DEPARTMENT OF COMMERCE

UNITED STATES OF AMERICA

NOAA Coastal Ocean Program

Decision Analysis Series No. 24

Science for Solutions

The Southeast

Bering Sea Ecosystem:

Implications for Marine

Resource Management

Final Report: Southeast Bering Sea Carrying Capacity

June 2004

US Department of Commerce

National Oceanic and Atmospheric Administration

National Ocean Service

Center for Sponsored Coastal Ocean Research

Coastal Ocean Program
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Science for Solutions

NOAA COASTAL OCEAN PROGRAM

U.S. D EPARTMENT OF CO M MERCE

NAT OI NAL OCEANIC A ND ATMOSPH ERIC ADMINISTR ATIO

Decision Analysis Series No. 24 N

The Southeast Bering Sea Ecosystem: Implications for

Marine Resource Management

S. Allen Macklin1 and George L. Hunt, Jr.2 (Editors)

1National Oceanic and Atmospheric Administration

Pacific Marine Environmental Laboratory

7600 Sand Point Way NE

Seattle, WA 98115

2University of California Irvine

Department of Ecology and Evolutionary Biology

Irvine, CA 92697

June 2004

U.S. DEPARTMENT OF COMMERCE

Donald L. Evans, Secretary

National Oceanic and Atmospheric Administration

Conrad C. Lautenbacher, Jr., Under Secretary for Oceans and Atmosphere

National Ocean Service

Richard Spinrad, Assistant Administrator

Center for Sponsored Coastal Ocean Research/Coastal Ocean Program

Robert Magnien, Director
This publication should be cited as:

Macklin, S.A., and G.L. Hunt, Jr. (Editors), 2004. The Southeast Bering Sea Ecosystem: Implications for Marine Resource Management (Final Report: Southeast Bering Sea Carrying Capacity).

NOAA Coastal Ocean Program Decision Analysis Series No. 24, Silver Spring, MD 20910, 192 pp.

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tests of such products for publicity or advertising purposes is not authorized.

Contribution No. 2658 from NOAA/Pacific Marine Environmental Laboratory

Contribution S529 from Fisheries-Oceanography Coordinated Investigations

For sale by the National Technical Information Service, 5285 Port Royal Road

Springfield, VA 22161

ii
Contents iii

Contents

Executive Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Part 1—Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Part 2—Synthesis of Research: A Contemporary Understanding

of the Southeast Bering Sea Ecosystem . . . . . . . . . . . . 11

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.1.1 Brief history and goals of recent research programs . 11

2.1.2 Structure of synthesis report . . . . . . . . . . . . . 12

2.1.3 Brief description of the Southeastern Bering Sea . . 12

2.2 Climate and the Bering Sea Marine Ecosystem . . . . . . . . 15

2.2.1 Climate indices . . . . . . . . . . . . . . . . . . . . . 15

2.2.2 Meteorological forcing . . . . . . . . . . . . . . . . . 20

2.3 Physical Components of the Eastern Bering Sea Ecosystem . 21

2.3.1 Sea Ice in the Eastern Bering Sea . . . . . . . . . . 21

2.3.2 Biological importance of the timing of ice retreat . . 26

2.3.3 Cold pool formation . . . . . . . . . . . . . . . . . . 28

2.3.4 Biological impacts of water temperature . . . . . . . 29

2.3.5 On-shelf fluxes and nutrient replenishment . . . . . 30

2.3.6 Stratification, mixing, and the vertical flux of nutrients 32

2.4 Biological Components of the Eastern Bering Sea Ecosystem 32

2.4.1 Primary production . . . . . . . . . . . . . . . . . . 32

2.4.2 Coccolithophore bloom . . . . . . . . . . . . . . . . 34

2.4.3 Zooplankton . . . . . . . . . . . . . . . . . . . . . . 38

2.4.4 Fish . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

2.4.5 Marine mammals and seabirds . . . . . . . . . . . . 51

2.5 What Controls Recruitment of Walleye Pollock? . . . . . . . 62

2.6 Summary of Advances . . . . . . . . . . . . . . . . . . . . . . 68

2.6.1 Marine climate . . . . . . . . . . . . . . . . . . . . . 68

2.6.2 Basin and slope waters . . . . . . . . . . . . . . . . 68

2.6.3 Shelf waters . . . . . . . . . . . . . . . . . . . . . . 68

2.6.4 Biological components of the ecosystem . . . . . . . 69

2.7 Areas of Research Requiring More Attention . . . . . . . . . 70

2.8 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . 72

Part 3—Forecasting Abundance of Walleye Pollock: Indices for

Juvenile Abundance ....................... 73

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

3.2 Conceptual Framework for Development of Indices . . . . . . 76

3.2.1 A pollock-centric perspective of the southeastern Bering

Sea ecosystem . . . . . . . . . . . . . . . . . . . . . 76

3.2.2 A generic model of pathways of energy flow through

the ecosystem . . . . . . . . . . . . . . . . . . . . . 77

3.2.3 Other aspects of indices development . . . . . . . . 79

3.3 Development of Environmental Indices . . . . . . . . . . . . . 80

3.3.1 Indices of stratification, potential supply of nutrients to the upper mixed layer, and turbulence experienced by larval pollock (Carol Ladd and Phyllis

Stabeno) . . . . . . . . . . . . . . . . . . . . . . . . 80

3.3.2 An index related to wind turbulence and feeding success (Bern Megrey and Nick Bond) . . . . . . . . . 83
iv Contents

3.3.3 Variations in net short wave radiation: Toward development of an index of when sufficient light exists

for primary production (Jeff Napp and Nancy Kachel) 84

3.3.4 Indices related to sea ice (Sigrid Salo and Phyllis

Stabeno) . . . . . . . . . . . . . . . . . . . . . . . . 86

3.3.5 Indices related to the cold pool and adjacent fronts

(Peggy Sullivan) . . . . . . . . . . . . . . . . . . . . 89

3.3.6 Pribilof age-0 pollock as an index of pollock yearclass strength (Jim Ianelli) . . . . . . . . . . . . . . 90

3.3.7 Model simulations . . . . . . . . . . . . . . . . . . . 95

3.3.8 A matrix for cataloging indices (Allen Macklin) . . 100

3.4 Application of a Transport and Predation Index to Recruitment for Stock Assessment Purposes (Jim Ianelli) . . . . . . 103

3.4.1 Background . . . . . . . . . . . . . . . . . . . . . . . 103

3.4.2 An application . . . . . . . . . . . . . . . . . . . . . 105

3.4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . 106

3.5 Changing Relationships between Climate and Biological Indices in the Eastern Bering Sea (Alan Springer) . . . . . . . . 112

3.5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . 112

3.5.2 Observations . . . . . . . . . . . . . . . . . . . . . . 112

3.5.3 Physical-physical relationships . . . . . . . . . . . . 113

3.5.4 Physical-biological relationships . . . . . . . . . . . 114

3.5.5 Biological-biological relationships . . . . . . . . . . . 117

3.5.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . 120

3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

3.6.1 Progress accomplished in the development of indices 125

3.6.2 Further development of indices . . . . . . . . . . . . 127

3.6.3 Concluding remarks . . . . . . . . . . . . . . . . . . 128

Part 4—Pribilof Islands: A Microcosm for the Southeast Bering

Sea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

4.1 Overview of the Pribilof Island Ecosystem . . . . . . . . . . . 129

4.2 Working Group Products . . . . . . . . . . . . . . . . . . . . 130

4.2.1 How large an area is the Pribilof region? . . . . . . 131

4.2.2 How much transport is there to this area from major

pollock spawning regions? . . . . . . . . . . . . . . . 132

4.2.3 How does the abundance of juvenile pollock near the

Pribilof Islands compare with abundance along the

Bering Sea Shelf? . . . . . . . . . . . . . . . . . . . 133

4.2.4 How does the abundance of age-0 pollock near the

Pribilof Islands relate to diet, survival, and energetics of higher predators? . . . . . . . . . . . . . . . . 142

4.2.5 How important is predation to pollock abundance

near the Pribilof Islands? . . . . . . . . . . . . . . . 146

4.3 Comparison of abundance of juvenile pollock and zooplankton between the Pribilof Islands and other shelf areas in the

eastern Bering Sea based on acoustic surveys in 1997, 1998,

and 1999 (Andreas Winter and Kenneth Coyle) . . . . . . . . 149

4.3.1 Methods . . . . . . . . . . . . . . . . . . . . . . . . 149

4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . 154

4.3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . 155

4.4 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . 157

Part 5—SEBSCC Administration and Management . . . . . . . 159

5.1 Project Management Team . . . . . . . . . . . . . . . . . . . 159

5.2 Technical Advisory Committee . . . . . . . . . . . . . . . . . 161
Contents v

5.3 Research Council . . . . . . . . . . . . . . . . . . . . . . . . . 162

5.4 Project Coordination and Communication . . . . . . . . . . . 162

5.5 Data Management . . . . . . . . . . . . . . . . . . . . . . . . 163

5.6 Research Structure and Methodology . . . . . . . . . . . . . . 164

5.7 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165

5.8 Leverage and Collaboration . . . . . . . . . . . . . . . . . . . 165

5.9 Research Components and Principal Investigators . . . . . . 165

5.10 Field Operations: SEBSCC Cruise Schedule . . . . . . . . 170

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172

Part 6—References and SEBSCC Publications . . . . . . . . . . 173

The Southeast Bering Sea Ecosystem: Implications

for Marine Resource Management (Final Report:

Southeast Bering Sea Carrying Capacity)

S. Allen Macklin and George L. Hunt, Jr. (Editors)

Executive Summary

Background

Southeast Bering Sea Carrying Capacity (SEBSCC, 1996–2002) was a NOAA

Coastal Ocean Program project that investigated the marine ecosystem of

the southeastern Bering Sea. SEBSCC was co-managed by the University

of Alaska Fairbanks, NOAA Alaska Fisheries Science Center, and NOAA

Pacific Marine Environmental Laboratory. Project goals were to understand the changing physical environment and its relationship to the biota of

the region, to relate that understanding to natural variations in year-class

strength of walleye pollock (Theragra chalcogramma), and to improve the

flow of ecosystem information to fishery managers.

