL03-12 69.92 142.37 61.4 0.00 19.01 58.97 22.02 80.99 2.04 0.17 12.00 BL03-13 69.92 142.36 76.6 0.00 89.76 7.45 2.79 10.24 1.43 0.12 11.91 BL03-14 69.92 142.34 67.0 0.00 77.97 9.18 12.85 22.03 1.66 0.15 11.07 BL03-15 69.92 142.34 NA 0.00 83.42 3.15 13.42 16.58 1.76 0.17 10.35 BL03-16 69.88 142.28 43.8 0.00 24.24 52.46 23.30 75.76 3.20 0.26 12.30 BL03-17 69.87 142.28 79.6 0.12 97.88 0.05 1.95 1.99 2.57 0.30 8.56 BL03-18 69.89 142.29 56.4 0.00 25.17 36.81 38.02 74.83 2.60 0.23 11.30 BL03-19 69.89 142.31 89.8 28.60 63.62 1.34 6.45 7.78 4.05 0.38 10.66 BL03-20 69.88 142.32 79.3 37.75 58.80 0.72 2.73 3.45 3.75 0.37 10.13 OS 69.88 142.31 NA NA NA NA NA NA NA NA NA 

103
104

Hydrocarbon analyses for n-alkanes, polycyclic aromatic hydrocarbons [PAHs,] triterpanes and

steranes were performed on 19 gross sediment samples and according to well-established methods

[Venkatesan et al. 1987; Venkatesan 1994]. After thawing the frozen sample the wet sediment was

spiked with the following surrogates: deuterated n-alkanes (for n-alkanes) and hexamethylbenzene,

dodecylbenzene and deuterated terphenyl (for PAHs). After solvent extraction and separation into

individual compounds by gas chromatograph (GC) the alkanes were quantified using a flame

ionization detector. Tricyclic di- and pentacyclic triterpenoids and PAHs were measured by

GC/mass spectrometry. As in our last two CMI/MMS–funded investigations [Naidu et al. 2001,

2003b] 24 PAHs were analyzed as well as 6 additional PAHs, which are routinely analyzed by

NIST/NOAA for its QA/QC program. Also, as in our previous studies [Naidu et al. 2001, 2003b],

QA/QC measures were undertaken in the hydrocarbon analysis. In this context, our subcontractor,

Dr. Venkatesan, participated successfully first in 1992 and then in 1999 in inter-laboratory roundrobin exercises conducted by NIST/NOAA NS&T for its QA/QC program.

At the Institute of Marine Science/University of Alaska Fairbanks (IMS/UAF) laboratory, the

sediment grain size distributions were analyzed by the sieve-pipette method [Folk 1968]. The purpose

of this analysis is to gain understanding of the influence of granulometry on the concentrations of

trace metals and hydrocarbons. The chemical data obtained in this study provide continuity for

comparison with our previous database on lagoon sediments collected from several disjointed

locations of the Beaufort Sea nearshore, because the analytical techniques were the same in all

investigations. The contents of organic carbon (OC) and total nitrogen (N) and their stable isotopes

(13C and 15N) were determined on carbonate-free muds, following the methods outlined in Naidu

et al. [2000] and using a Thermo Finnigan Model Delta Plus XP isotope ratio mass spectrometer

(IRMS). The values of 13C (‰) are referenced to the V-PDB standard and those of 15N to an air

standard. The standard error of the isotope analysis is ± 0.2‰.

Statistical analysis

Statistical analysis to date has consisted of the following: Determination of the correlation

coefficients between various sediment analytes, with an aim to understanding relationships between

concentrations of metals, silt, clay and organic carbon in the mud fraction. The ultimate purpose of

this analysis is to aid in the deduction of the geochemical partitioning of the metals. The time-series

comparison was obtained between the concentrations of selected trace metals in sandy muds (>75%

silt and clay collected in 1977 [after Naidu 1981; Naidu et al. 2003b]), and muds of this study (Table

2). Surface trend analysis was conducted using all data, to elucidate if there are any geographical

gradient(s) in the individual analytes from point sources, such as the abandoned DEW Line site, or

the oil seeps around Angun or Nuvagapak Point, respectively. Cluster analysis was undertaken to

identify grouping, if any, of stations based on the trace metal compositions surrounding the above two

point sources and the pristine lagoon. This was followed by stepwise multiple discriminant analysis

of the chemical data to correlate station group separation by cluster analysis with sediment variables.
105

Table 2. Concentrations of metals in Beaufort Lagoon sediments. All are g g–1, except THg (ng g–1) and Fe (%).

Sample V g g–1 Cr Mn Ni Cu Zn As Cd Sn Ba Pb g g–1 

BL03-1 110 68.6 318 36.9 38.5 98.4 10.9 0.224 1.78 559 16.8 BL03-2 121 73.7 506 37.0 34.9 105 17.7 0.195 1.83 585 16.8 BL03-3 127 72.7 311 40.6 42.3 110 18.4 0.233 1.76 512 19.1 BL03-4 87.3 55.5 1222 27.2 19.9 60.2 10.0 0.196 1.53 355 11.0 BL03-4B 136 78.2 668 44.9 44.3 107 20.3 0.345 2.65 511 19.1 BL03-5 94.7 59.9 182 25.7 16.2 48.2 6.9 0.085 1.32 436 12.0 BL03-5B 121 71.4 407 41.8 44.0 107 15.8 0.286 1.73 583 18.5 BL03-6B 129 76.9 319 41.2 43.0 111 17.0 0.266 2.01 592 19.9 BL03-9 123 74.2 339 40.0 39.2 100 12.8 0.242 1.69 530 16.7 BL03-9B 131 83.8 370 45.0 44.7 107 12.5 0.287 1.83 649 18.6 BL03-10 106 71.1 457 32.7 28.5 80.4 11.7 0.246 1.60 473 13.1 BL03-11A 116 71.5 305 37.7 36.1 89.1 12.0 0.206 1.80 550 15.7 BL03-12 118 72.2 364 35.2 33.0 88.5 13.9 0.232 1.68 592 15.2 BL03-13 115 73.0 503 35.0 29.7 79.5 13.3 0.190 1.74 329 13.8 BL03-14 120 70.2 359 37.1 34.6 85.5 12.8 0.223 1.71 446 15.1 BL03-16 113 67.9 351 37.3 32.1 81.9 14.2 0.214 1.58 413 13.4 BL03-17 131 74.1 3132 54.3 55.2 93.3 16.4 0.341 4.86 430 28.7 BL03-18 136 81.7 346 42.2 37.9 104 16.8 0.207 1.88 572 18.3 BL03-19 125 82.2 868 43.0 41.2 107 16.4 0.623 3.99 479 23.1 BL03-20 136 81.3 829 50.8 40.8 111 15.0 0.454 4.13 496 20.3 A-Mean 120 73.0 608 39.0 37.0 94 14.0 0.265 2.00 505 17.0 Std AM 13 7.0 643 6.8 8.9 17 3.2 0.110 0.98 85 4.1 G-Mean 119 73.0 472 39.0 36.0 92 14.0 0.246 2.00 497 17.0 Std GM 13 7.0 658 6.9 9.0 17 3.2 0.110 0.99 85 4.1 % CV 11 9.7 139 17.7 25.2 19 23.2 46.400 49.40 17 24.3 

A-Mean: Arithmetic meanStd AM: Standard deviation of arithmetic meanG-Mean: Geometric meanStd GM: Standard deviation of geometric mean% CV: Percent coefficient of variation

105
106

Results

The percents of solids, gravel, sand, silt, clay and mud in gross sediments, and organic carbon (OC)

and nitrogen (N), OC/N ratios and stable isotopes of OC and N in the mud fraction of the individual

samples are included in Table 1. In Table 2 are shown the concentrations (on a dry weight basis) of

12 metals (V, Cr, Mn, Ni, Cu, Zn, As, Cd, Sn, Ba, Pb and Fe) in the mud fraction and of total Hg in

the gross sediments. Table 2 also provides the arithmetic and geometric means of the concentrations

of the metals analyzed in the 21 samples and the standard deviations and coefficient of variation (CV)

of the analysis relative to each of the metals analyzed.

The results of the QA/QC procedure for the trace metal analyses in reference to calibration

verification, calibration blanks, spikes, replicate analyses (for precision determination), and

assessment of the analytical accuracy based on Certified Reference Materials NIST 2709 and IAEA

405 are presented in Tables 3, 4, 5, 6, and 7, respectively. The concentrations of the n-alkanes,

polycyclic aromatic hydrocarbons, triterpanes and steranes in gross sediments are shown in Tables 8,

9, 10, and 11, respectively.

The correlation coefficients (r values) determined between the concentrations of silt, clay, OC, N and

the 12 metals in the mud fraction are shown in Table 12. These correlations were determined by

assuming the total percentage of silt and clay in the mud fraction to be 100%, and then prorating

(recalculating) the silt and clay percents based on their relative percents in the gross samples. Table

13 shows the time-series comparison for Beaufort Lagoon of the concentrations of selected trace

metals in suites of sandy muds collected in 1977 [Naidu 1981; Naidu et al. 2003b] and muds sampled

in 2003 (this study, Table 2).
107

Table 3. Results of the QA/QC analysis concerning calibration verification.

Analyte (unit) Batch ID ICV-TV ICV-Obs ICV-Rec CCV1-TV CCV1-Obs CCV1-Rec CCV2-TV CCV (g L–1) A 50 45.3523091 90.7046182 10 10.10702359 101.0702359 10 9.8Cr A 50 46.96249819 93.92499637 10 10.42038436 104.2038436 10 10.3Mn A 50 45.61942801 91.23885601 10 10.47879367 104.7879367 10 10.2Fe A 100 73.05313706 73.05313706 50.00000381 39.06361265 78.12721935 50.00000381 38.6Ni A 50 50.8867821 101.7735642 10 10.68007653 106.8007653 10 10.6Cu A 50 50.30532361 100.6106472 10 10.60041579 106.0041579 10 10.4Zn A 50 51.36481686 102.7296337 20 20.94026515 104.7013258 20 20.4As A 50 45.45553065 90.91106131 10 10.38048842 103.8048842 10 10.0Cd A 5 4.649514064 92.99028129 1 1.029401306 102.9401306 1 1.0Sn A 50 53.76962124 107.5392425 2.003951832 1.9Ba A 100 97.20926536 97.20926536 10 9.892735523 98.92735523 10 9.7Pb (g L–1) A 5 5.148574426 102.9714885 2 2.219005835 110.9502917 2 2.1THg (ng L–1) B 15 15.14149028 100.9432685 20 19.85846544 99.29232721 20 20.0Analyte (unit) Batch ID CCV3-TV CCV3-Obs CCV3-Rec CCV4-TV CCV4-Obs CCV4-Rec CCV5-TV CCV (g L–1) A 10 9.991375273 99.91375273 10 10.17824322 101.7824322 10 9.9Cr A 10 10.23747284 102.3747284 10 10.43125707 104.3125707 10 10.2Mn A 10 10.15512333 101.5512333 10 10.37120146 103.7120146 10 10.3Fe A 50.00000381 36.14374571 72.28748591 50.00000381 38.91393305 77.82786016 50.00000381 37.1Ni A 10 10.50028633 105.0028633 10 10.74240442 107.4240442 10 10.5Cu A 10 10.44103275 104.4103275 10 10.48291771 104.8291771 10 10.4Zn A 20 20.84492497 104.2246249 20 20.58320021 102.9160011 20 20.7As A 10 10.07112922 100.7112922 10 10.24800377 102.4800377 10 9.9Cd A 1 1.025773372 102.5773372 1 1.021258889 102.1258889 1 1.0Sn A 1.928422781 1.926497615 1.8Ba A 10 9.859105991 98.59105991 10 9.892031641 98.92031641 10 9.6Pb (g L–1) A 2 2.169817453 108.4908727 2 2.173315751 108.6657876 2 2.1THg (ng L–1) B 20 20.16609426 100.8304713 20 20.2686372 101.343186Analyte (unit) Batch ID CCV6-TV CCV6-Obs CCV6-Rec CCV7-TV CCV7-Obs CCV7-Rec CCV8-TV CCV (g L–1) A 10 9.849617357 98.49617357 10 9.783745819 97.83745819 10 9.5Cr A 10 10.1783771 101.783771 10 10.02781438 100.2781438 10 9.8Mn A 10 10.09998875 100.9998875 10 10.10092558 101.0092558 10 9.8Fe A 50.00000381 38.19419485 76.38838388 50.00000381 36.77596348 73.55192136 50.00000381 37.0Ni A 10 10.36700825 103.6700825 10 10.13588373 101.3588373 10 9.8Cu A 10 10.31545979 103.1545979 10 10.4797927 104.797927 10 10.4Zn A 20 20.23273385 101.1636692 20 21.04050314 105.2025157 20 20.6As A 10 9.847302208 98.47302208 10 9.983570755 99.83570755 10 9.7Cd A 1 1.002141532 100.2141532 1 1.031852437 103.1852437 1 0.9Sn A 1.887518769 1.858317909 1.8Ba A 10 9.763108636 97.63108636 10 9.730458986 97.30458986 10 9.6Pb (g L–1) A 2 2.09613605 104.8068025 2 2.12493247 106.2466235 2 2.1THg (ng L–1) B

107
108

Table 3. (continued)

Analyte (unit) Batch ID CCV9-TV CCV9-Obs CCV9-Rec CCV10-TV CCV10-Obs CCV10-Rec CCV11-TV CCV (g L–1) A 10 9.650594968 96.50594968 10 9.30736503 93.0736503 10 9.4Cr A 10 9.76573373 97.6573373 10 9.544484373 95.44484373 10 9.6Mn A 10 9.827188222 98.27188222 10 9.458645312 94.58645312 10 9.6Fe A 50.00000381 39.53449401 79.06898199 50.00000381 42.37362985 84.74725323 50.00000381 47.0Ni A 10 9.932683621 99.32683621 10 9.576026001 95.76026001 10 9.6Cu A 10 10.2025627 102.025627 10 9.890386321 98.90386321 10 10.0Zn A 20 20.3137235 101.5686175 20 19.52618759 97.63093797 20 19.5As A 10 9.585721832 95.85721832 10 9.299644052 92.99644052 10 9.4Cd A 1 0.989542451 98.9542451 1 0.947602132 94.76021317 1 0.9Sn A 1.918633807 1.824633062 1.9Ba A 10 9.838818362 98.38818362 10 9.148799712 91.48799712 10 9.6Pb (g L–1) A 2 2.12562117 106.2810585 2 2.025839268 101.2919634 2 2.1THg (ng L–1) BAnalyte (unit) Batch ID CCV12-TV CCV12-Obs CCV12-Rec CCV13-TV CCV13-Obs CCV13-Rec CCV14-TV CCV (g L–1) A 10 9.438779146 94.38779146 10 10.11357487 101.1357487 10 10.0Cr A 10 9.5126383 95.126383 10 10.31263132 103.1263132 10 10.2Mn A 10 9.651176105 96.51176105 10 10.29423463 102.9423463 10 10.1Fe A 50.00000381 53.94851826 107.8970283 50.00000381 43.16024445 86.32048232 50.00000381 38.8Ni A 10 9.577118118 95.77118118 10 10.4172239 104.172239 10 10.2Cu A 10 9.803673602 98.03673602 10 10.22385582 102.2385582 10 10.2Zn A 20 19.1865418 95.93270898 20 20.44292065 102.2146032 20 20.3As A 10 9.149850875 91.49850875 10 9.69797573 96.9797573 10 9.6Cd A 1 0.947509367 94.75093668 1 1.017795471 101.7795471 1 0.9Sn A 1.87476249 1.934516221 1.9Ba A 10 9.423369849 94.23369849 10 9.725311342 97.25311342 10 9.5Pb (g L–1) A 2 2.101084924 105.0542462 2 2.142351295 107.1175648 2 2.1THg (ng L–1) B

108
109

Table 4. Results of the QA/QC analysis concerning calibration blanks.

Analyte (unit) Batch ID ICB CCB1 CCB2 CCB3 CCB4 CCB5 CCV (g L–1) A –0.019412662 –0.022915678 0.003640651 0.017759003 0.048708424 0.023439894 0.0243Cr A –0.002122966 0.003766614 0.010165725 –0.002818597 –0.009003925 –0.023367497 –0.0305Mn A 0.001514224 –0.000366763 0.001696906 0.000215278 0.001268211 –0.000454617 0.0010Fe A –1.281956901 2.199543674 2.977579904 2.51391155 1.110781557 0.8398272 0.8799Ni A 0.006692471 0.001049361 0.003343839 0.001642198 0.001944399 0.002645204 0.0018Cu A –0.002534501 –0.01197791 –0.016823747 –0.010802005 –0.007336244 –0.004534008 –0.0053Zn A 0.009717066 –0.007442436 0.000322795 0.006762036 –0.006444729 0.008655965 0.0067As A 0.03487219 0.023736137 0.056976428 0.045339725 0.040974748 0.03895845 0.0788Cd A 0.001390389 0.00066378 8.54564E–05 0.000269234 –0.000194387 0.000389272 –0.0002Sn A 0.247473511 0.019964263 0.023887361 0.026699068 0.02564591 0.020272009 0.0281Ba A 0.010189992 0.001502795 0.000655891 0.000560007 0.001351205 0.001324258 0.0020Pb (g L–1) A 0.000486244 0.000574009 0.000689952 0.000983047 0.000643604 0.000557415 0.0002THg (ng L–1) B 0.005127147 0.010254294 0.020508588 0.076907204 0.061525765Analyte (unit) Batch ID CCB8 CCB9 CCB10 CCB11 CCB12 CCV (g L–1) A –0.010947072 –0.016725359 0.011709312 0.027295459 0.010289739 –0.0139Cr A –0.056714697 –0.075768997 –0.066915037 –0.094584515 –0.116960163 –0.1312Mn A –0.001074566 0.002161963 0.03136897 0.054314014 0.051490255 0.0154Fe A 2.653762053 2.800329156 9.014763907 14.93157478 8.144938152 1.3419Ni A 0.012223274 0.01125807 0.033918849 0.047015877 0.046362983 0.0631Cu A –0.008492945 –0.000696379 0.013466952 0.027762757 0.027535099 0.0061Zn A 0.033953836 0.038064624 0.099442749 0.102272735 0.1616685 1.1125As A 0.061924645 0.084226575 0.115874334 0.115621475 0.148838907 0.0731Cd A –1.18679E–05 0.000420587 0.002093238 0.003724701 0.004472952 0.0002Sn A 0.020168967 0.01414789 0.033233545 0.026029557 0.025150975 0.0130Ba A 0.000679496 0.001074303 0.025160283 0.044806803 0.045522298 0.0113Pb (g L–1) A –0.000259981 –0.000394815 0.004911209 0.009329379 0.009550545 0.0052THg (ng L–1) B

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110

Table 5. Results of the QA/QC analysis concerning spikes.