In addition to SEBSCC, the Inner Front study (1997–2000), supported by

the National Science Foundation (Prolonged Production and Trophic Transfer to Predators: Processes at the Inner Front of the S.E. Bering Sea), was

active in the southeastern Bering Sea from 1997 to 1999. The SEBSCC and

Inner Front studies were complementary. SEBSCC focused on the middle

and outer shelf. Inner Front worked the middle and inner shelf. Collaboration between investigators in the two programs was strong, and the joint

results yielded a substantially increased understanding of the regional ecosystem.

SEBSCC focused on four central scientific issues: (1) How does climate

variability influence the marine ecosystem of the Bering Sea? (2) What determines the timing, amount, and fate of primary and secondary production?

(3) How do oceanographic conditions on the shelf influence distributions of

fish and other species? (4) What limits the growth of fish populations on

the eastern Bering Sea shelf? Underlying these broad questions was a narrower focus on walleye pollock, particularly a desire to understand ecological

factors that affect year-class strength and the ability to predict the potential of a year class at the earliest possible time. The Inner Front program

focused on the role of the structural front between the well-mixed waters of

the coastal domain and the two-layer system of the middle domain. Of special interest was the potential for prolonged post-spring-bloom production

at the front and its role in supporting upper trophic level organisms such as

juvenile pollock and seabirds. Of concern to both programs was the role of

interannual and longer-term variability in marine climates and their effects

on the function of sub-arctic marine ecosystems and their ability to support

upper trophic level organisms.
2 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

The eastern Bering Sea contains an Oceanic Regime that occupies the

basin and a Shelf Regime that occupies the eastern shelf. The Oceanic

Regime of the eastern basin is influenced by Alaska Stream water that enters the Bering Sea through Amchitka and Amukta passes in the Aleutian

Islands, and turns northeastward to form the Aleutian North Slope Current.

This current is the major source of water for the Bering Slope Current that

sometimes follows the depth contours of the eastern shelf northwestward

with a regular flow, and sometimes degenerates into an ill-defined, variable

flow characterized by numerous eddies and meanders. These eddies are potentially important as habitat for larval and juvenile pollock, and can carry

these fish, as well as nutrient salts, from the Oceanic Domain into the Outer

Shelf Domain.

The broad continental shelf (up to 500 km wide) of the southeastern

Bering Sea is differentiated into three bathymetrically fixed domains: the

Coastal Domain that extends from the shore to about the 50-m isobath, the

Middle Shelf Domain, between the 50-m and 100-m isobaths, and the Outer

Shelf Domain that ranges from 100 m to 200 m in depth. The domains

are separated by fronts or transition zones, with the narrow (5 to 30 km)

Inner Front or Structural Front between the Coastal Domain and the Middle

Shelf Domain, the wide (>50 km) middle transition zone between the Middle

Shelf Domain and the Outer Shelf Domain, and the Outer Front between

the Outer Shelf Domain and the waters of the slope.

Results

Many of the important advances in our understanding of the coupling of

regional atmospheric processes, physical processes in the Bering Sea, and

biological responses to these forcing mechanisms have resulted from SEBSCC

and Inner Front research. Among these advances in our knowledge are:

1. A new understanding of the importance of warm-season climate

2. An improved understanding of the functions of the Aleutian North

Slope Current and the Bering Slope Current and their potential importance to fish

3. Documentation of eddies over the slope and along the outer shelf and

their importance as habitats for fish and for the transport of small

pollock onto the shelf

4. Development of a model that resolves eddies and suggests that the

source of eddies in the southeastern Bering Sea basin is in the Bering

Slope Current

5. Discovery of mean flows across the shelf, which, for short intervals in

summer, can result in replenishment of salts and transport of oceanic

zooplankton to the Inner Front

6. A clarification of the role of the timing of ice retreat for the timing and

fate of the spring phytoplankton bloom
The Southeast Bering Sea Ecosystem 3

7. Understanding of the relative importance of winter and warm season

conditions for determining the timing and strength of spring stratification and the potential for summer mixing across the pycnocline

8. An understanding of the temporal and spatial variability of the Inner

Front and its role in nutrient fluxes from depth

9. A greater appreciation for the role of water temperature in the distribution of fish, especially pollock, the hatching time of pollock eggs,

and the population dynamics of zooplankton

10. A greater appreciation for the role of episodic events in structuring the

ecosystem

11. A new hypothesis concerning the role of wind-forced transport for the

survival of juvenile walleye pollock

12. Evidence that the net annual primary production in the northern

Bering Sea may be declining, whereas in the southeastern Bering Sea,

increases in standing stocks of fish and non-crab benthic invertebrates

do not suggest a decline in production

13. Commencement of research on the role of nanno- and microplankton,

including coccolithophores, in the marine ecosystem of the southeastern Bering Sea

14. From re-examination of zooplankton time series, no evidence for a

significant decline in zooplankton biomass within samples stratified by

the domain in which they were collected

15. Immense increases in the biomass of large jellyfish and then a rapid

decline to levels seen in the early 1980s

16. Observations of responses of crustacean zooplankton to variations in

water temperature, including apparent shifts in the timing of breeding

in shelf euphausiids, and the abundance of small shelf copepods

17. Determination that large cetaceans are now encountered more frequently in shelf waters than in the 1970s, and that substantial numbers

are present over the shelf during summer

18. Progress in defining multiple hypotheses concerning the control of yearclass strength in walleye pollock

19. Improved understanding of the foraging habits and diets of marine

birds, northern fur seals, and Steller sea lions at the Pribilof Islands;

the potential relationships between the abundance of adult pollock over

the shelf and the reproductive success of black-legged kittiwakes at the

Pribilof Islands

20. Evidence that sub-lethal food stress during the breeding season can

lead to elevated levels of corticosteroids, and possibly elevated levels

of post-breeding season mortality in seabirds
4 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

21. Development of a mechanistic explanation of how variations in climate

could cause shifts in the mechanisms controlling walleye pollock populations (Oscillating Control Hypothesis)

Elements of these advances were used to develop indices for prediction of

walleye pollock year-class strength. A conceptual switch model that relates

survival of pollock from one developmental stage to the next is a function

of environmental parameters, such as climate, sea ice, and timing of prey

production. The Oscillating Control Hypothesis is an important climate

element of the conceptual model. The following indices are some that were

developed by SEBSCC as potentially useful for predicting pollock abundance

based on elements of the conceptual model.

1. An index of wind turbulence versus larval feeding success

2. An index of variations in net short-wave radiation

3. A new index of sea ice that led to insight regarding changes in timing

of spring in the region

4. Indices related to thermal conditions developed from the compilation

of water temperature data taken during annual trawl surveys

5. An index developed from simulations by the Northeastern Pacific Regional Ocean Model System (NEPROMS) that combines early-lifehistory transport and predation by adult pollock

6. An index from autumn age-0 pollock abundance that may be an acceptable early predictor for pollock year-class strength

An examination of a large set of biological and physical indices showed

that potential relationships could change sign with regime shifts, providing

a clear warning that a simple model does not exist.

SEBSCC also addressed the importance of age-0 pollock in the Pribilof

Island region as possible indicators of future eastern Bering Sea pollock recruitment, and discussed relationships of age-0 pollock to their prey and

predators. To this end, the Pribilof Island region was considered to include

an area extending along the shelf and shelf-break 100 nm from the center

of the islands. This determination was based on ecological efficiency predicted by the energetic/trophic-web-based ECOPATH model. According to

its prediction, the largest percentage of the energetic demands of regional

biota was met by food sources within that boundary.

Age-0 pollock abundance near the Pribilof Islands was generally higher

than in the surrounding area, and was about the same as abundance in the

Inner Front region. Drogued buoy trajectories suggest that most pollock

found near the Pribilofs during summer likely come from spawning areas

near Unimak Pass. Some may be spawned near Bogoslof Island or even

closer to the Pribilofs.

There may be an interaction among the spatial distribution of adult

pollock (that appears to have two modes over the years: a northwest and
The Southeast Bering Sea Ecosystem 5

southeast mode), the relative importance of different pollock spawning areas

and the summer wind patterns that can influence changes in the distribution

of age-0 pollock over the summer. To whit, the abundance of age-0 pollock

near the Pribilof Islands may change drastically throughout the summer.

It is difficult to establish a relationship between bird and mammal diets

and abundance of age-0 pollock near the Pribilof Islands. No such relationships were found for murres or kittiwakes. Percent occurrence of age-0

pollock otoliths in fur seal scat was high throughout most summers, which

suggests that large year classes cannot be distinguished from moderate-sized

year classes by scat samples. However, small pollock year classes may be

distinguishable using scat data.

The vast quantity of information collected during SEBSCC and Inner

Front has led to new understanding and hypotheses of how the southeastern

Bering Sea ecosystem functions. This information is providing input to the

National Marine Fisheries Service’s stock assessment of juvenile pollock. At

present, the pathway to providing input is through a grass-roots approach;

the integrative research method employed during SEBSCC included fisheries

scientists at the Alaska Fisheries Science Center whose tasks are directed toward status of stocks. Some of these scientists were SEBSCC Principal

Investigators and/or members of SEBSCC Working Groups. They were the

ones who helped to develop indices of potential survival of early life histories

of pollock and a formal technique to use such indices in stock assessment

models. The quantitative use of annual metrics of the physical and biological environment is the next logical step in the progression toward improved

forecasts of age-1 recruitment. As the management of fisheries matures toward an ecosystem-based approach, the integrated biophysical knowledge

attained by fisheries scientists during SEBSCC and Inner Front will prove

to be an invaluable foundation.

Future Research

Although a great deal of progress in understanding the forcing mechanisms

and the ecosystem responses of the southeastern Bering Sea has been made

since the early 1990s, there are still many unanswered questions that demand

attention if we are to provide good stewardship for the resources of the Bering

Sea shelf. Answers to these questions go beyond academic value; we need

to understand the processes and mechanisms that determine how changes in

global climate and the associated physical forcing affect the flow of energy

to upper trophic level organisms including commercially valuable fish and

shellfish, marine mammals, and seabirds. Below are listed a number of

questions that require attention.

1. How do atmospheric forcing mechanisms influence the distribution and

transport of salts, heat, nutrients, and organic matter in the eastern

Bering Sea?

2. How is the timing and magnitude of cross-shelf flux controlled?
6 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

3. How do different patterns in the formation, thickness, extent, and meltback of sea ice influence the ecosystems of the eastern Bering Sea shelf?

4. What controls the amount and fate of primary production?

5. What controls the biomass and productivity of upper trophic level

organisms?

6. How can we translate information about Bering Sea ecosystems into

products useful to those who are managing the fisheries of the eastern

Bering Sea?

The following are logical next steps in the development and refinement

of indices. The first steps deal with indices for estimating recruitment of

age-1 pollock. The last is an index that may provide guidance regarding

interpretation of the annual stock assessment from trawl results.

1. Continue comparisons with other model simulations and observations

to provide verification of the Bering Sea model (NEPROMS).

2. Develop a wind index of nutrient supply to the shelf from Bering

Canyon.

3. Develop a user interface and/or other techniques so the NEPROMS is

more accessible as a tool.

4. Examine various indices versus occurrence of northwest and southeast

centers of the adult pollock population.

5. Extract an index of zooplankton abundance from ECOSYM.

6. Develop a history of the time/space occurrence of the pollock-roe fishery to help define time/space limits for pollock eggs as initial points

for trajectory simulations.

7. Develop a similar product for pollock spawning that occurs near Bogoslof Island.

8. Use temperature observations from annual trawl surveys to develop an

index of the presence of the cold pool and the locations of the inner

and middle fronts (i.e., the boundaries of the middle shelf domain) in

given years.

9. Use temperature observations to create annual distributions of temperature that, in turn, can be used to simulate zooplankton production.

10. Examine the influence of changes in the physical environment (e.g.,

water temperature, location of fronts) on preferred habitat for pollock,

and how variations in these features affect estimates from trawl results.
The Southeast Bering Sea Ecosystem 7

With the results of SEBSCC and the Inner Front Study, the stage is set

for future research programs to increase understanding of regional processes,

their coupling to larger-scale phenomena, and the impacts that changes to

this ecosystem will have on our society, and vice versa. Several programs and

projects on the immediate horizon will be the first to contribute. Programs

such as the Bering Sea Ecosystem Study (BEST) (http://www.arcus.org/

bering/) and the Study of Environmental Arctic Change (SEARCH)

(http://psc.apl.washington.edu/search/) are developing research plans

for the Bering Sea and the waters to the north, and they will be supported by ongoing efforts from the North Pacific Research Board, FisheriesOceanography Coordinated Investigations (FOCI), the National Marine Fisheries Service, Alaska Ocean Observing System, North Pacific Anadromous

Fish Commission, and others.

The Southeast Bering Sea Ecosystem—Part 1 9

Part 1—Introduction

As a society, we recognize that the bounty of our planet is not endless. Accordingly, we develop and apply techniques that enable us to better manage

resources. With the establishment of the exclusive economic zone (EEZ)

of U.S. coastal waters in 1976, our society realized that many valuable resources are maritime in nature. Legislation was adopted to provide for their

protection, and the ensuing decades brought into existence systematic research and managerial programs to optimize the balance between resource

utilization and conservation. The principles to effect such balance are called

stewardship. Some coastal stewardship programs have been less than effective, however, as demonstrated by the collapse of the Georges Bank fishery

off New England. Although it is difficult to discern who or what is to blame

for fishery collapses, i.e., whether they are the result of natural processes,

anthropological influence, or a mixture of both, the outcome is certain. The

penalties for failure to maintain rich, viable fisheries are billions of dollars

of lost revenue and loss of livelihood to all dependent on the industry for an

unknown period of time.

Of all U.S. coastal waters, the Gulf of Alaska and Bering Sea ecosystems

are among the most productive, supporting vast populations of fishes, birds,

and marine mammals. The Alaskan EEZ is crucial to the U.S. economy.

Fish and shellfish from these waters constitute nearly 50% of the U.S. and

5% of the world harvest. Alaskan pollock, salmon, halibut, and crab generate over $2 billion in revenue each year and provide an important source of

high protein food. Pollock also provide food for numerous fish, marine birds

and mammals and as such are a keystone or node of Alaskan ecosystems.

Until recently, these most productive waters haven’t seen the same commercial pressure as other U.S. fisheries, and the opportunity to manage them

successfully is still with us.

Southeast Bering Sea Carrying Capacity (SEBSCC) was a NOAA Coastal

Ocean Program project designed to conduct basic research on the Bering Sea

ecosystem and transfer information to applied management of fish stocks.

SEBSCC began in the final months of fiscal year 1996 and closed at the

end of fiscal year 2002. The project contributed to resource management by

examining ecosystem dynamics and survival of walleye pollock in the southeastern Bering Sea. The goals of SEBSCC were to understand the overall

workings of the ecosystem (the changing physical environment and its relationship to the biota of the region), to relate that understanding to natural

variations in year-class strength of walleye pollock (Theragra chalcogramma),

and transfer this information to fishery managers. Incorporating scientific

understanding of survival processes represents an advance from the classical

fishery management technique of survey and projection.

The model for SEBSCC’s information transfer came from the FisheriesOceanography Coordinated Investigations (FOCI) in the Gulf of Alaska.

FOCI provides information from research directly to NOAA’s National Marine Fisheries Service advisory team whose mission is to advise the North

Pacific Fisheries Management Council on the status of pollock stocks in the

Gulf of Alaska and Bering Sea. In this way, researchers play a unique role
10 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

of directly transferring results to applied management. Moreover, the investment in research is a small fraction (less than 0.04%) of the commercial

value of the Alaskan stocks. Beginning in 1992, FOCI scientists have analyzed biological and physical time series to estimate survival qualitatively.

This scientific application significantly simplifies the stock projection analysis used by NMFS to recommend fishing quotas to the management council.

As our understanding of how ecosystem processes interact to limit or encourage survival of young pollock, our ability to provide more accurate and

quantitative forecasts will increase. With SEBSCC, research has begun to

address the more complex questions of survival in the Bering Sea. SEBSCC

scientists coordinated their research efforts with several international scientific organizations to address the effect of climate fluctuations on the Bering

Sea ecosystem. As we understand how these systems function, we will become more able to forecast changes. These include not only large changes

in abundance of pollock, but also changes in the ecosystem that favor other

species. Such knowledge will permit commercial interests to reallocate and

focus their efforts. With time, this ongoing fisheries oceanographic research

will provide expanded social and economic benefits. As our knowledge of natural variations in the population of commercially valuable stocks increases,

the application of scientific techniques will occupy a growing niche in the

management process. Our ability to understand ecosystem interactions will

amplify our ability to maintain and allocate coastal resources effectively.
The Southeast Bering Sea Ecosystem—Part 2 11

Part 2—Synthesis of Research: A Contemporary

Understanding of the Southeast Bering Sea

Ecosystem

Results From the Synthesis Working Group on the Southeastern Bering Sea: Recent Advances in Knowledge From Bering

Sea FOCI, Southeast Bering Sea Carrying Capacity and the Inner Front Program

Principal Author: George L. Hunt, Jr.

With participation and contributions from:

Chris Baier, Nick Bond, Ric Brodeur, Troy Buckley, Lorenzo Ciannelli, Liz

Conners, Chuck Fowler, Susan Henrichs, Jerry Hoff, Anne Hollowed, George

Hunt, Jim Ianelli, Nancy Kachel, Carol Ladd, Allen Macklin, Lyn McNutt,

Jeff Napp, Jim Overland, Sigrid Salo, Robert Schabetsberger, Jim Schumacher,

Beth Sinclair, Alan Springer, Phyllis Stabeno, Al Tyler, Lucy Vlietstra, Muyin

Wang, and Terry Whitledge

2.1 Introduction

2.1.1 Brief history and goals of recent research programs

The Bering Sea is a semi-enclosed sea that connects the North Pacific and

Arctic Oceans. The 500-km-wide eastern continental shelf encompasses

about one-half of its area and supports extraordinarily rich marine resources.

These are of vital importance to the economic survival, subsistence, and cultural foundations of the many indigenous people of western Alaska (IARPC,

2001). Marine resources of the Bering Sea include fisheries equal to about

one half of the United States’ fishery production, about 80% of the seabirds

found in the nation’s waters, and substantial populations of marine mammals (NRC, 1996; IARPC, 2001). Its fishery landings include walleye pollock

(Theragra chalcogramma, a nodal species in the shelf ecosystem), salmon,

halibut and crab, and generate over $2 billion in revenue each year (IARPC,

2001). It is thus vital to the economic and social well being of the region

that we understand the factors that determine the productivity of the Bering

Sea. Such information will facilitate the wise exploitation and stewardship

of this most important marine ecosystem.