Analyte

(unit)

Batch

ID

Sample

ID Mean SpikeTV

Dup

Spike

TV

Obs

Spike

Value

Dup Obs

Spike

Value

Spike

% Rec

Dup

Spike

% Rec

RPD

V (mg kg–1) A BL03-4 87.22 98.81 98.81 190.0 190.8 104.0 104.8 0.7

V A BL03-3 126.6 79.37 79.68 205.3 207.9 99.1 102.0 2.9

Cr A BL03-4 55.07 98.81 98.81 157.7 158.3 103.8 104.4 0.6

Cr A BL03-3 72.52 79.37 79.68 145.8 142.0 92.3 87.2 5.7

Mn A BL03-4 1215 98.81 98.81 1324 1324 110.4 109.6 0.8

Mn A BL03-3 312.4 79.37 79.68 385.4 394.4 92.1 102.9 11.1

Fe A BL03-3 30902 79.37 79.68 32448 30620 1949 –353.9 288.8

Fe A BL03-4 21899 494.1 494.1 22701 22647 162.3 151.4 6.9

Ni A BL03-3 40.88 79.37 79.68 124.0 126.3 104.8 107.2 2.3

Ni A BL03-4 27.48 123.5 123.5 154.7 156.6 103.0 104.5 1.4

Cu A BL03-4 20.40 123.5 123.5 148.1 146.4 103.4 102.0 1.4

Cu A BL03-3 42.67 79.37 79.68 127.3 128.1 106.7 107.2 0.5

Zn A BL03-3 111.6 79.37 79.68 187.8 188.3 96.1 96.3 0.2

Zn A BL03-4 60.59 247.0 247.0 311.7 313.3 101.6 102.3 0.6

As A BL03-3 18.48 79.37 79.68 91.56 92.81 92.1 93.3 1.3

As A BL03-4 9.84 98.81 98.81 113.1 111.9 104.5 103.3 1.1

Cd A BL03-3 0.229 79.37 79.68 76.35 76.99 95.9 96.3 0.4

Cd A BL03-4 0.196 9.881 9.881 10.47 10.27 103.9 101.9 2.0

Sn A BL03-3 1.774 79.37 79.68 80.55 79.81 99.3 97.9 1.3

Sn A BL03-4 1.523 24.70 24.70 27.54 26.69 105.3 101.9 3.3

Ba A BL03-3 497.2 79.37 79.68 596.7 521.8 125.4 30.9 120.9

Ba A BL03-4 357.1 197.6 197.6 578.6 576.8 112.1 111.2 0.8

Pb A BL03-3 19.08 79.37 79.68 98.77 98.35 100.4 99.5 0.9

Pb (mg kg–1) A BL03-4 11.04 24.70 24.70 35.68 35.48 99.7 99.0 0.8

THg (ng g–1) B BL03-4 12.62 48.75 50.20 59.84 61.63 96.9 97.6 0.8

Table 6. Results of the QA/QC analysis concerning replicates (precision determination).

Analyte (unit) Batch ID Sample ID Sample Duplicate Mean RPD

V (mg kg–1) A BL03-4 87.28 87.17 87.22 0.1

V A BL03-3 127.0 126.2 126.6 0.6

Cr A BL03-4 55.46 54.68 55.07 1.4

Cr A BL03-3 72.70 72.35 72.52 0.5

Mn A BL03-4 1222 1209 1215 1.1

Mn A BL03-3 310.5 314.3 312.4 1.2

Fe A BL03-3 30998 30805 30902 0.6

Fe A BL03-4 22087 21711 21899 1.7

Ni A BL03-3 40.63 41.13 40.88 1.2

Ni A BL03-4 27.23 27.74 27.48 1.8

Cu A BL03-4 19.87 20.94 20.40 5.3

Cu A BL03-3 42.27 43.07 42.67 1.9

Zn A BL03-3 110.4 112.8 111.6 2.1

Zn A BL03-4 60.21 60.96 60.59 1.2

As A BL03-3 18.36 18.60 18.48 1.3

As A BL03-4 9.97 9.71 9.84 2.6

Cd A BL03-3 0.233 0.225 0.229 3.4

Cd A BL03-4 0.196 0.196 0.196 0.2

Sn A BL03-3 1.76 1.79 1.77 1.6

Sn A BL03-4 1.53 1.51 1.52 1.1

Ba A BL03-3 511.6 482.8 497.2 5.8

Ba A BL03-4 355.0 359.2 357.1 1.2

Pb A BL03-3 19.10 19.05 19.08 0.2

Pb (mg kg–1) A BL03-4 11.03 11.05 11.04 0.2

THg (ng g–1) B BL03-4 11.68 13.56 12.62 14.9

% Total Solids C BL03-4 79.5 78.9 79.2 0.8

% Total Solids C BL03-17 79.6 79.5 79.6 0.1
111

Table 7. Results of the QA/QC analysis concerning analytical accuracy

using certified reference materials, NIST 2709 and IAEA 405.

Analyte (unit) Batch ID CRM Identity Cert Value Obs Value % Rec

V (mg kg–1) A NIST 2709 112.00 97.12 86.7

V A BlankSpike 80.00 85.78 107.2

V A IAEA 405 95.00 90.21 95.0

Cr A IAEA 405 84.00 67.42 80.3

Cr A BlankSpike 80.00 86.14 107.7

Cr A NIST 2709 130.00 93.28 71.8

Mn A NIST 2709 538.00 420.18 78.1

Mn A IAEA 405 495.00 384.99 77.8

Mn A BlankSpike 80.00 87.95 109.9

Fe A BlankSpike 80.00 58.29 72.9

Fe A NIST 2709 35000.00 28463.83 81.3

Fe A IAEA 405 37400.00 29885.25 79.9

Ni A IAEA 405 32.50 32.42 99.8

Ni A BlankSpike 80.00 93.66 117.1

Ni A NIST 2709 88.00 76.19 86.6

Cu A BlankSpike 80.00 89.09 111.4

Cu A NIST 2709 34.60 37.34 107.9

Cu A IAEA 405 47.70 54.28 113.8

Zn A IAEA 405 279.00 281.40 100.9

Zn A NIST 2709 106.00 106.90 100.8

Zn A BlankSpike 80.00 83.51 104.4

As A NIST 2709 17.70 16.84 95.1

As A IAEA 405 23.60 23.47 99.5

As A BlankSpike 80.00 75.32 94.2

Cd A BlankSpike 80.00 78.38 98.0

Cd A NIST 2709 0.38 0.65 172.1

Cd A IAEA 405 0.73 0.74 101.8

Sn A IAEA 405 7.60 9.54 125.5

Sn A BlankSpike 80.00 79.93 99.9

Ba A NIST 2709 968.00 845.36 87.3

Ba A BlankSpike 80.00 97.11 121.4

Pb A NIST 2709 18.90 19.29 102.1

Pb A IAEA 405 74.80 77.83 104.0

Pb (mg kg–1) A BlankSpike 80.00 78.63 98.3

THg (ng g–1) B IAEA 405 810.00 765.78 94.5
112

Table 8. Distribution (ng g–1 dry wt) of n-alkanes in gross sediments of Beaufort Lagoon. All sample numbers have the pre

Sample ID – UCLA No. 1 2 3 3D* 4 4R* 5B 6B 9 9B 9BD* Surrogate Recovery (%)Deu C14 nd$ 52 52 53 56 57 53 55 52 62 62 Deu C24 nd$ 66 67 69 69 64 70 63 65 76 76 Deu C36 nd$ 67 78 75 61 66 72 65 66 66 79 n-alkane (ng g–1 dry wt)n-C10 nd nd nd nd nd nd nd nd nd nd nd n-C11 nd nd nd nd nd nd 62.9 217.6 nd nd nd n-C12 nd nd nd nd nd nd nd nd nd nd nd n-C13 nd nd nd nd nd nd nd nd nd nd nd n-C14 nd nd nd nd nd nd nd nd nd nd nd n-C15 67.1 34.4 60.7 52.6 nd nd 38.7 72.3 45.5 41.1 30.7 n-C16 96.2 26.0 56.6 41.9 nd nd 32.8 62.5 56.9 50.9 40.5 n-C17 184.3 70.9 192.5 172.5 3.2 3.8 117.0 198.8 192.8 98.5 76.0 pr nd nd nd nd nd nd nd nd nd nd nd n-C18 89.6 38.2 108.7 98.0 1.8 2.2 63.5 110.4 102.0 54.3 46.2 ph nd nd nd nd nd nd nd nd nd nd nd n-C19 280.1 99.8 317.3 287.4 3.8 4.6 181.5 307.2 321.8 162.1 140.2 n-C20 166.5 65.2 198.8 180.4 2.8 3.4 116.9 202.4 191.1 110.5 96.5 n-C21 579.0 204.2 630.6 584.7 7.3 7.5 382.1 627.9 623.2 360.7 316.3 n-C22 312.8 116.1 323.6 311.7 5.1 5.7 196.5 332.7 330.7 196.8 180.1 n-C23 990.9 354.0 1056.6 974.1 13.2 14.9 672.8 1068.7 995.0 676.7 605.0 3n-C24 268.7 105.7 287.0 294.6 5.6 6.0 186.8 318.7 305.5 188.1 184.1 n-C25 826.3 341.2 839.9 814.5 14.9 14.6 573.5 951.2 819.1 735.8 661.7 3n-C26 126.6 58.2 153.8 146.9 3.9 3.6 103.0 178.8 146.2 112.4 109.5 n-C27 1015.9 472.0 1483.4 1450.0 18.1 18.5 867.3 1239.8 1738.7 1496.4 1370.4 4n-C28 73.3 34.5 94.5 176.6 3.5 3.4 70.0 120.0 80.1 81.0 144.5 n-C29 886.5 376.5 1217.9 1346.5 17.9 18.6 933.4 1323.9 1054.3 1160.3 1450.2 4n-C30 83.2 29.9 128.9 141.1 3.6 2.8 79.0 129.8 35.0 100.8 116.7 n-C31 749.0 308.8 1190.4 1330.8 18.4 18.4 889.2 1312.5 938.5 988.7 1152.2 3n-C32 146.2 46.7 82.0 39.4 1.8 2.0 49.0 86.0 41.5 68.5 39.2 n-C33 210.3 90.1 378.7 444.7 7.1 7.1 274.4 424.7 264.6 300.5 375.4 n-C34 nd 26.7 58.2 63.3 2.4 2.0 nd 67.6 80.1 nd 47.9 n-C35 nd nd nd 44.7 2.0 2.6 nd nd nd nd nd n-C36 nd nd nd nd nd nd nd nd nd nd nd Total n-alkanes (ng g–1 dry wt) 7152.4 2899.1 8860.1 8996.4 136.5 142.0 5890.2 9353.3 8362.6 6984.2 7183.3 27 C12–C19 (ng g–1 dry wt) 717.3 269.4 735.9 652.3 8.8 10.7 433.5 751.2 719.0 407.0 333.5  C20–C33 (ng g–1 dry wt) 6435.1 2603.0 8066.0 8236.0 123.2 126.7 5393.8 8316.9 7563.5 6577.3 6801.8 25Pr/Ph nd nd nd nd nd nd nd nd nd nd nd Odd/Even** 4.2 4.3 4.9 5.0 3.5 3.5 5.5 4.7 5.1 6.3 6.1 

112
113

Table 8. (continued)

Sample ID – UCLA No. 13 14 15 16 17 18 19 20 OS# Pr. Blk XSPIKE 1 % Recovery Surrogate Recovery (%)Deu C14 57 55 58 nd$ 53 58 60 54 nd$ 51 52 Deu C24 76 64 61 nd$ 65 68 61 66 nd$ 57 64 Deu C36 80 65 58 nd$ 92 70 65 68 nd$ 66 74 n-alkane (ng g–1 dry wt)

g g–1

dry wt

n-C10 nd nd nd nd nd nd 25.4 nd 499.6 nd 18 n-C11 nd nd nd nd nd nd 34.2 nd 3761.2 nd 28 n-C12 nd nd nd nd nd nd 17.3 nd 489.6 nd 33 n-C13 nd 32.2 nd nd nd nd nd nd 253.8 nd 40 n-C14 nd 27.2 nd nd nd nd nd nd 148.3 nd 50 n-C15 74.2 66.1 nd nd 2.7 31.7 14.9 17.0 65.0 nd 57 n-C16 55.7 53.9 nd 223.8 2.0 31.4 15.2 9.6 nd nd 66 n-C17 170.8 144.6 75.4 754.9 5.7 103.6 66.4 37.1 nd nd 65 pr nd 38.7 nd nd nd 13.1 7.7 nd nd nd 67 n-C18 94.4 80.1 41.0 339.8 3.9 58.9 37.5 19.1 nd nd 69 ph nd 18.4 nd nd nd nd nd nd nd nd 69 n-C19 36.0 208.0 103.7 1141.0 1.9 174.1 122.4 62.6 nd nd 70 n-C20 146.1 147.8 73.7 680.8 5.3 112.2 76.4 36.1 58.1 1.51 72 n-C21 421.2 464.0 227.0 2275.8 13.9 364.2 246.9 109.3 86.9 nd 72 n-C22 36.9 260.3 138.3 1238.2 7.5 199.6 127.8 61.3 53.2 nd 76 n-C23 831.6 834.4 444.2 4079.3 20.6 630.8 400.8 167.0 67.6 nd 69 n-C24 35.0 250.1 142.8 1116.4 6.9 186.7 112.7 51.3 71.8 nd 60 n-C25 911.3 888.6 491.9 3918.1 19.4 556.5 349.5 136.0 218.4 nd 65 n-C26 191.3 124.6 86.3 574.5 4.2 100.5 52.0 27.0 87.9 nd 70 n-C27 1608.1 1718.1 863.0 5131.4 20.6 1085.1 369.4 146.4 179.1 nd 69 n-C28 159.2 97.3 65.5 328.6 3.1 64.3 26.1 15.5 60.2 nd 66 n-C29 1628.0 1538.0 682.9 4763.2 25.7 855.6 325.2 143.7 138.0 nd 66 n-C30 149.4 98.4 60.2 334.1 2.2 93.1 41.0 20.4 230.1 nd 66 n-C31 1621.1 1150.4 600.7 4488.1 25.2 861.2 339.9 156.5 171.2 nd 67 n-C32 68.6 32.3 0.0 784.7 0.0 26.2 15.1 11.8 169.6 nd 64 n-C33 564.4 354.9 195.2 1339.2 8.3 271.3 110.5 51.0 nd nd 66 n-C34 49.4 52.6 nd 330.4 nd 45.8 29.9 13.5 nd nd 64 n-C35 75.2 30.2 nd nd nd 24.4 10.9 nd nd nd 60 n-C36 nd nd nd nd nd nd nd nd nd nd 69 Total n-alkanes (ng g–1 dry wt) 8927.9 8654.2 4291.7 33842.3 179.2 5877.3 2967.7 1292.1 6819.4 C12–C19 (ng g–1 dry wt) 431.1 612.2 220.1 2459.5 16.1 399.7 273.8 145.3 956.6 C20–C33 (ng g–1 dry wt) 8372.3 7959.2 4071.7 31052.4 163.1 5407.4 2593.5 1133.3 1602.1Pr/Ph nd 2.1 nd nd nd na† na† nd ndOdd/Even** 8.1 6.2 6.1 4.7 4.1 5.4 4.4 3.9 1.3

* Duplicate (D) or 

** Summed n-C15–

# OS=oil spill sam

 of concentratio

nd$=dilution too gr

nd=not detectedna†=not applicable

113
114

Table 9. Distribution (ng g–1 dry wt) of PAHs in gross sediments of Beaufort Lagoon. All lagoon sample numbers have the p

Sample ID – UCLA No. 1 2 3 3D* 4 5B 6B 1 2 3 3Surrogate Recovery (%) PAH (ng g–1 dry wt) (cont.)hexamethylbenzene 62 53 50 50 38 41 44 C3-fluoranthenes/pyrenes 4.9 2.6 2.2 n-dodecylbenzene 57 58 62 64 59 59 60 C4-fluoranthenes/pyrenes nd 1.7 3.3 4-terphenyl-D14 64 60 75 68 68 65 57 benz(a)anthracene 4.0 1.4 2.7 