Since the mid 1990s, two coordinated research programs, the Southeast

Bering Sea Carrying Capacity (SEBSCC) project (1996–2002), supported

by the Coastal Ocean Program of NOAA, and the Inner Front study (1997–

2000), supported by the National Science Foundation (Prolonged Production and Trophic Transfer to Predators: Processes at the Inner Front of

the S.E. Bering Sea), were active in the southeastern Bering Sea (Macklin

et al., 2002). These two studies were complementary, with one focused on

the middle and outer shelf (SEBSCC) and the other (Inner Front) on the

inner shelf. Collaboration between investigators in the two programs was

strong. In addition, the Bering Sea FOCI (Fisheries-Oceanography Coordinated Investigations) program, funded by NOAA’s Coastal Ocean Program,
12 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

was active from 1991 through 1997, and many of the FOCI investigators

were also participants in the Inner Front and SEBSCC programs.

The goals of the Bering Sea FOCI program were to understand the factors that control the abundance of fish populations, and, in particular, the

abundance and stock structure of walleye pollock in the Bering Sea (Macklin, 1999). A specific objective was to reduce uncertainty in managing these

fish. The goals of the SEBSCC program were to increase understanding of

the southeastern Bering Sea ecosystem, to document the ecological role of

juvenile walleye pollock and factors that affect their survival, and to develop

and test annual indices of pre-recruit (age-1) pollock abundance (Macklin et

al., 2002). SEBSCC focused on four central scientific issues: (1) How does

climate variability influence the marine ecosystem of the Bering Sea? (2)

What limits the growth of fish populations on the eastern Bering Sea shelf?

(3) How do oceanographic conditions on the shelf influence distributions of

fish and other species? (4) What determines the timing, amount, and fate of

primary and secondary production? Underlying these broad goals was a narrower focus on walleye pollock. Of particular concern was the understanding

of ecological factors that affect year-class strength and the ability to predict

the potential of a year-class at the earliest possible time. The Inner Front

program focused on the role of the structural front between the well-mixed

waters of the coastal domain and the two-layer system of the middle domain.

Of particular interest was the potential for prolonged post-spring-bloom production at the front and its role in supporting upper trophic level organisms

such as juvenile pollock and seabirds. Of concern to both programs was the

role of interannual and longer-term variability in marine climates and their

effects on the function of sub-arctic marine ecosystems and their ability to

support upper trophic level organisms (see also Francis et al., 1998).

2.1.2 Structure of synthesis report

In this section of the final report, we provide an overview of the contributions

of the SEBSCC and Inner Front programs to our understanding of processes

in the southeastern Bering Sea. We begin with a brief description of the

physical environment and marine climate of the eastern Bering Sea. We

follow with an examination of changes and mechanisms of change for the

physical and biological components of the eastern Bering Sea ecosystem.

We then discuss several conceptual hypotheses that provide possible avenues

toward understanding changes in the amount and fate of production in the

southeastern Bering Sea, and how climate change may affect the function of

this ecosystem. Finally, we identify a series of questions that recent work

suggests will be important to answer in our quest for a fuller understanding

of the function of the eastern Bering Sea in a changing climate.

2.1.3 Brief description of the Southeastern Bering Sea

The Bering Sea consists of a deep central basin, a northwestern shelf in the

Gulf of Anadyr that reaches south along the Kamchatka Peninsula, and a

broad eastern shelf that stretches from the Alaska Peninsula to Russia and
The Southeast Bering Sea Ecosystem—Part 2 13

Figure 2.1: Three-dimensional view of the eastern Bering Sea, with location of

the biophysical mooring M2 shown. Map courtesy of K. Birchfield.

the Bering Strait (Fig. 2.1). For the purposes of this report, the waters of the

eastern Bering Sea can be divided into an Oceanic Regime that occupies the

basin and a Shelf Regime that occupies the eastern shelf. The eastern shelf

can be further sub-divided into the southeastern shelf and a northeastern

shelf, with the dividing line running east-west just south of St. Matthew

Island from the coast to the shelf edge.

The following overview of the Oceanic Regime is based on the description of Schumacher et al. (2003). The Oceanic Regime of the eastern basin

is influenced by Alaska Stream water that enters the Bering Sea through

Amchitka and Amukta passes in the Aleutian Islands, and turns right to

form the Aleutian North Slope Current (ANSC; Reed and Stabeno, 1999)

(Fig. 2.2). This current in turn provides the major source of water for the

Bering Slope Current (BSC) that varies between following the depth contours of the eastern shelf northwestward with a regular flow, and becoming

an ill-defined, variable flow characterized by numerous eddies and meanders

(Stabeno et al., 1999a). The eddies occur not only in water seaward of the

eastern shelf (Schumacher and Reed, 1992), but also in waters as shallow

as 100–122 m deep (Reed, 1998). These eddies are potentially important

as habitat for larval and juvenile pollock, and can carry these fish, as well

as nutrient salts, from the Oceanic Domain into the Outer Shelf Domain

(Schumacher and Stabeno, 1994; Stabeno et al., 1999a).
14 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Figure 2.2: Southeastern Bering Sea showing isobaths and domains. Map courtesy

of N. Kachel.

The broad continental shelf (up to 500 km wide) of the southeastern

Bering Sea is differentiated into three bathymetrically fixed domains, which

include the Coastal Domain that extends from the shore to about the 50-m

isobath, the Middle Shelf Domain, between the 50-m and 100-m isobaths,

and the Outer Shelf Domain that ranges from 100 m to 200 m in depth

(Fig. 2.3) (Iverson et al., 1979b; Coachman, 1986; Schumacher and Stabeno,

1998; Stabeno et al., 2001). The domains are separated by fronts or transition zones, with the narrow (5 to 30 km) Inner Front or Structural Front between the Coastal Domain and the Middle Shelf Domain, the wide (>50 km)

middle transition zone between the Middle Shelf Domain and the Outer Shelf

Domain, and the Outer Front between the Outer Shelf Domain and the waters of the slope. In summer, the Coastal Domain is well mixed to weakly

stratified, the Middle Shelf Domain is strongly stratified, and the Outer Shelf

Domain has well mixed upper and lower layers with a zone of gradually increasing density between (Schumacher et al., 2003). During summer in the

Middle Shelf Domain, the temperature difference between the upper and

lower layers can be greater than 8◦C, and changes in density are dominated

by temperature rather than salinity (Hunt et al., 2002a). In the northeastern Bering Sea, changes in tidal energy and freshwater discharge from the

Yukon River affect the location of the fronts, with the Inner Front occurring

in water 30 m or less (Schumacher and Stabeno, 1998). During summer in
The Southeast Bering Sea Ecosystem—Part 2 15

166°E 168° 172° 176° 180° 176°W 172° 168° 164° 160°

64°N

62°

60°

58°

56°

54°

52°

50°

Figure 2.3: Schematic of major currents in the Bering Sea. From Stabeno et al. (1999a).

Norton Sound, a two-layered system can occur in water as shallow as 20 m.

In winter, the water column there is well mixed.

2.2 Climate and the Bering Sea Marine Ecosystem

An important development in fisheries oceanography during the last decades

of the twentieth century was the realization that climate variability, at the

scale of decades, could have profound impacts on the function of marine

ecosystems. Of particular importance was the realization that seemingly

small shifts in the mean values of atmospheric variables, at least when compared to their interannual variability, could result in major changes in the

productivity or standing stocks of fish populations. Recent work has suggested that climate variations may affect the mechanisms (e.g., bottom-up

or top-down) that control populations, such that the impact on fish stocks

of a given rate of harvest may be quite different in different climate patterns.

2.2.1 Climate indices

For the North Pacific Ocean, among many potential indices of climate variability, there are three well-studied indices of climate patterns that de-
16 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

pend on variability in winter, the Pacific Decadal Oscillation (PDO), the El

Ni ̃no/Southern Oscillation (ENSO), and the Arctic Oscillation (AO). The

PDO is defined as the leading mode of sea surface temperature variability in

the North Pacific (north of 20◦N), and has time scales of 20–30 years (Ladd

et al., unpublished manuscript; Mantua and Hare, 2002) (Fig. 2.4). The

PDO is a major mode of variability (Wallace et al., 1992), yet it explains

only 21% of the variance of the monthly SST and is primarily centered on

the central North Pacific rather than the Gulf of Alaska and Bering Sea.

The ENSO has widespread influence on global climate variability at time

scales of 2–7 years, and has its greatest influence in the tropics. Recently,

ENSO has been shown to have a small but significant influence on the marine

climate of the Bering Sea via atmospheric teleconnections (Niebauer, 1998;

Hollowed et al., 2001; Martin et al., 2001; Overland et al., 2001). The AO is

defined as the leading mode of sea level pressure variability north of 20◦N,

and consists of a pattern of zonally symmetric variability in the strength of

the polar vortex (Thompson and Wallace, 1998; Ladd et al., unpublished

manuscript). The AO has its largest variance in winter (January–March).

The strongest mode of variability in the AO is interannual, but it also varies

at decadal scales, having changed sign in 1976 and again in 1989 (Thompson and Wallace, 1998; Overland et al., 1999) (Fig. 2.4). The AO has an

influence on the Bering Sea through its affect on the Aleutian Low, which is

the monthly or seasonal mean location of the center of low sea level pressure

over the North Pacific (Overland et al., 1999). The value and position of the

Aleutian Low reflects the strength and distribution of storm tracks in the

southern Bering Sea and sub-arctic Pacific Ocean. These storms have great

influence on the marine climate of the Bering Sea in winter.

A fourth index of atmospheric pressure, the North Pacific (NP) pattern,

represents the leading mode in spring of the 700 hPa height and is most

prominent from March through July (Barnston and Livezey, 1987; Ladd et

al., unpublished manuscript). The NP consists of a north-south pressure

dipole, and its strong variance in spring relates to storminess in the Bering

Sea (Overland et al., 2002).