chrysene/triphenylene 12.0 8.0 14.8 1

PAH (ng g–1 dry wt) C1-chrysenes/triphenylenes 7.9 9.2 12.5 1naphthalene 7.7 7.4 13.0 16.7 nd 7.3 6.7 C2-chrysenes/triphenylenes 2.6 4.3 6.0 C1-naphthalenes 18.9 21.3 26.3 27.2 nd 15.4 21.4 C3-chrysenes/triphenylenes tr tr nd 2-methylnaphthalene 10.0 10.7 14.8 14.9 nd 6.4 12.1 C4-chrysenes/triphenylenes nd nd nd 1-methylnaphthalene 8.9 10.6 11.6 12.4 nd 9.0 9.4 benzo(k)fluoranthene 3.8 1.0 1.0 C2-naphthalenes 27.1 33.3 41.6 48.2 0.4 28.9 50.0 benzo(b)fluoranthene 7.6 4.5 8.0 2,6-dimethylnaphthalene 8.0 8.5 6.9 7.5 nd 5.6 16.0 benzo(e)pyrene 9.2 5.6 8.1 C3-naphthalenes 14.7 26.8 34.1 37.8 0.5 13.8 39.6 benzo(a)pyrene 3.8 1.6 1.6 2,3,5-trimethylnaphthalene nd 4.4 6.1 7.0 nd 2.4 3.8 9,10-diphenylanthracene nd nd nd C4-naphthalenes 1.7 12.0 17.9 19.8 nd 7.0 18.9 perylene 30.3 35.9 42.5 4biphenyl 5.6 4.3 9.2 9.9 nd 4.9 7.1 indeno(1,2,3-cd)pyrene nd nd nd acenaphthylene nd nd nd nd nd nd nd dibenz(a,h)anthracene nd nd nd acenaphthene nd 0.6 0.9 1.1 nd nd nd picene nd nd nd fluorene nd 2.9 4.9 5.1 nd 2.7 5.4 benzo(ghi)perylene nd 3.6 4.6 52-methylfluorene nd 2.7 4.3 5.1 nd nd nd anthanthrene nd nd nd C1-fluorenes 3.2 3.8 6.4 nd tr 2.8 5.8 coronene nd nd nd C2-fluorenes 4.9 6.7 13.3 nd tr 6.4 18.6 1,2,4,5-dibenzopyrene nd nd nd C3-fluorenes tr tr nd nd nd tr tr C1-C20H12 aromatics 9.5 5.1 6.9 phenanthrene 19.5 15.2 27.8 32.0 1.1 17.9 33.4 C2-C20H12 aromatics tr 1.7 2.6 1-methylphenanthrene 4.4 4.9 6.7 7.5 nd 3.4 8.8 C3-C20H12 aromatics nd tr nd anthracene 1.6 nd 1.3 1.3 nd nd nd C4-C20H12 aromatics nd tr nd C1-phenanthrenes/anthracenes 47.9 51.3 72.5 77.1 20.8 34.6 123.2C2-phenanthrenes/anthracenes 12.8 26.7 32.6 35.7 1.8 19.4 57.5 sum-naphthalenes(N) 70.1 100.9 132.9 143,6-dimethylphenanthrene nd 1.2 3.7 4.2 nd nd 1.8 sum-fluorenes(F) 8.1 13.4 24.6 C3-phenanthrenes/anthracenes 18.0 23.7 43.7 46.6 4.7 22.2 58.3 sum-phenanthrenes/anthracenes(PA) 105.1 127.9 194.8 21C4-phenanthrenes/anthracenes 5.2 10.9 17.0 17.5 1.0 9.5 28.0 sum-dibenzothiophenes(D) 11.0 35.2 23.3 22,3-benzofluorene nd 1.6 2.5 3.2 nd 1.3 2.9 sum-fluoranthenes/pyrenes(FP) 25.8 23.2 31.8 31,1 ́-binaphthalene nd nd nd nd nd nd nd sum-chrysenes(C) 22.5 21.5 33.3 3dibenzothiophene** nd 3.4 5.4 7.6 0.2 3.4 6.7 sum-C20H12 aromatics(C20) 64.2 55.2 70.6 7C1-dibenzothiophenes** 2.5 3.5 3.5 3.8 0.3 2.1 6.3 sum- 4,5 PAH (4,5 PAH) 107.0 94.6 128.9 13C2-dibenzothiophenes** 2.6 4.6 6.1 6.6 nd 2.9 12.0 sum-PAH(t-PAH) 316.4 388.8 531.3 55C3-dibenzothiophenes** 5.9 23.7 6.5 6.8 nd 1.8 6.8 N/PA 0.67 0.79 0.68 C4-dibenzothiophenes** nd tr 1.8 1.9 nd nd nd N/perylene 2.31 2.81 3.13 fluoranthene 3.8 2.7 4.1 4.3 nd 2.5 5.6 F/perylene 0.27 0.37 0.58 pyrene 5.7 4.9 7.6 8.6 0.6 4.4 9.6 PA/perylene 3.47 3.56 4.59 C1-fluoranthenes/pyrenes 3.8 4.2 6.8 7.1 tr 4.3 10.8 FP/perylene 0.85 0.65 0.75 C2-fluoranthenes/pyrenes 7.5 7.2 7.9 8.1 tr 7.2 12.8 t-PAH/perylene 10.45 10.83 12.51 1

114
115

Table 9. (continued)

Sample ID – UCLA No. 9 9B 10 11A 12 13 14 9 9B 10 1Surrogate Recovery (%) PAH (ng g–1 dry wt) (cont.)hexamethylbenzene 46 43 39 44 40 40 45 C3-fluoranthenes/pyrenes 1.6 2.9 1.3 n-dodecylbenzene 66 50 59 62 56 56 59 C4-fluoranthenes/pyrenes nd 3.2 1.1 4-terphenyl-D14 72 63 72 63 69 69 71 benz(a)anthracene 1.8 1.7 0.6 

chrysene/triphenylene 9.3 8.9 3.3 1

PAH (ng g–1 dry wt) C1-chrysenes/triphenylenes 6.7 6.8 2.4 1naphthalene 8.3 7.6 1.7 8.9 3.3 1.0 9.7 C2-chrysenes/triphenylenes 1.9 2.4 0.9 C1-naphthalenes 19.2 12.4 4.4 26.4 18.2 4.6 25.9 C3-chrysenes/triphenylenes nd nd nd 2-methylnaphthalene 9.7 7.0 2.5 13.8 9.3 2.7 14.8 C4-chrysenes/triphenylenes nd nd nd 1-methylnaphthalene 9.5 5.5 2.0 12.7 8.9 1.9 11.2 benzo(k)fluoranthene nd 0.6 0.3 C2-naphthalenes 23.3 19.5 7.8 42.7 46.9 9.4 46.7 benzo(b)fluoranthene 4.7 5.3 2.1 2,6-dimethylnaphthalene 7.3 6.2 2.0 14.4 12.9 2.5 12.1 benzo(e)pyrene 4.1 4.8 2.0 C3-naphthalenes 16.5 13.6 7.7 33.0 40.1 7.4 34.0 benzo(a)pyrene nd nd 0.4 2,3,5-trimethylnaphthalene 3.0 nd nd 3.1 4.2 0.9 4.1 9,10-diphenylanthracene nd nd nd C4-naphthalenes 7.0 6.5 2.6 13.1 16.1 4.6 18.4 perylene 15.6 79.8 13.7 3biphenyl 4.5 4.8 1.3 7.2 4.7 1.5 5.9 indeno(1,2,3-cd)pyrene nd nd nd acenaphthylene nd nd nd nd nd nd nd dibenz(a,h)anthracene nd nd nd acenaphthene nd nd nd nd nd nd nd picene nd nd nd fluorene 2.5 2.6 nd 4.3 4.0 1.1 4.0 benzo(ghi)perylene nd nd nd 2-methylfluorene nd nd nd nd 3.1 0.8 3.0 anthanthrene nd nd nd C1-fluorenes 3.5 3.1 1.2 5.3 5.5 1.2 6.3 coronene nd nd nd C2-fluorenes 6.0 5.6 2.6 15.0 13.1 1.9 13.1 1,2,4,5-dibenzopyrene nd nd nd C3-fluorenes tr tr tr tr tr tr tr C1-C20H12 aromatics 2.7 4.4 3.1 phenanthrene 14.9 17.3 5.3 24.0 25.1 6.4 21.3 C2-C20H12 aromatics nd 3.7 0.7 1-methylphenanthrene 3.1 5.1 1.6 6.7 8.0 1.9 6.5 C3-C20H12 aromatics nd nd nd anthracene nd nd nd nd nd nd nd C4-C20H12 aromatics nd nd nd C1-phenanthrenes/anthracenes 25.9 37.2 41.5 112.8 138.0 56.6 193.7C2-phenanthrenes/anthracenes 15.9 18.1 10.0 36.0 52.7 11.9 47.3 sum-naphthalenes(N) 74.3 59.5 24.2 123,6-dimethylphenanthrene 1.2 1.2 0.4 1.8 1.8 0.4 1.9 sum-fluorenes(F) 12.0 11.3 3.8 2C3-phenanthrenes/anthracenes 15.0 14.1 17.2 63.7 82.7 21.4 100.4 sum-phenanthrenes/anthracenes(PA) 81.5 94.5 79.4 25C4-phenanthrenes/anthracenes 6.3 7.8 5.4 20.3 18.6 6.4 22.9 sum-dibenzothiophenes(D) 16.8 6.3 4.4 12,3-benzofluorene 1.1 nd 0.3 2.1 2.6 nd 2.1 sum-fluoranthenes/pyrenes(FP) 15.5 24.6 9.1 21,1 ́-binaphthalene nd nd nd nd nd nd nd sum-chrysenes(C) 17.8 18.1 6.6 3dibenzothiophene** 3.5 3.3 1.0 5.0 nd 1.4 3.7 sum-C20H12 aromatics(C20) 27.1 98.6 22.3 5C1-dibenzothiophenes** 2.1 1.8 1.5 4.4 6.5 1.5 6.0 sum- 4,5 PAH (4,5 PAH) 59.6 134.8 34.8 10C2-dibenzothiophenes** 3.1 1.2 1.2 6.3 8.5 nd 4.1 sum-PAH(t-PAH) 252.6 319.4 152.0 55C3-dibenzothiophenes** 8.0 nd 0.7 4.2 8.4 nd 3.1 N/PA 0.91 0.63 0.30 C4-dibenzothiophenes** tr nd nd nd tr nd nd N/perylene 4.78 0.75 1.77 fluoranthene 3.2 2.4 0.9 4.1 5.1 1.2 3.7 F/perylene 0.77 0.14 0.28 pyrene 4.6 4.8 1.5 6.5 8.5 2.1 7.6 PA/perylene 5.24 1.18 5.80 C1-fluoranthenes/pyrenes 3.0 3.4 1.7 6.7 8.0 2.3 7.6 FP/perylene 1.00 0.31 0.66 C2-fluoranthenes/pyrenes 3.1 7.8 2.6 7.7 11.4 2.5 8.8 t-PAH/perylene 16.24 4.00 11.11 1

115
116

Table 9. (continued)

Sample ID – UCLA No. 15 16 17 18 19 20 OS# 15 16 17 1Surrogate Recovery (%) g g–1 dry wt PAH (ng g–1 dry wt) (cont.) hexamethylbenzene 44 46 43.0 47 40 51 50 C3-fluoranthenes/pyrenes 1.5 nd tr n-dodecylbenzene 63 59 50.0 58 59 56 61 C4-fluoranthenes/pyrenes 2.0 nd nd 4-terphenyl-D14 70 68 69.0 63 68 69 70 benz(a)anthracene 0.8 4.8 0.1 

chrysene/triphenylene 6.8 10.2 0.3 

PAH (ng g–1 dry wt) C1-chrysenes/triphenylenes 6.1 6.6 0.1 1naphthalene nd 9.1 nd 9.6 1.3 0.5 2.9 C2-chrysenes/triphenylenes 2.5 nd nd C1-naphthalenes nd 11.9 0.1 24.2 2.7 1.0 nd C3-chrysenes/triphenylenes nd nd nd n2-methylnaphthalene nd 8.7 0.1 12.1 1.5 0.5 nd C4-chrysenes/triphenylenes nd nd nd n1-methylnaphthalene nd 3.3 nd 12.0 1.2 0.5 4.8 benzo(k)fluoranthene 0.8 nd nd C2-naphthalenes nd 20.9 0.3 34.6 4.5 1.9 83.0 benzo(b)fluoranthene 3.8 5.9 0.2 2,6-dimethylnaphthalene nd 7.5 nd 10.0 1.4 0.4 31.0 benzo(e)pyrene 4.5 nd 0.2 C3-naphthalenes 4.9 7.7 0.3 26.2 4.3 1.4 44.2 benzo(a)pyrene nd nd nd n2,3,5-trimethylnaphthalene nd nd nd 4.5 0.8 nd 7.4 9,10-diphenylanthracene nd nd nd nC4-naphthalenes 5.9 nd nd 13.2 2.1 0.6 15.0 perylene 19.2 29.2 nd 1biphenyl nd 4.8 0.1 5.4 0.8 0.3 nd indeno(1,2,3-cd)pyrene nd nd nd nacenaphthylene nd nd nd nd nd nd nd dibenz(a,h)anthracene nd nd nd nacenaphthene nd nd nd nd nd nd 0.5 picene nd nd nd nfluorene nd nd nd 3.3 0.7 nd 1.5 benzo(ghi)perylene nd nd nd 2-methylfluorene nd nd nd 2.7 0.4 nd 2.2 anthanthrene nd nd nd nC1-fluorenes 2.7 nd 0.3 3.9 0.9 0.8 2.0 coronene nd nd nd nC2-fluorenes 8.5 nd 0.4 10.6 3.7 1.6 2.1 1,2,4,5-dibenzopyrene nd nd nd nC3-fluorenes tr nd tr nd tr tr nd C1-C20H12 aromatics 4.2 tr 0.3 phenanthrene 10.3 17.1 1.0 18.3 3.6 1.9 2.3 C2-C20H12 aromatics nd tr nd n1-methylphenanthrene nd 3.7 nd 4.8 0.9 nd 1.1 C3-C20H12 aromatics nd nd nd nanthracene nd nd nd nd nd nd 0.7 C4-C20H12 aromatics nd nd nd nC1-phenanthrenes/anthracenes 86.7 25.3 15.0 124.1 24.2 109.3 3.8C2-phenanthrenes/anthracenes 25.8 13.6 1.4 36.5 6.5 10.6 4.4 sum-naphthalenes(N) 10.9 49.6 0.7 103,6-dimethylphenanthrene nd nd nd 1.8 0.2 nd 0.3 sum-fluorenes(F) 11.2 nd 0.7 1C3-phenanthrenes/anthracenes 64.0 6.8 7.2 61.9 21.9 57.4 5.7 sum-phenanthrenes/anthracenes(PA) 200.3 62.8 26.0 26C4-phenanthrenes/anthracenes 13.5 nd 1.4 24.3 5.9 6.2 2.1 sum-dibenzothiophenes(D) 12.8 nd 0.6 12,3-benzofluorene 1.4 nd nd 1.3 nd nd nd sum-fluoranthenes/pyrenes(FP) 18.5 8.0 0.9 21,1 ́-binaphthalene nd nd nd nd nd nd nd sum-chrysenes(C) 15.4 16.8 0.4 2dibenzothiophene** 2.2 nd 0.2 2.8 0.5 0.3 nd sum-C20H12 aromatics(C20) 32.5 35.2 0.7 3C1-dibenzothiophenes** 3.2 nd 0.3 3.5 0.7 0.6 0.4 sum- 4,5 PAH (4,5 PAH) 62.9 64.7 1.9 7C2-dibenzothiophenes** 3.2 nd 0.1 2.8 0.8 0.4 0.3 sum-PAH(t-PAH) 303.7 181.9 30.3 49C3-dibenzothiophenes** 4.3 nd tr 2.1 0.9 1.4 0.5 N/PA 0.05 0.79 0.03 C4-dibenzothiophenes** nd nd nd nd tr nd 0.5 N/perylene 0.57 1.70 na fluoranthene 2.4 2.6 0.2 3.2 1.8 0.7 2.5 F/perylene 0.58 nd na pyrene 4.3 4.5 0.3 4.9 2.0 0.8 2.0 PA/perylene 10.45 2.15 na 1C1-fluoranthenes/pyrenes 4.5 0.9 0.1 5.6 1.0 0.7 0.9 FP/perylene 0.96 0.27 na C2-fluoranthenes/pyrenes 3.7 nd 0.3 5.2 0.8 1.2 1.1 t-PAH/perylene 15.84 6.22 na 2

116
117

Table 9. (continued)

Sample ID – UCLA No. Pr. Blk X- SPIKE 1 X- SPIKE 2 X- SPIKE Avg Ref Sed SRM 1941† NIST Values† Sample ID – UCLA No. Pr. Blk X- SPIKE 1 X- SPIKE 2 

S

Surrogate Recovery (%) PAH (ng g–1 dry wt) (cont.)hexamethylbenzene 40 55 55 55 43 pyrene nd 78 86 n-dodecylbenzene 65 71 61 66 64 C1-fluoranthenes/pyrenes nd 4-terphenyl-D14 75 83 93 88 67 C2-fluoranthenes/pyrenes nd 

C3-fluoranthenes/pyrenes nd 

PAH (ng g–1 dry wt) C4-fluoranthenes/pyrenes nd naphthalene nd 34 30 32 1230 1322±14 benz(a)anthracene nd 59 63 C1-naphthalenes nd 43 chrysene/triphenylene nd 88 84 2-methylnaphthalene nd 44 40 42 336 406±36 C1-chrysenes/triphenylenes nd 1-methylnaphthalene nd 43 45 44 214 229±19 C2-chrysenes/triphenylenes nd C2-naphthalenes nd 49 C3-chrysenes/triphenylenes nd 2,6-dimethylnaphthalene nd 50 48 49 237 198±23 C4-chrysenes/triphenylenes nd C3-naphthalenes nd 57 benzo(k)fluoranthene nd 61 67 2,3,5-trimethylnaphthalene nd 59 55 57 87 96.3 benzo(b)fluoranthene nd 74 74 C4-naphthalenes nd 57 benzo(e)pyrene nd 68 60 biphenyl nd 57 65 61 104 115±15 benzo(a)pyrene nd 56 62 acenaphthylene nd 58 60 59 106 115±10 9,10-diphenylanthracene nd 77 74 acenaphthene nd 57 53 55 46 52±2 perylene nd 68 62 fluorene nd 57 59 58 88 104±5 indeno(1,2,3-cd)pyrene nd 63 64 2-methylfluorene nd 80 76 78 61 73.7 dibenz(a,h)anthracene nd 62 60 C1-fluorenes nd 78 picene nd 69 71 C2-fluorenes nd 75 benzo(ghi)perylene nd 62 66 C3-fluorenes nd 75 anthanthrene nd 52 56 phenanthrene nd 81 79 77 551 577±59 coronene nd 70 70 1-methylphenanthrene nd 80 76 78 92 109±6 1,2,4,5-dibenzopyrene nd 58 66 anthracene nd 78 78 78 171 202±42 C1-C20H12 aromatics nd C1-phenanthrenes/anthracenes nd 78 C2-C20H12 aromatics nd C2-phenanthrenes/anthracenes nd 75 C3-C20H12 aromatics nd 3,6-dimethylphenanthrene nd 79 71 75 56 78.0 C4-C20H12 aromatics nd C3-phenanthrenes/anthracenes nd 75C4-phenanthrenes/anthracenes nd 752,3-benzofluorene nd 76 84 80 139 124.81,1 ́-binaphthalene nd 70 82 76 98 117.0dibenzothiophene** nd 35 41 38 163 209.8C1-dibenzothiophenes** nd 78C2-dibenzothiophenes** nd 75C3-dibenzothiophenes** nd 75C4-dibenzothiophenes** nd 75fluoranthene nd 80 88 84 1132 1220±240

* Duplicate analysis

** Very low recovery due to activated copper treatment 

# OS=oil spill sample; note change in units of concent

nd=not detected, below MDLtr=trace amounts, not quantifiablena=not applicable

% recovery of some methylated homologs assumed to b

methylated phenanthrenes

117
118

Table 10. Distribution (ng g–1 dry wt) of triterpenoids in gross sediments of Beaufort Lagoon. All

sample numbers have the prefix BL03. O. is the oil spill sample.

SAMPLE ID – UCLA No.

Triterpanes (ng g–1 dry wt)* 1 2 3 4 5B 6B 9 9B 10 11A

18(H),21(H)-22,29,30-trisnorhopane tr# tr tr tr tr 2 1 1 1 1

17 (H),21 (H)-22,29,30-trisnorhopane 9 tr 6 tr tr 6431 3

17 (H),21 (H)-22,29,30-trisnorhopane 54 20 51 1 24 51 55 25 14 27

17 (H),18(H),21 (H)-28,30-bisnorhopane nd## nd nd nd nd nd nd nd nd nd

17 (H),21 (H)-30-norhopane 16 10 20 1 11 22 18 11 2 13

17 (H),21(H)-30-norhopane 32 12 30 1 15 32 28 15 8 17

17 (H),21 (H)-30-norhopane** 138 37 111 2 61 125 128 39 33 39

18 (H)-oleanane nd nd nd nd nd nd nd nd nd nd

17 (H),21 (H)-hopane 8 5 11 1 5 13 7 5 3 7

17 (H),21(H)-hopane tr 9 nd tr 16 tr tr 27 tr nd

17 (H),21 (H)-hopane 14 6 14 nd 7 14 12 6 3 8

22S-17 (H),21 (H)-30-homohopane nd nd nd nd nd nd nd nd nd nd

22R-17 (H),21 (H)-30-homohopane tr tr tr nd nd 15 tr nd nd nd

17 (H),21 (H)-30-homohopane 10 3 13 tr 6 13 8 6 2 5

22S-17 (H),21 (H)-30,31-bishomohopane nd nd nd nd nd nd nd nd nd nd

22R-17 (H),21 (H)-30,31-bishomohopane nd nd tr nd nd nd nd nd nd nd

22S-17 (H),21 (H)-30,31-trishomohopane nd nd nd nd nd nd nd nd nd nd

22R-17 (H),21 (H)-30,31-trishomohopane nd nd nd nd nd nd nd nd nd nd

22S-17 (H),21 (H)-30,31-tetrahomohopane nd nd nd nd nd nd nd nd nd nd

22R-17 (H),21 (H)-30,31-tetrahomohopane nd nd nd nd nd nd nd nd nd nd

hop-13(18)-ene 32 13 25 1 14 26 40 27 8 4

hop-21(22)-ene nd nd nd nd nd nd nd nd nd nd

diploptene 260 112 182 3 148 254 181 173 93 116

SAMPLE ID – UCLA No.

Triterpanes (ng g–1 dry wt)* 12 13 14 15 16 17 18 19 20

OS

g g–1

dry wt

18(H),21(H)-22,29,30-trisnorhopane 1 3 1 3 nd nd 1 1 nd 56

17 (H),21 (H)-22,29,30-trisnorhopane 6 8 5 4 13 nd 5 2 1 57

17 (H),21 (H)-22,29,30-trisnorhopane 44 32 36 16 202 nd 46 18 7 nd

17 (H),18(H),21 (H)-28,30-bisnorhopane nd nd nd nd nd nd nd nd nd nd

17 (H),21 (H)-30-norhopane 24 23 23 15 57 1 19 8 2 315

17 (H),21(H)-30-norhopane 24 17 18 11 118 1 26 11 5 30

17 (H),21 (H)-30-norhopane** 87 70 83 29 398 1 107 45 13 56

18 (H)-oleanane nd nd nd nd nd nd nd nd nd nd

17 (H),21 (H)-hopane 11 16 9 9 18 nd 8 3 1 263

17 (H),21(H)-hopane tr nd nd 12 nd nd nd nd nd nd

17 (H),21 (H)-hopane 13 11 10 2 44 nd 13 4 2 nd

22S-17 (H),21 (H)-30-homohopane nd nd nd nd nd nd nd nd nd 103

22R-17 (H),21 (H)-30-homohopane nd 6 nd nd nd nd nd 1 nd 68

17 (H),21 (H)-30-homohopane 11 6 9 3 11 nd 11 3 2 nd

22S-17 (H),21 (H)-30,31-bishomohopane nd nd nd nd nd nd nd nd nd 44

22R-17 (H),21 (H)-30,31-bishomohopane nd nd nd nd nd nd nd nd nd 31

22S-17 (H),21 (H)-30,31-trishomohopane nd nd nd nd nd nd nd nd nd 27

22R-17 (H),21 (H)-30,31-trishomohopane nd nd nd nd nd nd nd nd nd 7

22S-17 (H),21 (H)-30,31-tetrahomohopane nd nd nd nd nd nd nd nd nd 7

22R-17 (H),21 (H)-30,31-tetrahomohopane nd nd nd nd nd nd nd nd nd Tr

hop-13(18)-ene 32 30 18 15 53 nd 21 9 2 ?

hop-21(22)-ene nd nd nd nd nd nd nd nd nd nd

diploptene 352 211 314 143 903 1 184 64 28 nd

* Quantification based on m/z 191; also note change in the units for OS sample

** May be coeluting with C30 hopane in some samples # Trace amounts, not quantifiable ## Not detected, below detection limits
119

Table 11. Distribution (ng g–1 dry wt) of steroids in gross sediments of Beaufort Lagoon. All sample

numbers have the prefix BL03. OS is the oil spill sample.

SAMPLE ID – UCLA No.

Steranes (ng g–1 dry wt)* 1 2 3 4 5B 6B 9 9B 10 11A

5(H)androstane nd## nd nd nd nd nd nd nd nd nd

5(H)pregnane nd nd 1.4 0.1 nd nd nd nd nd nd

5(H)pregnane nd 0.7 1.6 0.1 0.7 2.8 1.1 nd nd nd

20-methyl-5(H)pregnane nd nd nd 0.2 0.4 1.0 nd nd nd nd

20S-13(H),17(H)-diacholestane** S1# nd nd nd 0.3 0.8 3.7 1.0 0.9 nd 1.3

20R-13(H),17(H)-diacholestane S2 nd nd nd 0.2 0.7 2.3 0.7 0.9 nd nd

20S-5(H),14(H),17(H)-cholestane S3 nd nd nd 0.2 0.8 2.6 1.1 1.0 nd 1.2

20R-5(H),14(H),17(H)-cholestane S6 nd nd nd 0.2 0.8 2.6 0.8 nd nd nd

20R-5(H),14(H),17(H)-cholestane S4 nd nd nd 0.4 2.8 6.7 1.8 nd nd nd

20S-5(H),14(H),17(H)-cholestane S5 nd nd nd 0.2 1.5 5.2 nd nd nd 3.3

20R-24-ethyl-13(H),17(H)-diacholestane S7 nd nd nd 0.2 1.2 3.2 1.8 nd nd nd

20S-5(H),14(H),17(H)-ergostane S8 nd nd nd nd nd nd nd nd 1.3 nd

20R-5(H),14(H),17(H)-ergostane S11 nd nd nd 0.2 nd nd 2.5 nd nd nd

20R-5(H),14(H),17(H)-ergostane S9 nd nd nd nd nd nd nd nd nd nd

20S-5(H),14(H),17(H)-ergostane S10 nd nd nd 0.2 nd nd nd nd nd nd

20S-5(H),14(H),17(H)-stigmastane S12 nd nd nd 0.2 nd nd nd nd nd nd

20R-5(H),14(H),17(H)-stigmastane S15 7.5 6.9 5.4 0.1 3.1 3.2 1.6 1.4 2.0 1.1

20R-5(H),14(H),17(H)-stigmastane S13 nd nd nd 0.3 0.8 2.3 1.5 nd 1.1 nd

20S-5(H),14(H),17(H)-stigmastane S14 nd nd nd nd nd nd nd nd nd nd

SAMPLE ID – UCLA No.

Steranes (ng g–1 dry wt)* 12 13 14 15 16 17 18 19 20

OS

g g–1

dry wt

5(H)androstane nd nd nd nd nd nd nd nd nd 8

5(H)pregnane nd 1.5 0.8 nd nd nd nd nd nd 8

5(H)pregnane 1.4 4.2 1.9 2.1 nd 0.1 nd 0.7 nd 11

20-methyl-5(H)pregnane 0.9 1.7 1.4 nd nd 0.1 0.8 nd nd 4

20S-13(H),17(H)-diacholestane** S1# 1.7 4.4 2.0 3.1 nd 0.1 1.7 0.9 0.5 116

20R-13(H),17(H)-diacholestane S2 1.8 2.6 1.3 1.8 nd nd 1.0 0.6 0.2 92

20S-5(H),14(H),17(H)-cholestane S3 1.3 1.9 2.4 2.3 nd 0.1 1.5 0.8 nd 64

20R-5(H),14(H),17(H)-cholestane S6 1.7 2.9 1.7 nd nd nd 0.9 0.5 0.2 55

20R-5(H),14(H),17(H)-cholestane S4 4.2 6.6 3.4 nd nd nd 2.1 1.1 0.6 215

20S-5(H),14(H),17(H)-cholestane S5 2.3 3.9 1.8 nd nd nd 2.3 1.3 0.3 125

20R-24-ethyl-13(H),17(H)-diacholestane S7 1.0 5.0 1.5 nd nd nd 1.1 0.5 0.2 170

20S-5(H),14(H),17(H)-ergostane S8 nd 5.2 nd nd nd nd nd nd nd 37

20R-5(H),14(H),17(H)-ergostane S11 nd 7.5 1.3 nd nd nd 2.3 nd nd 126

20R-5(H),14(H),17(H)-ergostane S9 5.5 4.3 nd nd nd nd 1.9 nd nd 217

20S-5(H),14(H),17(H)-ergostane S10 nd 7.2 5.5 nd nd nd nd nd nd 63

20S-5(H),14(H),17(H)-stigmastane S12 nd 1.0 1.5 nd nd nd nd nd nd 69

20R-5(H),14(H),17(H)-stigmastane S15 3.7 5.4 13.0 8.7 38.2 0.1 2.1 1.4 1.2 26

20R-5(H),14(H),17(H)-stigmastane S13 3.2 3.0 2.4 nd nd nd 2.0 0.7 nd 141

20S-5(H),14(H),17(H)-stigmastane S14 nd nd nd nd nd nd nd nd nd 55

* Note change in units for OS sample

** Steranes of similar configuration assumed to have the same RF as in the standard compounds mixture

# Notation to be used in figures ## Not detected, below detection limits
120

Table 12. Correlation coefficients (r) for chemical and physical parameters of muds from Beaufort Lagoon (N = 19; 

correlations [p <0.05] are shown in bold and insignificant correlations are in gray).

V Cr Mn Ni Cu Zn As Cd Sn Ba Pb Fe OC % 

V 1.00Cr 0.89 1.00Mn 0.07 –0.07 1.00Ni 0.85 0.71 0.48 1.00Cu 0.80 0.64 0.39 0.91 1.00Zn 0.84 0.78 –0.16 0.69 0.76 1.00As 0.72 0.52 0.12 0.55 0.58 0.68 1.00Cd 0.41 0.54 0.30 0.57 0.45 0.45 0.31 1.00Sn 0.48 0.43 0.74 0.78 0.61 0.35 0.40 0.78 1.00Ba 0.53 0.57 –0.38 0.31 0.47 0.70 0.26 0.08 –0.12 1.00Pb 0.70 0.50 0.80 0.89 0.92 0.58 0.68 0.63 0.86 0.10 1.00Fe 0.73 0.85 0.28 0.58 0.62 0.77 0.71 0.59 0.50 0.48 0.70 1.00OC % 0.37 0.40 0.12 0.42 0.31 0.62 0.44 0.66 0.46 0.07 0.38 0.44 1.00Silt % –0.63 –0.40 –0.75 –0.81 –0.73 –0.51 –0.72 –0.73 –0.93 0.19 –0.88 –0.52 –0.44 Clay % 0.64 0.40 0.74 0.82 0.75 0.55 0.72 0.72 0.93 –0.17 0.88 0.52 0.58 

120
121

_

X

_

X

Table 13. Time-series changes in the mean concentrations (g g

–1 dry wt)

of trace metals in sediments of Beaufort Lagoon.a

Year V Cr Mn Ni Cu Zn

1977b

N=5

139 69 359 48 22 81

SD 17 12 69 6 4 11

CV% 12 17 19 13 18 14

2003c

N = 21

119 73 472 39 36 92

SD 13 7 659 7 9 17

CV% 11 10 139 18 25 19

a

Significant differences (p<0.05) in bold

b

Naidu [1981, 2003a]

c

This study (Table 2)

Discussion

Sediment grain size, and organic carbon and nitrogen and their isotope composition

Beaufort Lagoon has a mosaic of sediment types consisting of sandy muds to muddy sands with rare

gravel (Table 1). The exceptions are sediments 4B, 19 and 20, which have significant or predominant

amounts of gravel (Table 1). The mud fractions of sediments, which were sampled for trace metal,

OC, N and isotope analysis, generally contain relatively higher contents of silt than clay particles. In

the gross sediments, which were collected for THg analysis, there are wide differences in the content

of sand relative to mud. We discuss later the possible control of granulometry on the concentrations

of 12 metals in the mud fraction and THg in the gross sediments.

The contents of organic carbon and nitrogen (N) in the mud fraction of Beaufort Lagoon sediments

in this study (Table 1) are generally higher by a factor of 1.7 compared to the gross sediments from

the Simpson Lagoon–Prudhoe Bay region and by a factor of 2.9 compared to gross sediments

collected from Beaufort Lagoon in 1977 [Naidu 1981]. The higher content of OC in mud than in

gross sediments is not surprising, because OC (tied with organic grains) invariably is co-deposited

with hydraulically similar finer silt and clay (mud) particles; also, clays concentrated in mud have a

greater ability to adsorb organics. Coarser particles such as sand in gross sediments are generally not

associated with organic particles of similar size and, therefore, their presence in gross sediments tends

to dilute the OC contents.

The OC/N ratios and compositions of the stable isotopes of carbon (13C) and nitrogen (15N) in

Beaufort Lagoon sediments (Table 1) indicate that all of the samples consist predominantly of

OC/organic matter derived from terrestrial C3 plant sources, with minor input from marine or

macrophyte sources. This interpretation is based on conclusions from very exhaustive investigations

relating to the isotopic and OC/N signatures of the end-member sources of OC/organic matter

(terrestrial, marine phytoplankton, sea ice algae and marine macrophytes, kelp) in the coastal region

of the Alaskan and Canadian Beaufort Sea [Naidu et al. 2000; Macdonald et al. 2004]. Results of our
122

studies on the sources of OC/organic matter in Beaufort Lagoon sediments are consistent with those

reported for the above areas and, as discussed later, with conclusions on the sources of organics in

the lagoon sediments based on the hydrocarbon composition. It is suggested that the high input of

terrestrial OC/organic matter to Beaufort Lagoon, as well as lagoons of the contiguous coastal region,

is due to the large supply of sediments derived from the peat-rich shoreline, which has one of the

world’s highest coastal erosion rates, 2–10 m y–1, [ Naidu et al. 2000; Macdonald et al. 2004 and

references therein].

Trace metal studies

QA/QC

The results of the calibration verification procedure (Table 3) include, for each metal, the known

initial calibration verification (true value, ICV-TV), the ICV-observed value (ICV-Obs), and the ICV

recovery value (ICV-Rec). ICV-Obs indicated that the percentage ICV-Rec for all metals except Fe

was at an acceptable level (close to 100%). Likewise, the blank analysis (Table 4), based on an initial

calibration blank (ICB) and the continuation calibration blank (CCB), indicates, for several runs, very

low contamination from the chemicals used throughout the analysis. Further, with reference to the

use of spikes (Table 5) run in duplicates, and based on the values of the mean concentration of an

individual metal via replicate analyses of a sample (Mean), spike true value (Spike TV), and the

observed spike value (Obs Spike Value), we show that the spike percent recovery is close to 100%

for all metals except Fe and Ba. The reason for the consistent poor showing for Fe and Ba is

unknown, but perhaps reflects the inherent limitation of the use of the ICP/MS technique. The

analytical precision (Table 6), as suggested by the relative percent difference (RPD) on duplicate

analytical runs of two sediment samples, would seem to be excellent for all metals except THg, in

which case it is very good and at acceptable level (<25%). Likewise, the analytical accuracy (Table

7), determined via the certified values (Cert Value), and analyzed or observed values (Obs Value) of

two certified reference materials (CRM – NIST 2709 and IAEA 405) are again at accepted levels for

all metals with reference to both the standards, except for Cr and Cd for NIST 2709 and Fe and Sn for

both of the CRMs. The above discussion indicates that the QA/QC for the trace metal analysis is

generally very good to excellent and our data are of high quality.

Statistical analysis on the trace metal is in progress. Here we report the preliminary results of the

analysis. The high % CV about the mean for all the 13 metals (Table 2) obviously indicates that there

are wide inter-sample variations in the concentrations of all the metals, with relatively low variability,

though, for V, Cr and Fe. The reason for the high inter-sample variability in the THg content may

be ascribed to the wide differences in sand content in the gross sediments; the silt and clay content

within the mud fraction may account for variability in the rest of the metals. The other possible

reason could be the differences in the contents of organic carbon (a measure of organic matter)

between sediments. Yet another reason could be the differences in the Mn and Fe content between

the sediments analyzed. It is to be expected that the greater the amount of clay size particles, Mn

and Fe content, and/or larger content of organic carbon (and by implication organic matter) in the

mud fraction or gross sediments there will be relatively greater concentrations of metals. This

concentration could result from several likely processes, such as adsorption of metals on clays,

formation of organic–metal complexes by ligand bonding, and/or co-precipitation of metals with Fe

and Mn oxyhydroxides. That such a granulometric control on metal content is present in the case of

Beaufort Lagoon muds is demonstrated by the significant (p <.05) positive correlations between all

of trace metals, except Cr and Ba, and the clay content. The positive significant correlations observed

between all the trace metals and Fe, suggests that Fe, as an oxyhydroxide, has an important role in

scavenging and accumulating metals in the mud of Beaufort Lagoon. In this context, it is to be noted

that all of the 2–5-cm surficial sediments (portions analyzed for trace metals) showed at the time of
123

collection a brownish ochre coloration distinct from the underlying gray layers—an indication that Fe

in the mud analyzed is most likely present in a highly oxidized state. Additional detailed

investigations on metal partitioning in sediments from Simpson Lagoon (located east of the Colville

Delta) and Beaufort Lagoon, based on sequential extraction techniques, further substantiate the role

of Fe oxyhydroxides in sequestering metals in the north Alaskan arctic sediments [Sweeney 1984;

Sweeney and Naidu 1989]. Likewise, organic carbon and Mn in the muds (Table 12) to some extent

bond a selected few metals (Zn, Cd, As and Sn by OC, and Ni, Sn and Pb by Mn).This interpretation

is consistent with our earlier conclusions on partitioning of trace metals on lagoon sediments from a

wide region off of the North Slope coast, namely from the Colville–Canning Delta area [Naidu et al.

2001 and references therein] and also from Beaufort Lagoon [Naidu et al. 2003a].