In the North Pacific region, the wintertime indices, in conjunction with

indices of biological responses in marine ecosystems, have been used to identify abrupt shifts in climatic forcing and ecosystem response at decadal time

scales (e.g., Trenberth and Hurrell, 1995; Mantua et al., 1997; Francis et

al., 1998; Hare and Mantua, 2000; McFarlane et al., 2000; Hollowed et al.,

2001). Two of these regime shifts have been identified in the past thirty

years. One followed the winter of 1976–1977, in which the PDO and the AO

both shifted (Fig. 2.4). A second shift, of just the AO, occurred after the

winter of 1988–1989 (Ebbesmeyer et al., 1991; Hare and Francis, 1995; Sugimoto and Tadokoro, 1998; Beamish et al., 1999b; Brodeur et al., 1999a; Hare

and Mantua, 2000). There is some evidence of a third shift in the winter of

1998–1999 (Schwing and Moore, 2000; Peterson et al., 2002). Although the

ENSO appears to alternate between two states that are repeatedly visited,

that does not appear to be the case for regime shifts in the southeastern

Bering Sea, where the few regimes documented so far have each had unique

characteristics.
The Southeast Bering Sea Ecosystem—Part 2 17

Figure 2.4: Time series of the Arctic Oscillation (AO) and the Pacific Decadal

Oscillation (PDO). Note the change from negative to positive in the PDO as of

1977 and from positive to negative in 1998. The AO showed a shift from negative

to positive about 1989. Updated from Overland et al. (1999).

The main climate feature influencing the southeastern Bering Sea is the

Aleutian Low (Fig. 2.5). The Bering Sea lies between the cold Arctic air

mass to the north and warmer maritime air mass of the North Pacific. Over

the Bering Sea, there is a region of strong gradients in sea-level pressure

(SLP) between the Aleutian Low and the high pressure over the Arctic.

Considerable interest has developed around low frequency (multi-decadal)

variability in the North Pacific in both the physical and biological portions

of the North Pacific ecosystem (Minobe, 1999). Hare and Mantua (2000)

found evidence in 100 time series for regime-like jumps in these records near

1977 and 1989. The evidence was clearer in the biological data than in

the physical data. Their second mode showed strong covariability between

physical and biological variations in the Bering Sea over the past 40 years.

To investigate this variability, the strength of the Aleutian Low is plotted

in Fig. 2.6 for 1900 through 2000. Visually, there are some indications of a

lowering of SLP after 1927, higher pressures from 1947–1977, and an event

near 1990. However, the main feature of this plot is the large change from

year to year. In fact, almost two-thirds of the variance in this record is at

time scales shorter than 5 years (Overland et al., 1999). When one looks

at the time series after 1977, almost half the years have SLP values near or

slightly above the mean (horizontal line) that occur between years of record

low SLP. Using a 5-year running mean is not particularly representative of

individual years, either for periods when the values were near average or for
18 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

1038 mb

1032

1028

1024

1020

1018

1012

1008

1004

1000

998

Figure 2.5: The location of the Aleutian Low, the statistical mean location of

winter low pressure cells in the northern North Pacific. Figure courtesy of J.E.

Overland.

those periods with large negative anomalies, as the averaged data remove

the signal from the extreme years that may force the system.

One approach to modeling the variability of the Aleutian low time series is to fit its auto-correlation function, the correlation of the time series

with itself as a function of lag between the series. Fig. 2.7 shows the autocorrelation function (the vertical stick plots) repeated in all three plots as

a function of lag in years. The values are all small, generally less than 0.3;

however, the values are mostly positive, suggesting a broad contribution of

frequencies to the original time series, rather than a Gaussian distribution

of random errors. The solid heavy lines represent three models of the time

series (Percival et al., 2001). The left plot is an Auto-Regressive (AR) red

noise model that rapidly decays to zero. The middle plot is a long memory

(LM) model that rapidly decays in the first years but then has a broad contribution from longer lags. The third model (right) is a 50-year oscillation

plus white noise. All three models are fit with two free parameters and all are

candidates to represent the Aleutian Low, because a 100-year record is too

short an interval to say one model is superior based on classical statistical

grounds.

We have additional information that favors the long memory model. The
The Southeast Bering Sea Ecosystem—Part 2 19

Figure 2.6: The strength of the Aleutian Low with respect to the long-term

mean, annual signal, and 5-year running mean. Note that the signal is dominated

by extremely strong interannual variability. From Percival et al. (2001).

Figure 2.7: Auto-correlation functions (stick plots) of the Aleutian Low time

series, with three models of the time series: Left: Auto-Regressive, Middle: Long

memory, and Right: 50-year oscillation plus white noise. (From Percival et al.,

2001.)

LM model assumes contributions from many processes with different time

lags, while the AR model represents a single time scale. It is more reasonable

to expect that many processes, such as North Pacific SST, Arctic air masses,

ENSO, and Siberian storm systems, influence atmospheric variability over

the Bering Sea. We also know from the work of Hare and Mantua (2000)

that regime-like behavior favors the LM and oscillator models, as the LM

model is about 5 times more likely to have a 20-year run of all positive or

negative values than the AR model.

What does this mean for the biology of the Bering Sea? Instead of a

pure regime shift model of the physical system with changes spread over

20+ years, we have a physical system that has a strong response in 1 or 2
20 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

years. The low frequencies contribute to the timing of these strong events.

In this conceptual model, the impact of the change in the physical system is

large enough to promote the reorganization of the ecosystem. The massive

increase in pollock after 1978 and 1989 (Wespestad et al., 2000) and the

1997 coccolithophore event (Sukhanova and Flint, 1998; Vance et al., 1998;

Overland et al., 2001) follow these patterns. Thus, the regimes seen in the

biological data of Hare and Mantua (2000) may be the result of a continuing

decadal ecosystem reorganization following a major meteorological extreme.

2.2.2 Meteorological forcing

Variability in SST warming is expected to be a dominant factor on the Bering

Sea shelf during the warm season. Figure 2.8 summarizes the 40-year record

of the rate of warming in early summer and 1 August SST (top panel); two

crucial components of the surface heating, the downward shortwave (solar)

radiation and latent heat fluxes (middle panel); and two aspects of the wind,

the rate of wind mixing (u3

x) (Fig. 2.8, bottom panel), and along-shelfbreak

wind stress (Fig. 2.9, bottom panel). These time series reveal that recent

August SSTs are roughly 1◦C warmer than those typifying the 1960s. Much

of the trend in this heating can be attributed to a long-term tendency toward

more solar heating and decreasing surface latent heat fluxes, or evaporative

cooling, over the 40-year record. Interannual variability in the summertime

warming appears to be due to a combination of variations in solar heating

and wind mixing.

The physical state of the ocean over the shelf during spring and summer

has been well documented only since 1995, which compromises interpretation

of decadal-scale changes in the marine ecosystem. Recent work has indicated

that there exists a viable approach for addressing this lack of direct oceanographic observations for retrospective analyses. Specifically, it appears that

reasonably accurate hindcasts of the evolution of thermal profiles over the

shelf can be made using a 1-D ocean model (the Price-Weller-Pinkel or PWP

model) forced by surface winds and heat fluxes generated from reanalysis of

historical records (Ladd et al., unpublished manuscript). These hindcasts

can be used to estimate not just temperatures (an important factor for zooplankton growth rates) but also the supply of nutrients to the euphotic zone,

with implications for summertime primary production.

Progress has been made in understanding the impact of climate variations

on the Bering Sea shelf, but there remain a host of important but unanswered

questions. For example, while the mean flow on the shelf is sluggish, it is

likely at times to be significant. It remains unclear which aspects of atmospheric forcing are important to cross-shelf flow, and what time scales are of

primary importance to these events. In general, the sources and potential

predictability of climate variations for the Bering Sea in spring through fall

are poorly understood. In particular, little attention has been devoted to

air-sea interactions during fall and the possible ramifications of their variability for the ecosystem. Thus, while there is a growing appreciation that

relatively short-lived but intense events can account for a disproportionate

share of the seasonally integrated forcing, it remains unknown how the fre-
The Southeast Bering Sea Ecosystem—Part 2 21

Figure 2.8: Summer season averages for 1959–1999. Top: SST tendency (solid line, ◦C 100 d−1) and SST

anomaly on 1 August (dotted line, ◦C), both at 57◦N, 164◦W. The zero anomaly line for SST also represents

the value of the mean SST tendency. Middle: Downward shortwave radiative heat flux anomaly (solid line,

W m−2) and surface latent heat flux (dotted line, W m−2), both at 57◦N, 167◦W. Bottom: Wind mixing

anomaly at 57◦N, 164◦W (solid line, m3 s−3 × 100) and along-shelf wind stress anomaly at 56◦N, 169◦W

(dotted line, N m−2). From Bond and Adams (2002).

quency and timing of these events relate to the longer-term variations in

climate.

2.3 Physical Components of the Eastern Bering Sea Ecosystem

2.3.1 Sea Ice in the Eastern Bering Sea

Sea ice extent is a crucial aspect of the physical environment of the eastern

Bering Sea shelf. The Bering Sea is a marginal ice zone, which is typically ice

free from June through October. Beginning in November, cold winds from

the Arctic cool the water and begin the formation of ice in the polynyas
22 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Figure 2.9: Winter season average anomalies for 1959–1999. Top: Surface temperature at 57◦N, 164◦W

(solid line, ◦C). Middle: Cross-shelf wind stress at 57◦N, 164◦W (solid line, N m−2), and net surface-heat

flux at 56◦N, 172◦W (dotted line, W m−2). Bottom: Wind stress curl at 55◦N, 173◦W (solid line, N m−3 ×

107) and along-peninsula wind stress at 53◦N, 173◦W (dotted line, N m−2). From Bond and Adams (2002).

that form on the lee sides of islands and coasts. Throughout winter, the

prevailing winds advect the ice southward into warmer water where the ice

melts at its southern edge, cooling and freshening the seawater (Pease, 1980;

Niebauer et al., 1999). Recently, Niebauer (1998) has found that the position

and depth (strength) of the Aleutian Low has a significant effect on the

sea ice cover of the Bering Sea, and that the effect of the Aleutian Low

is linked to ENSO events. The maximum southerly extent of the ice and

the amount of ice melt affect fluxes of heat and salt, thereby influencing

both baroclinic flow and the temperature at the bottom (the cold pool)
The Southeast Bering Sea Ecosystem—Part 2 23

in the Middle Shelf Domain (Ohtani and Azumaya, 1995; Schumacher and

Stabeno, 1998; Wyllie-Echeverria and Wooster, 1998).