Results of further exploratory statistical analysis of trace metal concentrations in Beaufort Lagoon

consisted of the following: We examined dot plots of metal concentrations by station and identified

two (4 and 5) with unusually low concentrations of a number of metals, including V, Zn, Fe, Ni, As,

Cr, and Cu. In contrast, station 17 was characterized by unusually high concentrations of Sn, Mn, Ni,

Pb and Cu. Histograms, box-and-whisker plots, and normal probability plots suggested that most

metal concentrations were approximately normally distributed but that several extreme outliers were

present, including high concentrations of Mn, Sn, and Pb at station 17, as well as high concentrations

of Sn at 19 and 20. Other outliers included the low concentration of Cr at stations 4 and 5. Because

of the presence of a number of outliers, we supplemented the above mentioned correlation coefficient

analysis (Table 12) with rank-based correlations (Spearman’s rank correlation, r) to examine

relationships among various metals in the Beaufort Lagoon samples. The Spearman’s rank correlation

analysis showed very strong positive correlations among most metals (r = 0.58 to 0.91, p <0.01).

However, concentrations of Mn and Ba were only weakly correlated with each other and with any of

the other metals, except for a relatively strong correlation between Ba and Zn (r = 0.604, p = 0.005).

These results are generally consistent with those shown in the initial correlation coefficient analysis

(Table 12).

Multivariate analyses of metal concentrations in mud across all locations, including a cluster analysis

and a principal components (PC) analysis, confirmed that metal concentrations at stations 4, 5, and

17 were most different from each other and from any of the other stations. A cluster analysis showed

that the remaining stations form three relatively distinct groups (Figure 2), but these groups did not

correspond to geographically distinct regions. Because of the strong correlations among metal

concentrations, 80% of the variability in these correlations can be summarized by only two principal

components. A plot of the stations in the space of the first two components also shows the unusual

characteristics of stations 4, 5, and 17 (Figure 3). The first PC had positive loadings for all metals,

implying a general increase in metal concentrations along the x-axis in Figure 2. This PC was

negatively correlated with latitude (r = –0.531, p = 0.023). This implies that the trend of metal

concentrations decreases from south to north across the sampling area. It seems clear, therefore,

that there are no definite distributional patterns in the concentrations of trace metals in the mud of

Beaufort Lagoon, especially in context of the hypothesis that we had enunciated at the start of the

project: We had hypothesized that there will be significant differences in the metal concentrations

between the regions exposed to natural oil seeps, anthropogenic activities such as those related to the

now abandoned DEW Line station, and pristine environment portions of the lagoon. It is suggested

that any input of the 13 metals investigated which might have been introduced into the lagoon from

the oil seeps and/or past military activities are dispersed so widely and diluted that the affect of the

point sources of the natural and anthropogenic contaminants are not decipherable in the lagoon mud.

In summary, there is no significant difference between mud collected from possible impacted areas

and the pristine portion of the lagoon, suggesting that the lagoon has remained uncontaminated as far

as the trace metals analyzed in this study.
124

Figure 2. Dendrogram based on Ward’s linkage clustering (Euclidean distances)

and on concentrations of 13 metals measured at each station.

Figure 3. Beaufort Lagoon stations in the space of the first two principal

components (PC).
125

Tentative examination of the data suggests that the mean concentrations of all trace metals, except

THg, in the muds of Beaufort Lagoon are generally at the same level as those we reported from the

Colville Delta–Prudhoe Bay–Canning Delta region. The significantly higher THg values in Beaufort

Lagoon mud (mean = 57 ng g–1) than in the above region (mean = 19 ng g–1) are probably due to

differences in the natural input of the metal from the hinterland source, a proposition that remains

to be further investigated.

Time-series comparison of the mean concentrations of metals

Table 13 shows a comparison of the mean concentrations of six selected metals between 5 sandy mud

(>75% silt plus clay) and 21 mud samples which were collected from Beaufort Lagoon in 1977 and

for this study. A significant (p <.05) decrease in V and an increase in Mn and Cu are noted from

1977 to 2003, whereas no differences in the time-series mean concentrations in Cr, Ni and Zn are

identified. The differences may be artifacts of the variations in the granulometry and organic carbon

contents between the two sets of samples rather than metal pollution, as alluded to previously. It is to

be noted that we have minimized the affect of granulometry by restricting the comparison to mean

metal values on sandy mud samples from 1977 and mud samples in this study. The fact that there is

no wholesale increase in the mean concentrations of all or most of the metals concerned is consistent

with our earlier inference that Beaufort Lagoon has remained generally a clean environment over the

26-year period (1977–2003), despite its having been subjected to past anthropogenic activities from

the operation of the Dew Line station.

Hydrocarbon studies

The discussions on the hydrocarbon studies are focused on two aspects: one to elucidate the sources

of the hydrocarbons, and the second to assess whether the types and concentrations of hydrocarbons

in Beaufort Lagoon sediments are any way different than those in nearshore sediments collected

elsewhere in the North Slope region.

Criteria to infer hydrocarbon sources

We assessed the relative abundance of the various sources of hydrocarbons (natural oil seep, refined

petroleum, fresh crude and natural terrestrial and marine biogenic origin) in each of the sediments

analyzed by using, for example, the following guidelines.

Natural crude seepage: Weathered petroleum; alkane gas chromatogram with a hump (unresolved

complex mixture of branched and cyclic components) [Farrington and Tripp 1977; Simoneit and

Kaplan 1980; Venkatesan et al. 1980].

Fresh petroleum: Unweathered petroleum; characterized by n-alkane distribution with no odd/even

carbon preference throughout the carbon number envelope [Philp 1985); alkylcyclohexanes and

alkylbenzenes are found at significant levels, pristine and phytane are usually more dominant than

C17 and C18 n-alkanes [Zafiriou et al. 1972], triterpenoids are of high thermal maturity characterized

by the presence of predominantly the 17-hopanes and moretanes (17-hopanes) [Dastillung and

Albrecht 1976]. Alkylated PAHs are more dominant than the parent PAHs [Youngblood and Blumer

1975].

Biogenic hydrocarbons: Alkane gas chromatogram normally has baseline resolved peaks and does

not have a hump [Venkatesan and Kaplan 1982]; C15, C17 dominant n-alkanes from marine plankton,

whereas dominance of C25 to C31 n-alkanes indicates terrestrial plant wax, with C25, C27, C29, C31

and C33 more dominant than the even carbon n-alkanes (resulting in high odd/even ratio) and with

maximum at C29 or C31 n-alkane [Simoneit and Kaplan 1980; Venkatesan et al. 1980]; presence of a

significant level of alkanes from marine biota [Blumer et al. 1971]; triterpenoids are of low thermal
126

maturity characterized by the presence of predominantly the 17-hopanes and hopanes [Dastillung

and Albrecht 1976]; not many alkylated PAHs.

Characteristics of hydrocarbons and their sources

Alkanes: The alkane gas chromatogram exhibits baseline separation of the components and is

generally bimodal with the major maximum at n-C27 in 14 of the 20 samples analyzed and at n-C29 or

n-C31 in the remaining sediments (Table 8). Normal alkanes >C25 predominate in all of the sediment

samples with an odd/even carbon alkane ratio in the range from 3.5 to 8.1. These alkanes are derived

from higher plants. The secondary maximum at n-C17 is characteristic of aquatic algae. The C20 and

C21 olefins found are derived from plankton and bacteria. Although the total n-alkane level varies

from 0.1 to 34 g g

–1 similar to the Elson Lagoon sediments, Beaufort Lagoon sediments, in general,

seem to exhibit relatively greater vascular inputs than those of Elson Lagoon.

In contrast, the oil seep (OS) sample contains a 100- to 1000-fold concentration of n-alkanes

compared to the sediments (i.e., g g

–1

 vs. ng g

–1 as found in sediments, Table 8). Very high amounts

of n-C10 through n-C15 and significant levels of alkanes from n-C20 to n-C32 over a pronounced

unresolved hump (unresolved complex mixture spanning n-C21–n-C34) are found in this sample. The

most dominant alkane is n-C11. There is practically no single dominant n-alkane at the high molecular

weight end where the odd/even ratio is at 1.3, typical of petroleum input. From the overall profile of

the n-alkanes, it appears that OS may be degraded petroleum.

In addition, the L/H ratio (C12–C19/C20–C33 alkanes) of OS is 0.6 which is much higher than in

sediments (0.05–0.13) as computed from Table 10. Only one sample (14) has measurable pristane

and phytane and OS has neither of the two. The C20 and C21 planktonic olefins, which are found in

all of the sediments, are absent in the OS sample. Therefore, the n-alkane distribution in the sediment

samples is markedly different from that in the OS sample, implying that the lagoon sediment is

relatively pristine with no measurable petroleum contribution.

Polycyclic aromatic hydrocarbons: The levels of total PAH in the Beaufort Lagoon sediments

range from 30 to 710 ng g–1 (Table 9) and are comparable to sediments from the Colville Delta

–Prudhoe Bay–Canning Delta region (2001), Elson Lagoon [Naidu et al. 2003b] and other nearshore

regions of the Beaufort Sea. The PAH composition is dominated by the homologous series of

phenanthrenes. The sum of parent PAHs and their methyl homolog distribution follows generally

in the order: phenanthrenes>naphthaleneschrysenes/triphenylenes>fluoranthenes/pyrenes.

C2-naphthalene and C1-phenanthrene in all of the samples and C1-chrysene in four samples are the

most dominant PAH homologs. The general dominance of parent and monomethylated PAHs over

higher methylated homologs in the lagoon sediment samples suggests the absence of significant

petroleum input in the sediments. Compared to the lower molecular weight PAHs, 4- and 5-ring

PAHs are relatively less and perylene is the most dominant parent PAH in all of the samples except

for 17, which has the least PAH content and contains no perylene. The dominance of perylene in

the sediments is consistent with its origin in Alaskan peats similar to that found in coastal and other

lagoon sediments of north arctic Alaska [Naidu et al. 2001, 2003b and references therein]. This

interpretation is consistent with the very light (negative) values of carbon isotope ratios ( 13C‰,

Table 1) of all sediments, which point to a predominantly terrestrial (C3) plant source of OC/organic

matter in Beaufort Lagoon.

The OS sample contains 208 g g

–1 of total PAHs, about 1000-fold greater than the Beaufort Lagoon

sediments (Table 9), which is also consistent with the trend in n-alkane content. Unlike the sediments,

naphthalenes are the most dominant PAHs in OS, and similar to the dominance of low molecular

weight C10–C15 n-alkanes over high molecular weight analogs. Further, an anomalously high value of
127

the naphthalene/phenanthrene ratio in OS is observed (7.62) in contrast to the very low range in the

sediments (0.03–0.91). C2-naphthalenes, C2-phenanthrenes and C3-chrysene/triphenylenes are the

most dominant methyl homologs, characteristic of petroleum. Neither any PAHs beyond C4-chrysene/

triphenylenes nor perylene are detected in the oil seep. The sum of parent PAH and methyl homologs

follow the order: naphthalenes>>>phenanthrenes~chrysenes/triphenylenes>fluoranthenes/pyrenes.

The overall molecular composition of PAHs in Beaufort Lagoon sediments and a comparison to that

of the OS sample imply absence of petroleum in the Beaufort Lagoon sediments, which is consistent

with that noted above from the alkane profiles. The hydrocarbon composition of the sediments from

Beaufort Lagoon is comparable to those from Elson Lagoon and most of the Beaufort Sea sediments

we investigated in previous CMI/MMS studies.

Triterpenoids: Triterpanes are mostly biogenic in the sediments as reflected by the presence of 5

major components, i.e., 27(17)-, 29-, 29-, 30-hopanes and diploptene (Table 10). The most

dominant hopanoid is diploptene and 29-hopane is the second most dominant. Thermally mature

29- and 30-hopanes are present in much smaller/trace amounts relative to biogenic hopanes.

Extended hopanes with C31 are either not detected or present only in trace quantities and only the

R isomer is detected.

Similar to n-alkanes, total triterpanes are at the g g

–1 level and about 1000-fold greater in the OS

sample than in the lagoon sediment samples where they occur at the level of ng g–1 (Table 10).

Further, OS contains a whole suite of thermally mature -hopanes and none of the biogenic -

and -hopanes or the hopenes. Both S and R diastereomers of the extended hopanes (i.e.,

C31-hopanes) are present. This hopane distribution in OS is typical of petroleum/thermally mature

sediments.

The trace amounts of isolated thermally mature (and/or -hopane) biomarkers detected in the

sediments most probably derive from peat and/or coal. The overall fingerprint of the triterpanes in

sediments of Beaufort Lagoon do not support any contribution from the oil seep.

Steranes: Steranes are generally absent in Beaufort Lagoon sediment samples. If present, they are

only in small/trace amounts, usually about 10 times lower than triterpanes (Table 11). Sediment

samples 12, 13 and 14 contain almost a complete suite of target steranes and at relatively higher

levels than the other sediments. This probably implies minimal petroleum input. However, this

observation is not supported by triterpane or n-alkane profiles. Also, considering the extremely low

levels of steranes in these samples, the possible input from petroleum may not be warranted. The

other samples which contain smaller amounts of a few steranes are 6B, 18 and 19.

All of the target steranes are present in the OS sample and it is about 1000-fold greater than in the

lagoon sediments. The sterane profile of OS clearly indicates petroleum characteristics.

In summary, normal and cyclic alkane distribution is characteristic of biogenic origin in all of the

sediment samples and is different from that of the OS sample, thus indicating very little or no

petroleum input to Beaufort Lagoon sediments. The overall profile of alkanes in the Beaufort Lagoon

sediments is similar to that of nearshore sediments from the Beaufort Sea which we analyzed in

previous CMI/MMS projects [Naidu et al. 2001, 2003b]. The alkane distribution of the OS sample,

in contrast, clearly reflects that of petroleum. The overall profiles of its hydrocarbons indicate a

significant derivation from terrestrial plants, which is corroborated by the OC/N and stable carbon

isotope ratios. In fact, terrestrial organic material pervades (50%) the organic matter of sediments

throughout the Alaskan–Canadian nearshore, which is substantiated by more extensive investigations

on OC/N, 13C and n-alkanes of.sediments of the region [Naidu et al. 2000; Macdonald et al. 2004].
128

Study Products

Naidu, A.S., J.J. Kelley and J.J. Goering. 2003. Three decades of investigations on heavy metals

in coastal sediments, North Arctic Alaska: A synthesis. J. Phys. IV France 107:913–916.

Kelley, J.J., A.S. Naidu and J.J. Goering. 2004. Trace metals in sediments of Elson Lagoon

(Northwest Arctic Alaska) as related to the Prudhoe Bay industrial region, p. 175–179 (abstract).

In Proc. 19th Int. Symp. on Okhotsk Sea and Sea Ice, Mombetsu, Hokkaido, Japan. Oral

presentation by J.J. Kelley.

Naidu, A.S. and J.J. Kelley and D. Misra. 2004. Heavy metal monitoring in sediments of the North

Alaskan Arctic. Asia Oceania Geosciences Society (AOGS) First Annual Meeting, Singapore,

July, 2004. Abstract 57-OOA-A447. Oral presentation (invited) by A.S. Naidu.

Naidu, A.S., J.J. Kelley, D. Misra and M.I. Venkatesan. 2004. Trace Metals and Hydrocarbons in

Sediments of Beaufort Lagoon, Northeast Arctic Alaska, Exposed to Long-term Natural Oil

Seepage, Recent Anthropogenic Activities and Pristine Conditions. Oral Presentation by A.S.

Naidu at the University of Alaska Coastal Marine Institute Annual Research Review, February

2003, Fairbanks.

Naidu, A.S., J.J. Kelley, D. Misra and J.J. Goering. 2004. Responsible industrial development in the

North Slope nearshore: sediment trace metal history. Luncheon talk given by A. S. Naidu at the

Alaska Miners Association, Fairbanks.

Naidu, A.S., J.J. Kelley and J.J. Goering. 2005. Heavy Metals, p. 851–852. In Mark Nuttall [ed.],

Encyclopedia of the Arctic, Volume 2. Routledge, New York.

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Blumer, M., R.R.L. Guillard and T. Chase. 1971. Hydrocarbons of marine phytoplankton. Mar.

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Dastillung, M., and P. Albrecht. 1976. Molecular test for oil pollution in surface sediments. Mar.

Poll. Bull. 7(1):13–15.

Douglas, D.C., P.E. Reynolds and E.B. Rhode [eds.]. 2002. Arctic Refuge Coastal Plain Terrestrial

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Geol. Survey, Reston, VA, 75 p.

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Folk, R. 1968. Petrology of Sedimentary Rocks. Hemphill Publishing Co., Austin, Texas, 70 p.

Macdonald, R.W., A.S. Naidu, M.B. Yunker and C. Gobeil. 2004. The Beaufort Sea: Distribution,

sources, fluxes, and burial rates of organic carbon, p. 177–192. In R. Stein and R.W. Macdonald

[eds.], The Organic Carbon Cycle in the Arctic Ocean. Springer, Berlin/New York.
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Naidu, A.S. 1981. Sources, Transport Pathways, Depositional Sites and Dynamics of Sediments in

the Lagoon and Adjacent Shallow Marine Region, Northern Arctic Alaska. Annual Report to

NOAA–OCSEAP Office, Boulder, Colorado, 39 p.

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Alaska, p. 311–321. In SCOPE/UNEP Sonderband Heft 58, Mitt. Geol-Paläont. Inst., Univ.

Hamburg, Germany.

Naidu, A.S., L.W. Cooper, B.P. Finney, R.W. Macdonald, C. Alexander and I.P. Semiletov. 2000.

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p. 522–532. In R. Stein [ed.], Circum-Arctic River Discharge and Its Geological Record, Int.

J. Earth Sci., Spec. Issue, Vol. 89.

Naidu, A.S., J.J. Goering, J.J. Kelley and M.I. Venkatesan. 2001. Historical Changes in Trace

Metals and Hydrocarbons in the Inner Shelf, Beaufort Sea: Prior and Subsequent to PetroleumRelated Industrial Developments. Final Report. OCS Study MMS 2001-061, University of Alaska

Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 80 p.

Naidu, A.S., J.J. Kelley and J.J. Goering. 2003a. Three decades of investigations on heavy metals in

coastal sediments, North Arctic Alaska: A synthesis. J. Phys. IV France 107:913–916.

Naidu, A.S., J.J. Kelley, J.J. Goering and M.I. Venkatesan. 2003b. Trace Metals and Hydrocarbons

in Sediments of Elson Lagoon (Barrow, Northwest Arctic Alaska) as Related to the Prudhoe Bay

Industrial Region. Final Report. OCS Study MMS 2003-057, University of Alaska Coastal

Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 33 p.

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Report for SOHIO Alaska Petroleum Co., Anchorage, 329 p.

Philp, R.P. 1985. Fossil Fuel Biomarkers: Applications and Spectra. Methods in Geochemistry and

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131

New Projects

Six new projects are being funded this federal fiscal year along with the ongoing projects reported

above. Preliminary reports (Quakenbush & Small [TO 35248] and Powell [TO 35269]) and abstracts

(Wang & Jin [TO 35262, TO 35407], Foster, Lees & Saupe [TO 37357] and Okkonen, Pegau and

Saupe [TO 37628]) are presented here to show the full range of work being supported by the

University of Alaska Coastal Marine Institute.
132

Satellite Tracking of Bowhead Whales:

The Planning Process

Lori T. Quakenbush <lori_quakenbush@fishgame.state.ak.us>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775–7220

Robert J. Small <bob_small@fishgame.state.ak.us>

Marine Mammal Program

Alaska Department of Fish and Game

Division of Wildlife Conservation

P.O. Box 25526

Juneau, AK 99802-5526

Task Order 35248

Abstract

Bowhead whales (Balaena mysticetus) are an important subsistence and cultural resource for

coastal people of northern Alaska. They migrate through the Beaufort Sea twice annually: during

their eastward, spring migration and their westward, fall migration. Oil and gas leasing, exploration,

development, and production are ongoing in the Alaskan Beaufort Sea and an understanding of

bowhead migration and feeding behavior in this area is important for the conservation and

management of the species. Satellite transmitters placed on bowhead whales near Kaktovik in fall

and near Barrow in spring would provide information on migration routes, migration timing, swim

speed, diving behavior, residence times in portions of their range, and incidental exposure to industry

activity. The objectives of this phase of the project are to accomplish the preliminary work necessary

to determine whether the whaling captains and commissioners of the Alaska Eskimo Whaling

Commission are supportive of a satellite tagging study and if they are interested in participating in

the study design and tag deployment. If so, we will work cooperatively with the whaling captains of

Barrow and Kaktovik, the commissioners of the Alaska Eskimo Whaling Commission, the North Slope

Borough, the National Marine Fisheries Service, the Minerals Management Service, the oil and gas

industry, and the Alaska Department of Fish and Game to determine the best available tag, tag

deployment method, and study design, and pursue the funding to subsequently deploy the tags and

conduct the study. The study will be designed in a way that does not interfere with subsistence

whaling activities.

Introduction

Bowhead whales are the most important species for the subsistence communities along the Beaufort

Sea coast both for the amount of nutrition and for their cultural importance. Subsistence whaling

communities are concerned that offshore and nearshore oil and gas activities (e.g., marine seismic
133

projects, offshore oil drilling from ships and islands) may deflect whales away from shore, making

hunting more difficult and dangerous and possibly displacing whales from feeding areas during the

short open water season. Oil spills during whale migrations are also of concern.

The importance of the Beaufort Sea as feeding habitat to bowhead whales is unclear. An analysis of

stable isotope ratios in the baleen of adult bowhead whales showed little change in their isotope ratios

seasonally, indicating that the major signal came from feeding in the Bering and Chukchi seas in

winter [Schell et al. 1989; Hobson and Schell 1998; Schell and Saupe 1993], not from feeding in the

Beaufort Sea in summer/fall. Baleen from subadult bowheads, however, showed strong seasonal

changes between summer (Beaufort) and winter (Bering) feeding, indicating significant feeding in

both places. A more recent study [Hoekstra et al. 2002] analyzed isotope ratios in bowhead muscle

instead of baleen and found seasonal fluctuation in 13C for all age classes, suggesting that the Bering

and Beaufort seas are both important for feeding.

Within the Beaufort Sea, the eastern Canadian Beaufort Sea and Amundsen Gulf regions are thought

to be used as summer feeding areas for bowhead whales [Fraker and Bockstoce 1980; Würsig et al.

1985], and although feeding has been observed in the Alaskan Beaufort Sea [Ljungblad et al. 1983,

1986; Lowry and Frost 1984; Moore et al. 1989] there is less agreement regarding its relative

importance. Richardson [1987] concluded that the bowhead population obtained <1% and <1.4%

of its annual energy needs in the Alaskan Beaufort Sea between Kaktovik and Canada in 1985 and

1986, respectively. Another feeding study conducted in 1998–2000 [Richardson and Thomson 2001]

concluded that although bowheads in the Alaskan Beaufort Sea spent an average of 47% of their time

feeding, they did not obtain more than 5% of their annual energy needs there. Aerial photography and

resightings of identifiable individuals was the primary method used to determine residence time for

whales in the study area, but this method may have underestimated the amount of time and/or the

number of whales feeding there. A significant amount of feeding may also be occurring during

behavior that has been recorded as migration [Ljungblad et al. 1986].

Over 75% of 132 bowhead whales harvested for subsistence at Barrow and Kaktovik (1976–2000)

were feeding prior to their death [Lowry and Sheffield 2002]. Subadult bowheads harvested in fall

are heavier and have blubber with higher lipid content than in the spring [Thomson 2002]. Stable

isotopes, behavioral observations, stomach contents, and lipid levels indicate bowhead whales are

feeding in the eastern and western areas of the Beaufort Sea, but further investigation is needed to

determine the importance of the region to their annual energy requirements.

Satellite telemetry is a powerful tool that can be used to address questions regarding marine mammal

habitat use [Boyd et al. 2002; Baumgartner and Mate 2001a, b; Lowry et al. 2000]. Detailed

movements of individual whales can determine residence time in potential feeding areas. Satellite

transmitters can collect and transmit diving information to provide behavior-at-location data that

can be correlated with feeding. Some satellite telemetry work has been done with bowhead whales

[Mate et al. 2000; Heide-Jørgensen et al. 2003] and the existing satellite transmitter technology is

well suited for use with bowheads, but consistency in deployment and retention time of transmitters

have been less than ideal [Mate et al. 2000] until recently. Two of the issues include: 1) positioning

the transmitter on the whale so that the transmitter comes out of the water frequently to maximize

transmissions to the satellite for good location data, and 2) retention of the tag on the whale due to

the design of the attachment and the physiological processes of healing in the whale.

Our objectives during the planning phase are to: 1) seek approval for this study from the whaling

community; 2) determine the level at which the whalers would like to be involved in the study;

3) provide a forum for collaboration with whaling captains, the Alaska Eskimo Whaling Commission

(AEWC), the North Slope Borough (NSB), National Marine Fisheries Service (NMFS), Minerals
134

Management Service (MMS), Alaska Department of Fish and Game (ADF&G) and the oil and gas

industry; 4) design a satellite telemetry study; and 5) identify funding for the study. Because of the

importance of bowhead whales for subsistence, their endangered species status, and the interest in

their summer habitat for oil and gas exploration and development, coordination with the whalers, the

agencies involved in marine mammal management, and the oil and gas industry will be necessary for

a successful project.

Results

Lori Quakenbush coordinated with NSB staff Craig George and Harry Brower, and NSB director

Charles Brower and AEWC executive director Maggie Ahmaogak, for an opportunity to present the

satellite tagging study idea to a meeting of the AEWC commissioners in Barrow 24 June 2004. Many

of the commissioners were present in Barrow, others attended via telephone, and some were absent.

The presentation occurred at the end of a long meeting and there were numerous questions. It was

suggested that a presentation and discussion be placed on the agenda of the next quarterly AEWC

meeting so that all of the commissioners could participate in person in a discussion. This meeting

will occur on 27 October 2004 in Anchorage.

If the meeting in October concludes with approval to conduct the study, we discussed the best time

and place for a workshop on its design. Maggie Ahmoagak felt that having a workshop in conjunction

with their annual AEWC mini-convention in Anchorage in February 2005 would be best. The

commissioners would be there for the AEWC meeting, and representatives from the agencies and

oil industry could easily attend.

We are researching the existing tags, deployment methods, retention times, and health effects on the

whales in order to choose the tag and method that will accomplish the study objectives with the least

impact to the whales. We have collected relevant literature, contacted scientists actively tagging

large whales and continue to monitor the progress of tagged whales. Several researchers have made

advances recently but have not published their results. For example, recent tags placed on blue whales

(Balaenoptera musculus) and sperm whales (Physeter macrocephalus) have lasted more than 300

days (B. Mate, unpubl. data). Tags were placed on two Pacific right whales for the first time this

summer, and while both are working only one is providing locations (P. Wade, pers. comm.). Because

of the need for better deployment and attachment systems, NMFS has announced their interest in

holding a workshop for researchers involved in tagging large whales to discuss problems and recent

developments. No suitable date for the workshop has been identified.

Discussion

Although our original plan was to have meetings in Kaktovik to talk with the whaling captains, the

meetings in Barrow and Anchorage will allow collaboration with all of the AEWC commissioners

from all of the whaling villages. A workshop in Anchorage with all of the AEWC commissioners

will also be ideal for participation of all interested parties.

Acknowledgements

The University of Alaska Coastal Marine Institute (Minerals Management Service) funded this

project with support provided by the North Slope Borough, Department of Wildlife Management.
135

Charlie Brower, Craig George, Harry Brower, and Robert Suydam of the North Slope Borough

have provided helpful comments and ideas. We thank Maggie Ahmoagak of the AEWC for her ideas,

assistance, and the opportunities to address the commissioners.

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right whale movements and habitat characteristics from satellite-monitored radio tag data: A

novel approach. 14th Biennial Conference on the Biology of Marine Mammals. Vancouver,

Canada, 28 November–3 December 2001 (Abstract).

Baumgartner, M.F., and B.R. Mate. 2001b. Summer feeding season movements and fall migration

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Beaufort Sea. Mar. Fish. Rev. 42(9–10):57–64.

Heide-Jørgensen, M.P., K.L. Laidre, Ø. Wiig, M.V. Jensen, L. Dueck, L.D. Maiers, H.C. Schmidt

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Balaena mysticetus, across Baffin Bay. Arctic 56(1):21–31.

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Lowry, L.F., and K.J. Frost. 1984. Foods and feeding of bowhead whales in western and northern

Alaska. Scientific Reports of the Whales Research Institute, Tokyo 35:1–16.

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Beaufort Sea. Chapter 18. In W.J. Richardson and D.H. Thomson [eds.], Bowhead Whale

Feeding in the Eastern Alaskan Beaufort Sea: Update of Scientific and Traditional Information,

Vol. 2. OCS Study MMS 2002-012. LGL Report TA2196-7. Report from LGL Ltd., King City,

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Mate, B.R., G.K, Krutzikowsky and M.H. Winsor. 2000. Satellite-monitored movements of radiotagged bowhead whales in the Beaufort and Chukchi seas during the late-summer feeding season

and fall migration. Can. J. Zool. 78(7):1168–1181.

Moore, S.E., J.T. Clarke and D.K. Ljungblad. 1989. Bowhead whale (Balaena mysticetus) spatial

and temporal distribution in the central Beaufort Sea during late summer and early fall 1979–86.

Report to the International Whaling Commission 39:283–290.

Richardson, W.J. [ed.]. 1987. Importance of the eastern Alaskan Beaufort Sea to feeding bowhead

whales, 1985–1986. Report by LGL Ecological Research Associates Inc. to U.S. Minerals

Management Service, NTIS No. PB 88 150271/AS, 131 p.

Richardson, W.J., and D.H. Thomson [eds.]. 2001. Bowhead whale feeding in the eastern Alaskan

Beaufort Sea: Update of scientific and traditional information. 2 Vols. Draft final report by LGL

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U.S. Minerals Management Service MMS# 1435-01-97-/CT-30842.

Schell, D.M., and S.M. Saupe. 1993. Feeding and growth as indicated by stable isotopes, p.

491–509. In J.J. Burns, J.J. Montague and C.J. Cowles [eds.], The Bowhead Whale. Special

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Schell, D.M., S.M. Saupe and N. Haubenstock. 1989. Bowhead whale (Balaena mysticetus) growth

and feeding as estimated by 13C techniques. Mar. Biol. (Berl.) 103:433–443.

Thomson, D.H. 2002. Energetics of bowhead whales. Chapter 22. In W.J. Richardson and D.H.

Thomson [eds.], Bowhead Whale Feeding in the Eastern Alaskan Beaufort Sea: Update of

Scientific and Traditional Information, Vol. 2. OCS Study MMS 2002-012. LGL Report

TA2196-7. Report from LGL Ltd., King City, Ontario Canada to U.S. Minerals Management

Service Anchorage.

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137

Workshop on Hydrological Modeling for Freshwater

Discharge from the Alaska Arctic Coast

Jia Wang <jwang@iarc.uaf.edu>

Meibing Jin <ffjm@uaf.edu>

International Arctic Research Center–Frontier Research System for Global Change

University of Alaska Fairbanks

Fairbanks, AK 99775-7340

Task Order 35262

Abstract

Although there have been digital elevation data collected with 1-km or finer spatial resolution from

the North Slope and six hydrological gauges north of the Brooks Range, there has not yet been a high

resolution digital elevation model (DEM)-based hydrological model constructed for the region to

calculate the freshwater discharge into the Arctic Ocean from the Alaskan Arctic coast. Freshwater

discharge is important locally because it controls breakup of nearshore landfast ice and the release

of spilled oil from landfast ice, and defines the water mass properties and the density-driven current

of the nearshore shelf. Furthermore, we do not know what portion of the the discharge goes

ungauged because it is due to snow melt, small creeks, streams and ungauged rivers.

We propose to hold a workshop on hydrological modeling of freshwater runoff in the North Slope

region. The workshop will focus on precedents in data processing, hydrological modeling, and field

observations, including needs, scientific and economic issues, and possible solutions in the region.

The workshop will be designed to include scientists from the University of Alaska Fairbanks (UAF),

the University of Washington (UW), the University of New Hampshire (UNH), the U.S. Geological

Survey (USGS), the State of Alaska, the North Slope Borough, and the Alaska Native communities.
138

Pre-Migratory Movements and Physiology of Shorebirds

Staging on Alaska’s North Slope

Abby N. Powell <ffanp@uaf.edu>

Audrey R. Taylor <ftart@uaf.edu>

Institute of Arctic Biology

University of Alaska Fairbanks

Fairbanks, AK 99775–7000

Richard B. Lanctot <richard_lanctot@fws.gov>

U.S. Fish and Wildlife Service

Migratory Bird Management

1011 East Tudor Road, MS 201

Anchorage, AK 99503

Task Order 35269

Abstract

Preliminary work conducted in the 1970s in Barrow, Alaska, indicated that arctic littoral habitats

were of critical importance for most arctic-breeding shorebirds during the staging period (prior to

southbound migration to wintering areas). However, no information exists to quantify pre-migratory

shorebird use of Alaska’s North Slope or what factors may influence site use. This information is

critical given increased levels of human activity and development across the arctic plain. This project

was initiated to gain a better understanding of the basic biology of post-breeding shorebirds during

the staging period, and to aid in assessing how future industrial and human activity across the North

Slope may affect shorebird populations. Our proposal to the Coastal Marine Institute involved

studying staging shorebird populations on the slope using a two-level approach, which includes:

• An intensive analysis of staging behavior characteristics and factors influencing

shorebird choice of staging sites in Barrow, Alaska, from 2004 to 2006.

• An extensive aerial survey in 2005 to determine how pre-migratory shorebirds

are distributed across selected staging areas on the North Slope.

Research during the summer of 2004 investigated the location of staging sites within the Barrow

vicinity, the phenology of staging among different shorebird species using the Barrow area prior to

fall migration, and the residency time of individual birds captured during the staging period. We also

collected data on shorebird density along historical transects last surveyed for staging shorebirds

during the Outer Continental Shelf Environmental Assessment Program in the late 1970s. We found

that numbers of birds at prominent staging locations and on the historical transects varied widely

throughout the staging period. Adult shorebirds were recorded in the study area through July and the

first few days of August; thereafter all birds seen in Barrow were juveniles (with the exception of

adult dunlin). Staging shorebirds generally used areas of saline tundra, the shores of small brackish

ponds and lagoons, and the Barrow sewage lagoon as foraging habitats. We captured 204 birds

during August 2004 and recorded 182 resightings of banded or painted birds within the study area.

The longest residency time recorded for a painted bird was 17 days (red phalarope); one radio-
139

tagged dunlin was recorded in the Barrow area for 13 days. In addition, individuals from four species

of shorebirds (red phalaropes [Phalaropus fulicaria], semipalmated sandpipers [Calidris pusilla],

western sandpipers [Calidris mauri], and dunlin [Calidris alpina]) captured during the staging period

were blood-sampled to compare fattening rates and stress levels among species and staging sites

around Barrow.

Introduction

The possible impacts of current and future energy development and climate change around the North

Slope are of concern to scientists, managers, and many citizens of the United States. Pre-migratory

staging shorebirds may be particularly susceptible to the effects of these environmental changes

because they are concentrated in coastal areas during a critical period in their life cycles. For

example, a study of shorebirds staging at the Colville River Delta on the North Slope estimated

that 41,000 individuals might use the delta prior to and during the fall migration [Andres 1994].

Insufficient information exists to determine how habitat alterations and disturbance may affect

shorebird populations across much of Alaska [Brown et al. 2001]. In particular, little is known about

shorebird use of littoral zones along the North Slope during the pre-migratory staging period. It is

likely that staging shorebirds depend on food resources found in coastal areas to acquire fat necessary

for southward migration. As pressure for energy development and potential for accelerated climate

change increase along the North Slope, so does the need for understanding the importance of coastal

areas in preparing shorebirds for southbound migration. If shorebirds are concentrated in and

depend on littoral zones at this critical period in their life cycle, the potential for climate change or

disturbance and contamination from development to impact a large segment of a species’ population

is considerable. Shoreline oiling from offshore spills could directly affect staging shorebirds by oiling

their plumages, or indirectly by contaminating or killing invertebrate food sources [Andres 1994]. In

addition, construction and maintenance of industrial development could negatively impact shorebirds

by causing them to flee from noise or human presence, or may eliminate important staging habitats

entirely. Of all waterbirds at Jamaica Bay National Wildlife Refuge in New York, shorebirds

appeared most susceptible to human-induced disturbance [Burger 1981]. Based on this evidence, it

seems plausible that the effects of energy development and associated human disturbance on staging

shorebirds could be detrimental to populations that already appear to be in decline [Brown et al.

2001]. Accelerated climate change may add to the effects of development and disturbance by

changing the spatial or temporal availability of littoral habitats suitable for staging shorebirds. A

mechanistic understanding of shorebird distribution and habitat use will help to pinpoint where and

when these impacts may have the largest effect and allow proactive rather than reactive management.

This study will help determine how pre-migratory shorebirds are distributed in littoral zones in

Barrow and around the North Slope, what habitats are particularly valuable during this period, and

how species composition and abundance at various staging sites may have changed between the

1970s and the present. It will also lend insight into what role physiological factors play in staging site

choice. This research is warranted because many North American species of shorebirds appear to be

declining in abundance [Brown et al. 2001]. This research will supply basic knowledge regarding

staging requirements and habitat for several species of arctic-breeding shorebirds in order to facilitate

an evaluation of the potential effects of development along the Arctic Coastal Plain.
140

Objectives

The specific objectives of this research are to:

1. Assess the abundance, species composition, and distribution of shorebirds

staging along selected North Slope coastlines prior to the fall migration.