One of the more readily observed impacts of climate change on the eastern Bering Sea ecosystem is the extent and duration of sea ice over the Bering

Sea shelf (Niebauer, 1998; Stabeno et al., 2001; Hunt et al., 2002a). The

seasonal variation in the position of the ice edge is about 1700 km, the most

extensive of any Arctic or sub-arctic region (Niebauer, 1998). In an average

winter, about 75% of the eastern shelf is ice covered, but the amount and

duration of ice cover can vary interannually by up to 25% of the seasonal

range, depending on the wind field (Niebauer, 1983, 1998; Schumacher and

Stabeno, 1998). During the last two decades, the maximum ice extent over

the eastern shelf occurred, on average, in March, but maximum ice extents

have been as early as January (in 2000), and as late as the end of April

(in 1976) (Stabeno and Hunt, 2002). During the early and mid 1970s, ice

arrived early over the southeastern shelf and persisted into spring. Following

this cold period, there was a warmer period when sea ice was less common.

Finally, in 1989 there appeared to be a shift to cooler conditions although

not as cold as those observed in 1972–1976 (Stabeno et al., 2001).

During cold winters, ice can cover most of the eastern shelf (Stabeno et

al., 2001). Alternately, during warmer winters, ice does not extend much

farther south than St. Matthew Island. Analysis of ice charts (Fig. 2.10)

shows that decadal patterns of variability are evident (Niebauer, 1998; Hunt

et al., 2002a). In the period from 1977 to 1996, there was a 5% reduction

of ice cover as compared to 1947–1977 (Niebauer, 1998). Interannual and

decadal-scale variability in sea-ice coverage was greater at the southernmost

edge of the ice field than farther north (Fig. 2.10). Between 57 and 58◦N,

there was an apparent decrease in days with ice after 1 January between

1972–1976 (mean number of days with ice 130 ± 18 SD) as compared with

1977–1989 (67 ± 26 SD, t = 1.767, p = 0.096) and 1990–2000 (76 ± 23.3 SD,

t = 2.036, p = 0.061). The average pattern of ice coverage has also changed

since the early 1970s (Fig. 2.11), when the maximum extent of the zone in

which ≥10% ice cover was present annually for >2 weeks extended farther

south and west than it did in the 1980s or the 1990s (Stabeno et al., 2001).

Additionally, in the 1980s and 1990s, the zone where ice lasted for at least

2 weeks withdrew northeastward along the Alaska Peninsula. Most of the

north side of the Peninsula has been ice free since 1990. The 1989 regime

shift did not result in a return to the extensive ice conditions present before

1977.

In the eastern Bering Sea, a proxy for sea ice extent is the mean winter (Jan–Apr) surface temperature (Bond and Adams, 2002). The 40-year

record for the site of Mooring 2 (top panel of Fig. 2.9) shows a notably cold

period in the early to middle 1970s, and a warm period in the late 1970s

into early 1980s, but conditions during the 1990s are similar to those in the

1960s. The time series of atmospheric parameters directly related to sea ice

(middle panel of Fig. 2.9) indicate that the presence of sea ice is a function

of not just the cross-shelf component of the wind, but is also strongly related

to the net surface heat fluxes, which are determined by air and water temperatures and wind speed. Niebauer (1998) found that before the regime
24 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

2000

1990

1980

1970

2000

1990

1980

1970

Oct Dec Feb Apr Jun

100%

75

50

25

5

59–60°N

57–58°N

100%

75

50

25

5

Figure 2.10: Percent ice cover for two latitudinal bands in the eastern Bering Sea.

Note the decrease in ice cover in the southern region as of about 1977, whereas

there is little evidence for a change in ice cover at this time in the more northerly

region. From Hunt et al. (2002a).

shift of 1976–1977, below-normal ice cover in the eastern Bering Sea was

associated with El Ni ̃no conditions, during which the center of the Aleutian

Low shifted eastward, and resulted in warm air from the Pacific flowing over

the southeastern Bering Sea. After the regime shift, the Aleutian Low was

located even farther eastward during El Ni ̃no periods, and under these circumstances the southeastern shelf was subjected to north and east winds

from the interior of Alaska, which resulted in increased ice cover.

An examination of the timing of the ice retreat and air temperature at

500 hPa (∼5 km altitude) reveals that during the last decade there has been
The Southeast Bering Sea Ecosystem—Part 2 25

66°N

64°

62°

60°

58°

56°

54°

66°N

64°

62°

60°

58°

56°

54°

66°N

64°

62°

60°

58°

56°

54°

175° 171° 167° 163° 159°W

175° 171° 167° 163° 159°W 175° 171° 167° 163° 159°W

Figure 2.11: Contours of the number of weeks that >10% sea-ice cover was present over the eastern Bering

Sea shelf. After Stabeno et al. (2001).
26 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Table 2.1: Relationship between the timing of ice retreat and the type of spring

bloom (from Hunt and Stabeno, 2002).

Bloom Occurs at Bloom in

Ice Edge Open Water

Ice gone by mid March 0 7

Ice remains after late March 6 0

a marked change in the timing of spring transitions (Stabeno and Overland, 2001). While sea ice has extended farther south in the last decade, it

has retreated more quickly, resulting in the northern Bering Sea being ice

free earlier than in previous decades. In addition, atmospheric temperature

during May has increased by 3◦C in the 1990s compared to the 1980s.

2.3.2 Biological importance of the timing of ice retreat

In the southeastern Bering Sea, the timing of spring primary production is

determined by a combination of the date of ice retreat, stabilization of the

water column by solar heating, and the cessation of strong storm activity

(Sambrotto et al., 1986; Stabeno et al., 1998, 2001; Eslinger and Iverson,

2001). The timing of the spring bloom is important because it determines

the ambient water temperatures in which grazers of the bloom must forage.

Data illustrative of the conditions that determine the timing of the spring

bloom were obtained from Mooring 2, located in ∼72 m of water in the

middle domain (Figs. 2.1, 2.12). If ice retreat comes before mid-March,

there is insufficient light to support net production in the well-mixed water

column (e.g., Fig. 2.12: 1996, 1998, 2000). Without ice remaining after mid

March, the spring bloom is delayed until May or June, after winter winds

have ceased and thermal stratification stabilizes the water column (Fig. 2.12)

(Stabeno et al., 1998, 2001; Eslinger and Iverson, 2001). If ice melt occurs

in April or May, there is an early, ice-associated bloom (e.g., Fig. 2.12: 1995,

1997, 1999). The pattern of late ice retreats with early ice-related blooms

and early ice retreats with late blooms has held since the 1970s (Table 2.1).

Although wind mixing of the water plays a role in determining when the

bloom will occur, it is apparent in Fig. 2.13 that the timing of the last

winter storm is less important than the date of ice retreat in determining

the timing of the bloom. Thus, early blooms occur in cold water and are

related to ice-edge blooms, whereas late blooms occur in relatively warm

water and are not related to the ice edge (Figs. 2.12, 2.14).

We also often see evidence of chlorophyll in the water under the ice during

ice melt. This may be evidence of the release of ice algae or seeding of the

bloom by ice algae. However, we do not know the origin of this signal, or if

its species composition is the same as that of the open water bloom. This

requires further investigation.
The Southeast Bering Sea Ecosystem—Part 2 27

Figure 2.12: Time series of ocean temperatures and fluorescence from Mooring 2. The thin yellow line at

the bottom of each panel is fluorescence scaled to the maximum each year. Temperature of <–1◦C indicates

the presence of melting ice. From Hunt et al. (2002a).
28 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Ice, Mixing and Blooms

15 Jun

5 Jun

26 May

16 May

6 May

26 Apr

16 Apr

6 Apr

27 Mar

Date of last winter storm

Date of ice retreat

No Ice 27 Mar 16 May 5 Jul

Early bloom

May bloom

June bloom

96

79

98 97

80 95 76

99 77

78

81

75

X

X

X

Figure 2.13: The relationship between the timing of the last winter storm, ice

retreat, and the relationship of the bloom to sea ice. Note that blooms that occur

to the right of the dotted gray line are associated with the ice edge. From Hunt et

al. (2002a).

2.3.3 Cold pool formation

The bottom waters over the eastern Bering Sea shelf show considerable interannual variation in temperature. Over the northern parts of the shelf, cold,

salty brine is rejected as sea ice forms in polynyas, and this dense water sinks

to the bottom. Density flows to the north carry much of this salty bottom

water through Bering Strait to the Arctic Ocean where it contributes to the

halocline (Cavalieri and Martin, 1994; Schumacher and Stabeno, 1998).

Over the central and southeastern Bering Sea shelf, cold bottom waters are formed when sea ice melts, and the cold, fresh meltwater is mixed

throughout the water column by storms (Stabeno et al., 1998). These meltwaters can chill the entire water column to about –1.7◦C. When the surface
The Southeast Bering Sea Ecosystem—Part 2 29

Figure 2.14: Relationship between timing of ice retreat, whether the bloom will occur in association with

ice in cold water or in open, warmer water, and the potential effect of a warm water bloom on copepod

production. After Hunt et al. (2002a).

waters are warmed by solar radiation in spring, a thermocline forms, and the

cold bottom waters are largely insulated from further heating (Coachman

et al., 1980; Ohtani and Azumaya, 1995; Wyllie-Echeverria, 1995). Bottom temperatures in this “cold pool” warm slightly over the summer, but

may remain below 2◦C until storm-induced mixing occurs in fall (Ladd et

al., unpublished manuscript). The extent and temperature of the southern

cold pool is dependent on the amount of ice melt that occurs, and since

ice is constantly melting as it is advected south, the amount of meltwater

generated is a function of the duration of time that ice is present (Pease,

1980; Overland and Pease, 1982). The southern extent of the cold pool may

also be influenced by the cross-shelf advection of warm water in winter (see

2.3.5, below), but the importance of advection of the warm water for cold

pool limitation has not been investigated.

2.3.4 Biological impacts of water temperature

The temperature of the water column beneath the surface is important for

determining the rates of the physiological processes of organisms residing

there. Physiological processes vary as a power function of temperature,

and phytoplankton growth is less sensitive to water temperature than is

zooplankton growth (see also Vidal, 1980; Vidal and Smith, 1986; Townsend

et al., 1994). Water temperature exerts a strong influence on the growth
30 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

rates of zooplankton, and is often thought of as more important than food

availability for limiting the growth rates of small-bodied copepods (McLaren,

1963; Corkett and McLaren, 1978; Vidal, 1980; Dagg et al., 1984; Huntley

and Lopez, 1992). Thus, in years with warm water, Walsh and McRoy

(1986) hypothesized that zooplankton would capture more of the primary

production than in cold years, and the greater production of zooplankton in

warm years would support the pelagic community, e.g., fish such as pollock.

Thus, in the middle domain where interannual environmental variability is

greatest, water temperature is likely to play a major role in interannual

variation in copepod biomass (Smith and Vidal, 1984, 1986; Napp et al.,

2000) (See section 2.4.3 Zooplankton).

Water temperature also affects the timing of hatching and survival of

pollock eggs (Blood, 2002). Modeling of hatching times based on laboratory experiments and temperatures found in the Bering Sea predicted that

the hatching periods for pollock eggs could vary as much as 13 days between the warmest and coldest years encountered between 1995 and 1998.

A longer incubation period means that the eggs are exposed to predation

for a greater period and that there is increased risk of exposure to extreme

low temperatures as the eggs in the upper water column can be subjected

to temperature variations driven by short-term weather fluctuations. Malformation of pollock embryos has been reported for eggs incubated at –1◦C

(Nakatani and Maeda, 1984), a temperature not infrequently encountered in

the shelf waters of the Bering Sea shelf in late winter and early spring (Hunt

et al., 2002a).

The cold pool has important effects on the distribution and survival of

fish. For example, juvenile walleye pollock prefer to avoid waters <2◦C

(Wyllie-Echeverria, 1996). When the southern cold pool is of reduced size,

these fish spread out over much of the middle domain in shelf waters not

frequented by adult pollock. When the southern cold pool is extensive, the

juvenile pollock move toward the warmer waters of the outer domain and

shelf edge, where they are subject to increased levels of cannibalism by adult

pollock that reside in these outer shelf waters (Ohtani and Azumaya, 1995;

Wyllie-Echeverria, 1995, 1996; Wyllie-Echeverria and Wooster, 1998).

2.3.5 On-shelf fluxes and nutrient replenishment

On-shelf fluxes of nutrient salts from the basin are critical for the long-term

productivity of the eastern Bering Sea shelf, but the mechanisms responsible

for forcing these fluxes are still not well understood. In the northern Bering

Sea, it has been suggested that water from the basin and slope crosses the

shelf to flow northward through Anadyr Strait and Bering Strait (Fig. 2.3)

(Shuert and Walsh, 1993). Nutrients in this water would then be available

to support the extraordinarily high rates of summertime production found

in the Chirikov Basin and northward through Bering Strait (Springer et

al., 1996). This process would also provide a mechanism for the transport

of large oceanic copepods onto the northern shelf, where they support immense populations of planktivorous seabirds (Springer and Roseneau, 1985;

Springer et al., 1987, 1989; Hunt and Harrison, 1990; Hunt et al., 1990; Rus-
The Southeast Bering Sea Ecosystem—Part 2 31

sell et al., 1999). However, the exact connections to the Bering Sea basin

remain unclear, as observations by Stabeno and Reed (1994) and model

results (Overland et al., 1994) suggest that the Bering Slope Current, the

presumed source of water carrying nutrients and copepods to the Chirikov

Basin, may turn westward south of 59◦N. Rather, the source of the slope

water passing through Anadyr Strait may be water that has advected onto

the outer shelf episodically or through the canyons of the shelf edge as far

south as Bering Canyon; these then flow along the outer shelf with increasing

intensity to the north and west, and thence through Anadyr Strait (Stabeno

et al., 1999a).

Over the southeastern Bering Sea shelf, nutrient replenishment was initially thought to result from tidally driven diffusion (Coachman, 1986). However, more recent work shows that the coefficients required for tidally driven

diffusion are larger than those present on the shelf (Stabeno et al., 2001).

Although mean annual current velocities over the middle shelf at Mooring 2

are weak, currents averaged over shorter periods (e.g., daily) can exceed

25 cm s−1. Currents are strongest in near-surface waters (34-month mean,

1.2 cm s−1 at 15 m), and much weaker at the bottom (0.2 cm s−1 at 60 m)

(Stabeno et al., 2001). Currents are strongest in winter and weakest in summer. In 1998, these currents were sufficiently strong to advect organisms typical of the oceanic regime into Middle Domain waters adjacent to the Inner

Front near Cape Newenham and Nunivak Island (Hunt et al., 1999; Coyle

and Pinchuk, 2002b). It is also hypothesized that the generally seaward

movement of sea ice in winter may result in an onshore flow at depth that

could contribute to replenishment of nutrients over the southeastern shelf,

but this hypothesis has yet to be investigated (Schumacher and Alexander,

1999).

Mechanisms for on-shelf transport of nutrients include eddies that bring

slope waters onto the shelf at least as far as the 150-m isobath (Stabeno

et al., 1999a; Stabeno and Van Meurs, 1999; Okkonen, 2001; Johnson et

al., 2004), and Reed (1998) has observed them in waters between 100 and

120 m about 20% of the time (Fig. 2.3). However, eddies are rare in water

<100 m deep, and other mechanisms are required to replenish nutrients in

the Middle and Inner Domains. Two regions of preferential on-shelf flow

are Bering Canyon, which is just north of the Aleutian Islands near Unimak

Pass, and the area west of the Pribilof Islands, where the shelf break narrows

(Stabeno et al., 1999a). There, acceleration of flow over the outer shelf

(Coachman, 1986; Schumacher and Stabeno, 1998) results in entrainment

of slope water (Stabeno et al., 1999b). On-shelf flow west of the Pribilof

Islands can move into the Middle Domain where it is marked by a front to

the northeast of St. Paul Island (Flint et al., 2002), or it may be entrained

around the islands in tidal currents (Stabeno et al., 1999b). Two measures of

the atmospheric forcing of the ocean circulation, the wind stress curl and the

wind stress along the Alaskan Peninsula/Aleutian Island chain, also exhibit

substantial variability (bottom panel of Fig. 2.9), but it remains an open

question whether the flow over the shelf is sensitive to these effects.
32 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

2.3.6 Stratification, mixing, and the vertical flux of nutrients

Shelf waters of the southeastern Bering Sea, although well mixed during winter by storms, stratify in late spring from solar heating (Eslinger and Iverson,

2001; Ladd et al., unpublished manuscript). This stratification inhibits vertical flux of nutrients, and once the spring bloom has exhausted them from

the upper mixed layer, the lack of nutrients limits new production. These

nutrients can be replenished when processes break down the stratification.

Sambrotto et al. (1986) identified the importance of summer storms as a

mechanism for deepening the pycnocline and stirring nutrients into the upper mixed layer where they could be taken up by plant cells in the presence

of light. Analysis of wind speed cubed, a measure of the ability of winds

to mix the upper water column, shows that summer winds have declined

since the early 1980s (Fig. 2.15). Ladd et al. identified the importance of

winter conditions for determining the strength of the pycnocline, and hence

the ease with which it could be eroded by storms. A second pathway for the

movement of nutrients from depth to the surface layers is upward mixing in

the vicinity of fronts (Iverson et al., 1979a; Sambrotto et al., 1986; Kachel

et al., 2002). For example, this upward mixing can result in regions of high

productivity on the stratified side of the inner front (Hunt et al., 1996a;

Kachel et al., 2002). These mechanisms require the availability of a pool of

nutrients at depth in the vicinity of the front. In 1997, after the completion

of the spring bloom, a severe storm in late May mixed the water column

to depths of 65 m or more and resulted in renewed production. The result

was a depletion of nutrients to 60 m or more, and a lack of availability of

nutrients at the base of the inner front and at the pycnocline for mixing into

the upper mixed layer (Stockwell et al., 2001). This observation pointed to

the importance of episodic events in structuring the ecology of shelf waters.

2.4 Biological Components of the Eastern Bering Sea Ecosystem

2.4.1 Primary production

During the past decade, we have made considerable advances in our understanding of factors influencing the timing of the spring bloom (see above,

Stabeno et al., 2001; Hunt et al., 2002a; Hunt and Stabeno, 2002). However,

we know less about the magnitude of the bloom, and whether the amount of

production varies between ice-associated and open-water blooms. Alexander

and Niebauer (1981) and Niebuauer et al. (1981, 1990, 1995) suggest that in

the early 1980s the ice edge-bloom used to be as great or greater than the

open-water bloom. In 1997, which had an ice-associated bloom followed by

a second, open-water bloom subsequent to a mixing event in May, primary

production, based on nutrient drawdown, was greater than in the early 1980s

(Stockwell et al., 2001).