2. Quantify habitat use characteristics of staging shorebirds, timing of arrival after

breeding, and residency times at staging sites.

3. Compare current densities of staging shorebirds with those reported in Connors

et al. [1979, 1984] to determine if shorebird use of coastal habitats near Barrow

has changed between the 1970s and the present.

4. Examine physiological factors influencing shorebird use of staging areas. Are

there quantifiable differences in local site quality (in terms of fattening rates and

physiological stress levels) that may affect how and when shorebirds use staging

areas? Such knowledge will allow an assessment of the relative importance of

different staging areas in preparing shorebirds for migration south from Alaska.

Study Area

During 2004, our studies were conducted at Barrow, Alaska, and the surrounding coastal vicinity

(71.290°N, 156.788°W). Barrow is bordered by the Chukchi Sea on the western side and the

Beaufort Sea and Elson Lagoon on the northern and eastern sides. This area is characterized by

continuous tundra inland and gravel beaches along most shorelines. Also present are many brackish

water ponds and lagoons with mud or sand shorelines that are heavily utilized by staging shorebirds.

The Barrow human population numbers near 5000, and many people use coastal areas near the village

for hunting, fishing, and other recreational activities.

Methods

To address Objectives 1–3, we conducted shorebird surveys along historical transects and at

prominent staging locations within the Barrow vicinity in 2004. The historical transects were last

surveyed in the 1970s by Peter Connors and colleagues during the OCSEAP (Outer Continental Shelf

Environmental Assessment) Program. Ten of Connors’ 1-km long transects were reestablished this

summer and surveyed once every three days from 21 July to 5 September (Figure 1). Surveys were

conducted by walking the transect line from start to finish, and recording the following for each bird

or group of birds present: number of birds in the group, species, age, perpendicular distance from the

transect line, and location along the transect line. In addition, several prominent staging locations

(i.e., areas with large and reliable numbers of birds) were surveyed daily for species and numbers of

shorebirds present.
141

Figure 1. Map of historical shorebird abundance transects run in 1970s during the OCSEAP

program. Shaded transect names indicate those surveyed during 2004. Modified from

Connors et al. [1984].

To understand how long shorebirds stage in the Barrow area before either migrating south or

moving to other areas on the North Slope to stage (residency time; Objective 2), we captured staging

shorebirds between 4–29 August at various locations around Barrow using walk-in traps and mist

nets, and subsequently resighted them. Captured birds were banded and their heads or necks marked

with a unique color combination of non-toxic enamel paint according to the date they were captured

(Table 1). Resightings of painted or banded birds on transects or at prominent staging locations were

recorded daily; paint and color band combinations allowed us to determine the minimum time the bird

had stayed in Barrow. In addition, we placed 1.2-g radio transmitters (Holohil Systems Ltd.) on 15

birds (Table 2) and monitored their locations daily within the Barrow vicinity using an ATS radio

receiver and an H-antenna mounted to the front of an ATV. All radio frequencies were monitored

until the birds had not been heard within the study area for at least four days.
142

Table 1. Paint/band combinations and number of individuals captured by date in Barrow

during the staging period in 2004.

Date Paint Band Combination Number

Captured

4 Aug Red on head No color bands; metal only 4

5 Aug Yellow on head Yellow lower right 7

6 Aug Blue on head Dark blue lower right 27

7 Aug Green on head Dark green lower right 15

9 Aug Red + yellow on head Red, yellow lower right 8

11 Aug Red + green on head Red, dark green lower right 16

14 Aug Green + yellow on head Dark green, yellow lower right 5

15 Aug Blue + yellow on head Dark blue, yellow lower right 4

17 Aug Blue + red on head Dark blue, red lower right 22

19 Aug Blue + green on head Dark blue, dark green lower right 22

21 Aug Purple on head or neck Light green lower right 17

22 Aug Red around neck Red lower left 11

23 Aug Yellow around neck Yellow lower left 8

25 Aug Green around neck Dark green lower left 16

27 Aug Red + yellow around neck Red, yellow lower left 11

30 Aug Blue around neck Dark blue lower left 11

Table 2. VHF radio frequencies, species, date tagged, and date last heard for 15 birds radio

tagged in Barrow in 2004. REPH – red phalarope. SEPH – semipalmated

sandpiper, WESA – western sandpiper, DUNL – dunlin

Frequency Species Date Tagged Last Detected in

Barrow

Days Present

Post-Capture

165.004 REPH 11 Aug 04 11 Aug 04 <1

165.058 SESA 11 Aug 04 14 Aug 04 3

165.094 WESA 14 Aug 04 23 Aug 04 9

165.134 WESA 14 Aug 04 18 Aug 04 4

165.172 REPH 15 Aug 04 15 Aug 04 <1

165.206 REPH 15 Aug 04 17 Aug 04 2*

165.246 WESA 17 Aug 04 28 Aug 04 11

165.285 DUNL 17 Aug 04 29 Aug 04 12

165.322 DUNL 21 Aug 04 3 Sep 04 13

165.358 WESA 19 Aug 04 23 Aug 04 4

165.396 DUNL 21 Aug 04 21 Aug 04 <1

165.433 SESA 23 Aug 04 24 Aug 04 1

165.470 WESA 25 Aug 04 4 Sep 04 10

165.508 DUNL 25 Aug 04 31 Aug 04 6

165.543 DUNL 25 Aug 04 26 Aug 04 1

*Questionable detection on 17 Aug for this bird; otherwise was not detected after tagging
143

To address Objective 4, we collected up to 300 L of blood from each individual captured during the

staging period. To obtain a blood sample, we swabbed the brachial vein of each bird with isopropyl

alcohol to clear the feathers and then punctured the vein with a sterile 26-gauge needle. Blood was

collected in 75-L heparinized capillary tubes and blown into heparinized 1.6-mL microcentrofuge

tubes. After bleeding we held a small piece of cotton over the puncture site on the vein such that

bleeding ceased rapidly. The blood was subsequently centrifuged for 15 min and separated into

plasma samples for fat metabolite and stress hormone analysis. Plasma samples were frozen

immediately after separation. The bird handling and bleeding procedures were approved by the

Institutional Animal Care and Use Committee at the University of Alaska Fairbanks (Protocol

Number 04-31).

Results

Each of the historical transects was surveyed 16 times throughout the staging period. Shorebirds

were seen regularly on each transect but many transects did not support large numbers of birds. The

transect data will be compared to that collected in the 1970s and analyzed using the program Distance

to obtain the best estimate of shorebird abundance for each transect over time. Repeated surveys

of staging locations with regular concentrations of birds indicated shorebird numbers fluctuated

dramatically throughout the staging period (see example: Figure 2). Shorebird species present in

the largest numbers around Barrow included red and red-necked phalaropes (Phalaropus lobatus),

semipalmated sandpipers, western sandpipers, dunlin, and long-billed dowitchers (Limnodromus

scolopaceus). We collected data to determine when species were first observed at Barrow staging

areas and which habitats each used; these data will be collated later in tabular form. Generally, adult

shorebirds were found on the staging areas through the end of July; by early August only juveniles

were left in the Barrow vicinity. Staging habitats used by shorebirds in Barrow included saline tundra

interspersed with brackish water ponds, small ponds and lagoons regularly inundated with salt water,

and the Barrow sewage treatment lagoon. Gravel lagoon and ocean beaches were rarely used by

staging shorebirds. To assess whether bird movements are correlated with local wind speed and

direction, we will relate shorebird abundance on our surveys with daily weather and wind data for

Barrow.

We captured 204 individuals of eight species at five different locations between 4 August and 29

August 2004. Species captured included red and red-necked phalaropes, semipalmated sandpipers,

western sandpipers, dunlin, long-billed dowitchers, ruddy turnstones (Arenaria interpres), and

semipalmated plovers (Charadrius semipalmatus). We recorded 182 resightings of painted or banded

birds, including several adult dunlin that had been previously banded on the adjacent tundra area

during the breeding season by either the U.S. Fish and Wildlife Service (2003 or 2004) or by Japanese

researchers (2001 or 2003). The longest interval between banding and subsequent resighting was

17 days (red phalarope). The longest residency time of a radioed bird was 13 days (dunlin) and the

shortest residency times were <1 day (three radio-tagged red phalaropes were never heard after they

were banded and the radios attached). As phalaropes tend to be pelagic species, radios may have

been difficult to detect due to signal attenuation in salt water. We also recorded the location of each

resighting (for both paint/band resightings and radio-tagged birds) with a handheld GPS unit so that

movements away from the banding locations and within the Barrow study area can be determined.

Preliminary examination of the movement data indicates that marked or radio-tagged birds moved

widely around the study area on a daily basis. Additional analyses on site tenure and movements are

underway.
144

Figure 2. Red and red-necked phalarope numbers on Middle Salt Lagoon (Barrow sewage

treatment lagoon) during August 2004. This lagoon was one of the prominent

staging locations surveyed daily for shorebird abundance and species composition.

Blood was collected for stress hormone and fat metabolite analysis from individuals of four species

(sample size in parentheses): red phalaropes (n = 84), semipalmated sandpipers (n = 23), western

sandpipers (n = 40), and dunlin (n = 26). Audrey Taylor will run the fat metabolite analysis at Simon

Fraser University and the stress hormone analysis at UAF during the fall semester 2004.

Discussion

It is clear from the 2004 field season that a number of shorebirds use the Barrow area as staging

habitat prior to fall migration. However, use of the area by adult birds was limited, resulting in

primarily juvenile birds being present throughout August and September. This pattern differs from

that documented by Peter Connors in the 1970s in which he found adults present on the littoral

transects through mid-August [Connors and Risebrough 1977]. Further data collection is needed

to determine whether the staging of adults during early July is due to high predation on shorebird

nests during the 2004 breeding season (resulting in adult migration out of Barrow being early and

protracted as failed breeders trickled out of the area) or whether this is the contemporary pattern of
145

staging in our study area. If typical, this may mean that Barrow is a more important staging area for

juveniles than for adults of most shorebird species.

The amount of time that individuals staged within the study area varied widely, with resightings of

painted birds occurring anywhere from <1 to 17 days post-capture, and radioed birds remaining in

the area from 0 to 13 days post-capture. Residency time did not appear to be different for different

species, although further examination of the data is needed. One notable exception may be adult

dunlin: some uniquely color-banded individuals remained at Barrow staging areas for up to four

weeks at a time, well past the date when adults of other species appeared to have left the area. This

may be due to the timing of prebasic flight feather molt in adult dunlin, which begins with regrowth

of primaries on the breeding grounds and is completed prior to migration [Warnock and Gill 1996].

Dunlin undergoing the highly energetic molt process may be unable to gain enough mass to

successfully migrate, and may therefore choose to stay in Barrow to complete molt and fatten

prior to southbound migration.

Preliminary examination of the movement data indicates that marked or radio-tagged birds moved

regularly from banding locations to other nearby areas, suggesting that Barrow itself can be viewed

as one large staging area rather than as multiple small staging sites. Analysis of blood plasma fat

metabolites and stress hormone levels from birds at different banding locations should lend further

insight into movement of birds within the Barrow area.

Future Plans

We will continue to analyze and interpret data from the 2004 field season; this information will be

used to refine existing and suggest new objectives. Audrey Taylor will attend the International Wader

Study Group annual conference in November 2004, during which she will participate in a workshop

on estimating residency times for staging and migrating shorebirds. Logistical planning for the 2005

field season will begin this fall and continue into the spring of 2005. Current plans for 2005 include

aerial surveys across the North Slope from Kasegaluk Lagoon to Demarcation Bay to determine

where staging shorebirds are concentrated, and in what numbers and species composition. We also

plan to have four ground camps across the slope (in addition to the Barrow camp) in which personnel

will conduct ground counts to provide a species composition “correction factor” for the aerial

surveys, and to capture, bleed, and band shorebirds using the methods developed in Barrow in

2004. These data should provide a broader perspective of where and when shorebirds stage, their

movements, and how physiology affects where and for how long shorebirds use staging areas across

the North Slope.

Acknowledgements

This project would not be possible without financial support from the UA Coastal Marine Institute

and Minerals Management Service. We would also like to thank the staff of the Barrow Arctic

Science Consortium and the North Slope Borough Department of Wildlife Management (especially

Robert Suydam) for logistical support. Sigma Xi and the Arctic Audubon Society provided funding

for radio transmitters. Data collection was accomplished with much help from our field technicians

(Juliette Juillerat, Greg Norwood, Jennifer Selvidge, and Bob Wilkerson). We also gratefully

acknowledge Audrey Taylor’s graduate advisory committee at UAF (Tony Williams, Sasha Kitaysky,

and Falk Huettmann).
146

References

Andres, B.A. 1994. Coastal zone use by postbreeding shorebirds in northern Alaska. J. Wildlife

Manage. 58(2):206–213.

Brown, S., C. Hickey, B. Harrington and R. Gill [eds.]. 2001. United States Shorebird Conservation

Plan, Second Edition. Manomet Center for Conservation Sciences, Manomet, Massachusetts, 61 p.

Burger, J. 1981. The effect of human activity on birds at a coastal bay. Biological Conserv.

21(3):231–241.

Connors, P.G., C.S. Connors and K.G. Smith. 1984. Shorebird littoral zone ecology of the Alaskan

Beaufort Coast, p. 295–396. In Outer Continental Shelf Environmental Assessment Program.

Final Reports of Principal Investigators, Volume 23. NOAA, National Ocean Service, Office of

Ocean and Marine Assessment, Ocean Asessments Division, Alaska Office, Anchorage.

Connors, P.G., J.P. Myers and F.A. Pitelka. 1979. Seasonal habitat use by arctic Alaskan shorebirds,

p. 101–111. In F.A. Pitelka [ed.], Shorebirds in Marine Environments. Studies in Avian Biology –

Number 2. Cooper Ornithological Society.

Connors, P., and R.W. Risebrough. 1977. Shorebird dependence on arctic littoral habitats. Report

R.U. 172, p. 402–524. In Environmental Assessment of the Alaskan Continental Shelf, Annual

Reports of Principal Investigators for the year ending March 1977, Volume II: Receptors – Birds.

Outer Continental Shelf Environmental Assessment Program, NOAA, Boulder, Colorado.

Warnock, N.D., and R.E. Gill, Jr. 1996. Dunlin (Calidris alpina), No. 203. In A. Poole and F. Gill

[eds.], The Birds of North America, Volume 6. The Academy of Natural Sciences, Philadelphia

and The American Ornithologists’ Union, Washington, D.C., 24 p.
147

Sea Ice–Ocean–Oil Spill Modeling System (SIOMS)

for the Nearshore Beaufort and Chukchi Seas:

Improvement and Parameterization (Phase II)

Jia Wang <jwang@iarc.uaf.edu>

Meibing Jin <ffjm@uaf.edu>

International Arctic Research Center–Frontier Research System for Global Change

University of Alaska Fairbanks

Fairbanks, AK 99775-7340

Task Order 35407

Abstract

We established a nested, fine-resolution (3.4 km) coupled ice–ocean–oil spill modeling system during

2000–2003 (phase I) in the Beaufort and Chukchi Seas titled, “A Nowcast/Forcast Model for the

Beaufort Sea Ice–Ocean–Oil Spill System (NFM-BSIOS)”. This modeling system can capture some

important thermodynamical and dynamical features of both sea ice and the ocean which have been

validated by available observations. Furthermore, we have developed an oil spill model with ice

under the forcing of observed wind at the MET-stations sponsored by MMS [Wang et al. 2002, 2003].

Nevertheless, it cannot resolve the landfast ice as observed. Because the model minimum depth is

10 m, the landfast ice formation, extent, breakup anchoring and ridging cannot be studied. The

barrier islands are not included due to the 3.4-km resolution and 10-m depth cutoff. Sea ice categories

are not yet parameterized. Ocean tides are not included. Problems exist in the conjunction boundary

between the coarse- and fine-resolution models.

To further improve this coupled ice–ocean–oil spill modeling system, we propose to improve the Sea

Ice–Ocean–Oil Spill Modeling System (SIOMS) for the nearshore Beaufort and Chukchi Seas by

increasing the resolution to 1–3 km and the minimum depth to 5 m to include: 1) geographically—

the barrier islands, inlets, river mouths up to 5-m depth using the fine resolution bathymetry data

(ETOP30); 2) physically—landfast ice parameterization for seasonal variations including breakup,

freezing, extent, and anchoring; 3) for such a small-scale sea ice model (1–3 km), we first need to

implement the viscous–plastic (VP) or elastic–viscous–plastic (EVP rheology) sea ice model [Hunke

and Dukowicz 1997] to the ocean model in the same region; 4) then we need to parameterize the sea

ice thickness not only into several categories [Wang et al. 2002], but also to represent thin ice, new

ice, level ice, lead ice, rafted ice, rubble ice, and ridged ice [Haapala 2000]; 5) stretched grids will

be used to replace the nested grids to avoid a conjuntion boundary effect; and 6) furthermore, ocean

tides will be included in this SIOMS. Whether we add the anisotropic property into the model will

depend upon our simulation results compared to the observations and the availability of such a model

delivered by the MMS/NASA interagency project.

The goal is to establish the stand-alone SIOMS by adding these new features into the existing

modeling system, which was sponsored by MMS during 2000–2003, to better assess and predict

ice–ocean conditions and possible nearshore oil spill impacts on the surrounding environment.
148

Evaluating a Potential Relict Arctic Invertebrate and

Algal Community on the West Side of Cook Inlet

Nora R. Foster <swamprat@mosquitonet.com>

University of Alaska Museum of the North

University of Alaska Fairbanks

Fairbanks, AK 99775–6960

Dennis C. Lees <dennislees@earthlink.net>

Littoral Ecological & Environmental Services

1075 Urania Avenue

Leucadia, CA 92024

Susan M. Saupe <saupe@circac.org>

Cook Inlet Regional Citizens Advisory Council

910 Highland Avenue

Kenai, AK 99611-8033

Task Order 37357

Abstract

We propose to conduct an analysis of archived samples and specimens collected during intertidal

and subtidal surveys in Cook Inlet to evaluate whether they are relict arctic species. The proposed

study is based on previous work conducted on the lower west side of Cook Inlet when taxonomic

identifications of epifaunal invertebrates collected in the 1970s for the Outer Continental Shelf

Environmental Assessment Program bore a striking resemblance to species reported for the Alaskan

Arctic. Additional information provided by other historical invertebrate collections in the area

indicate that these west side species and assemblages more closely matched arctic species and

assemblages than those on Cook Inlet’s east side or in other areas of the Gulf of Alaska and this

study will further evaluate these historical specimens. Appropriate international taxonomic experts

for each group of species will be contracted to provide the level of detail required to assess the

potential for a relict arctic flora and fauna assemblage on Cook Inlet’s west side.
149

Seasonality of Boundary Conditions for Cook Inlet, Alaska

Stephen R. Okkonen <okkonen@alaska.net>

Institute of Marine Science

University of Alaska Fairbanks

Fairbanks, AK 99775–7220

W. Scott Pegau <scott_pegau@fishgame.state.ak.us>

Kachemak Bay Research Reserve

Alaska Department of Fish and Game

95 Sterling Highway, Suite 2

Homer, AK 99603

Susan M. Saupe <saupe@circac.org>

Cook Inlet Regional Citizens Advisory Council

910 Highland Avenue

Kenai, AK 99611-8033

Task Order 37628

Abstract

In order to improve our understanding of the physical environment of lower Cook Inlet, Alaska

and to augment recent MMS-funded observational and modeling efforts we propose to conduct

an oceanographic monitoring program to measure the seasonal changes in volume and property

fluxes at the inflow and outflow boundaries in lower Cook Inlet and the northern Gulf of Alaska

and to investigate the mechanism(s) influencing these fluxes. The proposed program will acquire

hydrographic and velocity measurements along transect lines crossing: 1) Kennedy Entrance and

Stevenson Entrance from Port Chatham to Shuyak Island (80 km), 2) Shelikof Strait from Shuyak

Island to Cape Douglas (65 km), 3) Cook Inlet from Red River to Anchor Point (55 km), 4) Kachemak

Bay from Barbara Point to Bluff Point (19 km), and 5) the Forelands from East Foreland to West

Foreland (16 km). These lines correspond to convenient geographical boundaries and encompass the

current OCS lease region. Each transect will consist of vertical casts at 4-km intervals to profile the

water column using a caged array of instruments to measure temperature, conductivity, and light

transmission. A towed ADCP will acquire absolute water column velocities along transects. Each

transect will be measured monthly from May through October and in March and December starting

in March 2004 and ending in October 2005.
150

Funding Summary

Student Support

The cooperative agreement that formed the University of Alaska Coastal Marine Institute stressed

the need to support education as well as research. The following student support information is

summarized from proposals and may not accurately reflect actual expenditures:

Funds from MMS Matching Funds

Fiscal Year 94

1 Ph.D. student 22,558 9,220

6 M.S. students 65,107 37,411

1 undergrad 4,270 0

Source Total $191,935 $146,631

Fiscal Year 95

4 Ph.D. students 53,061 9,523

8 M.S. students 90,367 64,380

5 undergrads 4,297 13,933

Source Total $147,725 $187,836

Fiscal Year 96

5 Ph.D. students 75,499 8,499

5 M.S. students 80,245 18,661

2 undergrads 4,644 0

Source Total $160,388 $127,160

Fiscal Year 97

2 Ph.D. students 37,714 0

2 M.S. students 22,798 0

2 undergrads 2,610 0

Source Total $163,122 $100,000

Fiscal Year 98

2 Ph.D. students 17,109 17,109

2 M.S. students 26,012 7,200

2 undergrads 0 2,548

Source Total $143,121 $126,857

Fiscal Year 99

6 Ph.D. students 66,750 38,073

4 M.S. students 31,650 8,730

4 undergrads 0 10,704

Source Total $198,400 $157,507

Fiscal Year 00

6 Ph.D. students 61,383 30,551

2 M.S. students 5,868 10,135

7 undergrads 0 21,299

Source Total $167,251 $161,985

Fiscal Year 01

2 Ph.D. students 19,159 22,019

1 M.S. student 0 5,800

3 undergrads 10,983 5,761

Source Total $130,142 $133,580

Fiscal Year 02

3 Ph.D. students 48,476 0

5 M.S. students 66,676 7,500

Source Total $115,152 $107,500

Fiscal Year 03

3 Ph.D. students 45,032 12,000

5 M.S. students 79,448 7,500

1 undergrad 1,349 0

Source Total $115,152 $1019,500

Fiscal Year 04

4 Ph.D. students 55,365 15,000

2 M.S. students 34,715 0

Source Total $90,080 $1015,000

Total to Date $1,033,145 $383,556
151

Total CMI Funding

The total MMS funding committed to CMI projects through federal fiscal year 2003 is approximately

$11 million. Since all CMI-funded projects require a one-to-one match with non-federal monies, total

CMI project commitments through fiscal year 2003 have totaled approximately $22 million.

Sources of Matching Funds

Matching for CMI-funded projects has come from a wide variety of sources. Identifying and verifying

match remains a major administrative challenge in the development of CMI proposals. In general,

match has been available to those investigators who expend the necessary extra effort to locate and

secure the support. The following partial list of fund matching participants demonstrates the breadth

of support for CMI-funded programs:

Afognak Native Corporation

Alaska Beluga Whale Committee

Alaska Department of Environmental Conservation (ADEC)

Alaska Department of Fish and Game (ADF&G)

ADF&G – Kachemak Bay Research Reserve

Alaska Department of Transportation and Public Facilities

Alaska Science and Technology Foundation

Alyeska Pipeline Service Company

Ben A. Thomas Logging Camp

BP Amoco

BP Exploration (Alaska) Inc.

Canadian Wildlife Service

CODAR Ocean Sensors

Cominco Alaska, Inc.

ConocoPhillips Alaska, Inc.

Cook Inlet Regional Citizens Advisory Council

Cook Inlet Spill Prevention & Response, Inc.

Department of Fisheries and Oceans Canada

Exxon Valdez Oil Spill Trustee Council

Frontier Geosciences, Inc.

Golden Plover Guiding Co.

Littoral Ecological & Environmental Services

Japanese Marine Science and Technology Center (JAMSTEC)
152

Kodiak Island Borough

North Slope Borough

Oil Spill Recovery Institute

Phillips Alaska, Inc.

Prince William Sound Aquaculture Corporation

Simon Fraser University

University of Alaska Anchorage

University of Alaska Fairbanks

College of Science, Engineering and Mathematics

Frontier Research System for Global Change, IARC

Institute of Arctic Biology

Institute of Marine Science

International Arctic Research Center (IARC)

School of Agriculture and Land Resources Management

School of Fisheries and Ocean Sciences

School of Management

School of Mineral Engineering

University of Alaska Museum

Wadati Fund

Water Research Center

University of Alaska Natural Resources Fund

University of Alaska Southeast

University of California, Los Angeles

University of Northern Iowa

University of Texas

Woods Hole Oceanographic Institution

Some of the CMI-funded projects are closely related to other federally-funded projects which cannot

be considered as match but nevertheless augment and expand the value of a CMI project. Related or

joint projects have been funded by the National Science Foundation, the Office of Naval Research,

the National Aeronautics and Space Administration, the U.S. Geological Survey, the National

Oceanographic and Atmospheric Administration including the National Marine Fisheries Service, and

the Alaska Sea Grant College Program.

A positive relationship has been fostered between MMS, the University of Alaska, and the State of

Alaska since the formation of CMI. Residents of Alaska, as well as the parties to the agreement,

benefit from the cooperative research that has been and continues to be funded through CMI.
153

University of Alaska CMI Publications

These publications may be obtained from CMI until supplies are exhausted. Reports marked with an

asterisk are no longer available in hard copy from CMI.

Contact information

e-mail: cmi@sfos.uaf.edu

phone: 907.474.1811

fax: 907.474.1188

postal: Coastal Marine Institute

School of Fisheries and Ocean Sciences

University of Alaska Fairbanks

Fairbanks, AK 99775-7220

ALEXANDER, V. (Director). 1995. University of Alaska Coastal Marine Institute Annual Report

No. 1. University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 16 p.

ALEXANDER, V. (Director). 1996. University of Alaska Coastal Marine Institute Annual Report

No. 2. OCS Study MMS 95-0057, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 122 p.

ALEXANDER, V. (Director). 1997. University of Alaska Coastal Marine Institute Annual Report

No. 3. OCS Study MMS 97-0001, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 191 p.

ALEXANDER, V. (Director). 1998. University of Alaska Coastal Marine Institute Annual Report

No. 4. OCS Study MMS 98-0005, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 81 p.

ALEXANDER, V. (Director). 1998. University of Alaska Coastal Marine Institute Annual Report

No. 5. OCS Study MMS 98-0062, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 72 p.

ALEXANDER, V. (Director). 2000. University of Alaska Coastal Marine Institute Annual Report

No. 6. OCS Study MMS 2000-046, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 86 p.

ALEXANDER, V. (Director). 2000. University of Alaska Coastal Marine Institute Annual Report

No. 7. OCS Study MMS 2000-070, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 92 p.

ALEXANDER, V. (Director). 2002. University of Alaska Coastal Marine Institute Annual Report

No. 8. OCS Study MMS 2002-001, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 109 p.
154

ALEXANDER, V. (Director). 2003. University of Alaska Coastal Marine Institute Annual Report

No. 9. OCS Study MMS 2003-003, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 108 p.

ALEXANDER, V. (Director). 2004. University of Alaska Coastal Marine Institute Annual Report

No. 10. OCS Study MMS 2004-002, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 119 p.

ALEXANDER, V. (Director). 2005. University of Alaska Coastal Marine Institute Annual Report

No. 11. OCS Study MMS 2005-055, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 157 p.

BRADDOCK, J.F., and Z. RICHTER 1998. Microbial Degradation of Aromatic Hydrocarbons in

Marine Sediments. Final Report. OCS Study MMS 97-0041, University of Alaska Coastal Marine

Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 82 p.

BRADDOCK, J.F., K.A. GANNON and B.T. RASLEY. 2004. Petroleum hydrocarbon–degrading

microbial communities in Beaufort–Chukchi Sea sediments. Final Report. OCS Study MMS

2004-061, University of Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI,

MMS, Alaska OCS Region, 38 p.

COOK, J.A., and G.H. JARRELL. 2002. The Alaska Frozen Tissue Collection: A Resource for

Marine Biotechnology, Phase II. Final Report. OCS Study MMS 2002-027, University of Alaska

Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 23 p.

COOK, J.A., G.H. JARRELL, A.M. RUNCK and J.R. DEMBOSKI. 1999. The Alaska Frozen Tissue

Collection and Associated Electronic Database: A Resource for Marine Biotechnology. Final

Report. OCS Study MMS 99-0008, University of Alaska Coastal Marine Institute, University of

Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 23 p.

DUESTERLOH, S., and T.C. SHIRLEY. 2004. The Role of Copepods in the Distribution of

Hydrocarbons: An Experimental Approach. Final Report. OCS Study MMS 2004-034, University

of Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 53 p.

DUFFY, L.K., R.T. BOWYER, D.D. ROBY and J.B. FARO. 1998. Intertidal Effects of Pollution:

Assessment of Top Trophic Level Predators as Bioindicators. Final Report. OCS Study MMS

97-0008, University of Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI,

MMS, Alaska OCS Region, 62 p.

HENRICHS, S.M., M. LUOMA and S. SMITH. 1997. A Study of the Adsorption of Aromatic

Hydrocarbons by Marine Sediments. Final Report. OCS Study MMS 97-0002, University of

Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 47 p.

HERRMANN, M., S.T. LEE, C. HAMEL, K.R. CRIDDLE, H.T. GEIER, J.A. GREENBERG and C.E. LEWIS.

2001. An Economic Assessment of the Sport Fisheries for Halibut, Chinook and Coho Salmon

in Lower and Central Cook Inlet. Final Report. OCS Study MMS 2000-061, University of Alaska

Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 135 p.
155

HIGHSMITH, R.C., S.M. SAUPE and A.L. BLANCHARD. 2001. Kachemak Bay Experimental and

Monitoring Studies: Recruitment, Succession, and Recovery in Seasonally Disturbed RockyIntertidal Habitat. Final Report. OCS Study MMS 2001-053, University of Alaska Coastal Marine

Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 66 p.

HOLLADAY, B.A., B.L. NORCROSS and A. BLANCHARD. 1999. A Limited Investigation into the

Relationship of Diet to the Habitat Preferences of Juvenile Flathead Sole. Final Report. OCS

Study MMS 99-0025, University of Alaska Coastal Marine Institute, University of Alaska Fairbanks

and USDOI, MMS, Alaska OCS Region, 27 p.

JOHNSON, M.A., and S.R. OKKONEN [eds.]. 2000. Proceedings Cook Inlet Oceanography

Workshop. November 1999, Kenai, AK. Final Report. OCS Study MMS 2000-043, University of

Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 118 p.

KLINE, T.C., JR., and J.J. GOERING. 1998. North Slope Amphidromy Assessment. Final Report.

OCS Study MMS 98-0006, University of Alaska Coastal Marine Institute, University of Alaska

Fairbanks and USDOI, MMS, Alaska OCS Region, 25 p.

NAIDU, A.S., J.J. GOERING, J.J. KELLEY and M.I. VENKATESAN. 2001. Historical Changes in

Trace Metals and Hydrocarbons in the Inner Shelf Sediments, Beaufort Sea: Prior and

Subsequent to Petroleum-Related Industrial Developments. Final Report. OCS Study MMS

2001-061, University of Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI,

MMS, Alaska OCS Region, 80 p.

NAIDU, A.S., J.J. KELLEY, J.J. GOERING and M.I. VENKATESAN. 2003. Trace Metals and

Hydrocarbons in Sediments of Elson Lagoon (Barrow, Northwest Arctic Alaska) as Related

to the Prudhoe Bay Industrial Region. Final Report. OCS Study MMS 2003-057, University of

Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 33 p.

NIEBAUER, H.J. 2000. Physical–Biological Numerical Modeling on Alaskan Arctic Shelves. Final

Report. OCS Study MMS 2000-041, University of Alaska Coastal Marine Institute, University of

Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 84 p.

*NORCROSS, B.L. 1996. Recruitment of Juvenile Flatfishes in Alaska: Habitat Preference near

Kodiak Island, Vol. I. Final Report. OCS Study MMS 96-0003, University of Alaska Coastal

Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 118 p.

*NORCROSS, B.L. 1996. Recruitment of Juvenile Flatfishes in Alaska: Habitat Preference near

Kodiak Island, Vol. II. Final Report, Appendices. OCS Study MMS 96-0003, University of Alaska

Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region,

118 p.

NORCROSS, B.L., B.A. HOLLADAY, A.A. ABOOKIRE and S.C. DRESSEL. 1998. Defining Habitats

for Juvenile Groundfishes in Southcentral Alaska, Vol. I. Final Report. OCS Study MMS 97-

0046, University of Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI,

MMS, Alaska OCS Region, 131 p.
156

NORCROSS, B.L., B.A. HOLLADAY, A.A. ABOOKIRE and S.C. DRESSEL. 1998. Defining Habitats

for Juvenile Groundfishes in Southcentral Alaska, Vol. II. Final Report, Appendices. OCS Study

MMS 97-0046, University of Alaska Coastal Marine Institute, University of Alaska Fairbanks and

USDOI, MMS, Alaska OCS Region, 127 p.

OKKONEN, S.R., and S.S. HOWELL. 2003. Measurements of Temperature, Salinity and

Circulation in Cook Inlet, Alaska. Final Report. OCS Study MMS 2003-036, University of Alaska

Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 28 p.

PROSHUTINSKY, A.Y. 2000. Wind Field Representations and Their Effect on Shelf Circulation

Models: A Case Study in the Chukchi Sea. Final Report. OCS Study MMS 2000-011, University of

Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 136 p.

PROSHUTINSKY, A.Y., M.A. JOHNSON, T.O. PROSHUTINSKY and J.A. MASLANIK. 2003. Beaufort

and Chukchi Sea Seasonal Variability for Two Arctic Climate States. Final Report. OCS Study

MMS 2003-024, University of Alaska Coastal Marine Institute, University of Alaska Fairbanks and

USDOI, MMS, Alaska OCS Region, 197 p.

SHAW, D.G., and J. TERSCHAK. 1998. Interaction Between Marine Humic Matter and Polycyclic

Aromatic Hydrocarbons in Lower Cook Inlet and Port Valdez, Alaska. Final Report. OCS Study

MMS 98-0033, University of Alaska Coastal Marine Institute, University of Alaska Fairbanks and

USDOI, MMS, Alaska OCS Region, 27 p.

SCHELL, D.M. 1998. Testing Conceptual Models of Marine Mammal Trophic Dynamics Using

Carbon and Nitrogen Stable Isotope Ratios. Final Report. OCS Study MMS 98-0031, University

of Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS

Region, 137 p.

TERSCHAK, J.A., S.M. HENRICHS and D.G. SHAW. 2004. Phenanthrene Adsorption and

Desorption by Melanoidins and Marine Sediment Humic Acids. Final Report. OCS Study MMS

2004-001, University of Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI,

MMS, Alaska OCS Region. 65 p.

TYLER, A.V., C.O. SWANTON and B.C. MCINTOSH. 2001. Feeding Ecology of Maturing Sockeye

Salmon (Oncorhynchus nerka) in Nearshore Waters of the Kodiak Archipelago. Final Report.

OCS Study MMS 2001-059, University of Alaska Coastal Marine Institute, University of Alaska

Fairbanks and USDOI, MMS, Alaska OCS Region, 34 p.

WANG, J. 2003. Proceedings of a Workshop on Small-Scale Sea-Ice and Ocean Modeling

(SIOM) in the Nearshore Beaufort and Chukchi Seas. Final Report. OCS Study MMS 2003-043,

University of Alaska Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS,

Alaska OCS Region, 56 p.

WEINGARTNER, T.J. 1998. Circulation on the North Central Chukchi Sea Shelf. Final Report.

OCS Study MMS 98-0026, University of Alaska Coastal Marine Institute, University of Alaska

Fairbanks and USDOI, MMS, Alaska OCS Region, 39 p.
157

WEINGARTNER, T.J., and S.R. OKKONEN. 2001. Beaufort Sea Nearshore Under-Ice Currents:

Science, Analysis and Logistics. Final Report. OCS Study MMS 2001-068, University of Alaska

Coastal Marine Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 22 p.

WEINGARTNER, T.J., and T. PROSHUTINSKY. 1998. Modeling the Circulation on the Chukchi Sea

Shelf. Final Report. OCS Study MMS 98-0017, University of Alaska Coastal Marine Institute,

University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 75 p.

WINKER, K., and D.A. ROCQUE. 2004. Seabird Samples as Resources for Marine Environmental

Assessment. Final Report. OCS Study MMS 2004-035, University of Alaska Coastal Marine

Institute, University of Alaska Fairbanks and USDOI, MMS, Alaska OCS Region, 26 p.