The question of whether there has been a change in the amount of net

annual new production is important. Schell (2000), using stable isotope ratios from carbon sequestered in the baleen of bowhead whales during periods

of feeding in the northern Bering Sea, estimated that primary production
The Southeast Bering Sea Ecosystem—Part 2 33

2000

1990

1980

1970

1960

2400

2000

1600

1200

800

400

0

m3/s3

Feb Apr Jun Aug Oct Dec

Feb Apr Jun Aug Oct Dec

Year

Figure 2.15: Wind speed cubed at St. Paul Island. Note that since the late 1970s,

winter winds have been less strong, and in summer, there have been more periods

of wind below the long-term average (those areas in light gray). From Hunt et al.

(2002a).

in the Bering Sea had decreased by as much as 30 to 40% since 1967, with

almost all of the decrease coming since 1976. Cullen et al. (2001) questioned whether some of this effect was the result of anthropogenic CO2 or

changes in the species composition of the phytoplankton. However, Schell

(2001) provided additional information, including data from stable isotopes

of nitrogen, which corroborates the earlier findings. Grebmeier and Cooper

(1994, 2002), Grebmeier and Dunton (2000), and Grebmeier (1992) have

found evidence for declines in sediment oxygen respiration of as high as 73%

over the period 1987 to 2002, with declines in benthic biomass of 89% over

a longer period. In addition, they have documented changes in the species

composition of benthic bivalves and other fauna. Taken together, these

studies point to a decline in production levels for the northern Bering Sea,

including in the Saint Lawrence Island polynya region south of the island.

These reductions in production may be related to reduced northward flow

through Bering Strait (Roach et al., 1995) and a consequent diminution of

nutrient advection from the Bering Sea basin onto the northern shelf.

In the southeastern Bering Sea, there appears to be no clear indication

of a decrease in production, though Hirons et al. (2001) have attempted to

extend the results of Schell (2000) to the remainder of the eastern Bering Sea

and Gulf of Alaska by examining stable isotope ratios in the teeth of harbor

seals (Phoca vitulina), northern fur seals (Callorhinus ursinus) and Steller

sea lions (Eumetopias jubatus). For these species, when data from teeth from

both the Gulf of Alaska and the Bering Sea were combined, they found a

significant decline in the δ13C in sea lions and similar, though non-significant,
34 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

trends in harbor seals and fur seals. However, from their published data, it

is difficult to tell whether these trends depend upon the combination of data

from the Bering Sea and the Gulf of Alaska, or whether the trends would

hold up within regions as well.

In contrast, in 1997, estimates of primary production in the southeastern

Bering Sea, based on the reduction of nitrate over the middle and inner

shelf, suggested that new production might have been between 10 and 30%

greater in 1997 than in the early 1980s (Stockwell et al., 2001). Estimates

for production levels in 1998 and 1999, however, do not appear to differ from

those of the early 1980s (Whitledge, University of Alaska Fairbanks, personal

communication). In contradiction to these estimates of productivity in 1997,

1998, and 1999, sediment trap data supported the notion that productivity

was higher in 1998 than 1997 (Smith et al., 2002), as did data from the

uptake of ammonium (Rho, 2000). However, δ13C values from copepods in

the 1997–1999 period were lower than found by Schell et al. (1998). Smith et

al. hypothesized that this decline in δ13C could be the result of diminished

production, similar to that found by Schell (2000), or it could be the result

of other factors. Interestingly, in the period 1997–1999, Smith et al. did not

find a decrease in δ13C in euphausiids and chaetognaths, as would have been

expected if the decline in copepod δ13C was the result of changes in primary

productivity. Based on the sum of the above results and the high stable or

increasing biomass of fish and invertebrates over the southeastern shelf (see

below), it seems most unlikely that there has been a marked reduction in

primary production there since the 1970s (Hunt et al., 2002a).

2.4.2 Coccolithophore bloom

In the 1990s, there were marked anomalies in the species composition of

phytoplankton in the eastern Bering Sea. Although diatoms typically dominate phytoplankton biomass in the eastern Bering Sea (Sukhanova et al.,

1999), in the late 1990s, coccolithophore blooms dominated summer phytoplankton assemblages over much of the shelf (Figs. 2.16, 2.17) (Sukhanova

and Flint, 1998; Vance et al., 1998; Napp and Hunt, 2001; Stockwell et al.,

2001). Coccolithophores are small (5–20 μm) phytoplankton surrounded by

calcium carbonate plates. Coccolithophore blooms are common in the North

Sea, Gulf of Maine, and coastal eastern North Pacific, and characteristically

occur in nutrient depleted waters with warm, shallow mixed layers (Holligan

et al., 1983; Mitchell-Innes and Winter, 1987; Balch et al., 1992; Townsend

et al., 1994). High densities of coccoliths result in a whitening of the water detectable by satellite imagery (Holligan et al., 1983; Balch et al., 1991;

Brown and Yoder, 1993; Gower, 1997). Although coccolithophore blooms

had not been documented previous to 1997 in the eastern Bering Sea, there

are satellite images of “white” water from there (Brown and Yoder, 1993).

The first coccolithophore bloom (Emiliania huxleyi) recorded from the

Bering Sea was initially observed 3 July 1997 in the middle domain (Vance et

al., 1998), although E. huxleyi cells were present in the water as early as May

and June (Stockwell et al., 2001). By early August, the bloom was at least

200 km wide (Tynan, 1998), and by early September it covered 2.1 × 105 km2
The Southeast Bering Sea Ecosystem—Part 2 35

September 1997

25 April 1998

12 September 1999

Figure 2.16: Three SeaWIFS views of the Bering Sea coccolithophore bloom.
36 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Figure 2.17: SeaWIFS false color image of the coccolithophore bloom, 24 July

1998. Note the trace of coccoliths in the water passing through Shpanberg Strait

to the east of St. Lawrence island, and thence northward through the east side of

Bering Strait. From Napp and Hunt (2001).

of the middle domain and parts of the inner domain (Sukhanova and Flint,

1998; Vance et al., 1998; Napp and Hunt, 2001). It was apparent in SeaWiFS

imagery from 18–25 September, and traces could be detected as late as

October. This event was unusually large and long-lived when compared with

coastal blooms of coccolithophores described previously (Table 2.2) (Holligan

et al., 1983; Balch et al., 1991; Brown and Yoder, 1993; Gower, 1997). Most

coastal blooms are on the order of 104 km2, and persist less than 40 days.

Concentrations of coccolithophore cells were as great or greater than those

reported from the North Atlantic (Townsend et al., 1994b; Robertson et al.,

1994).

The coccolithophore bloom greatly reduced light penetration and visibility in the water. Near Nunivak Island (Fig. 2.2), the depth of the 1% light

level, often taken as the maximum limit for net photosynthesis, shoaled from

between 18 and 33 m in June to between 5 and 15 m in September in the

bloom (Zeeman, University of New England, personal communication). At

Slime Bank (Fig. 2.1), outside the bloom, the depth of the 1% light level
The Southeast Bering Sea Ecosystem—Part 2 37

Table 2.2: Comparison of large-scale Emiliania huxleyi blooms (after Napp and

Hunt, 2001).

Characteristic Measure Source

Size km2

Coastal Blooms 5–10 × 104 Holligan et al., 1983

5 × 105 Balch et al., 1991

5 × 105 Brown and Yoder, 1993

5 × 105 Brown and Podesta, 1997

Open Ocean Blooms 5 × 105 Holligan et al., 1983

Bering Sea 1997 2.4 × 105 Napp and Hunt, 2001

Duration months

Gulf of Maine, 1998 ≈1 Townsend et al., 1994

Gulf of Maine, 1989 ≈1 Townsend et al., 1994

NE Atlantic, 1991 1.1 Robertson et al., 1994

Bering Sea, 1997 4 Napp and Hunt, 2001

Cell Density cells · ml−1

Gulf of Maine, 1988 2.4 × 103 Townsend et al., 1994

Gulf of Maine, 1989 1.5 × 103 Townsend et al., 1994

NE Atlantic, 1991 1.0 × 103 Robertson et al., 1994

Bering Sea, 1997 3.1–4.7 × 103 Napp and Hunt, 2001

Lith Density liths · ml−1

Gulf of Maine, 1988 1.3 × 105 Townsend et al., 1994

Gulf of Maine, 1989 3.0 × 105 Townsend et al., 1994

NE Atlantic, 1991 3.5 × 105 Robertson et al., 1994

Bering Sea, 1997 3–5 × 105 Napp and Hunt, 2001

remained the same (27 m) from spring until fall. Underwater videos near

the Pribilof Islands documented cloudy bloom-water ranging in depth from

7 m to 44 m, and extending to the bottom in several locations (Brodeur,

NOAA/NWFSC, personal communication).

The eastern Bering Sea coccolithophore bloom recurred yearly from 1997

through 2001, and in most years was comparable in spatial extent and cell

density to large-scale coccolithophore blooms in other parts of the world’s

oceans, though of considerably longer duration (Table 2.2). New algorithms

for the analysis of SeaWiFS imagery showed that the coccolithophore blooms

started in February as melting began along the ice edge and then spread

northward, peaking in April (Iida et al., 2002). The blooms with the largest

aerial extent occurred in the warm years of 1998 and 2000, whereas those in

the cold years of 1999 and 2001 were smaller (Iida et al., 2002).

The summer of 1997 was marked by unusually warm surface temperatures, a strong thermocline and depletion of nitrate and silicate from the

surface waters (Napp and Hunt, 2001). The conditions under which the

1997 bloom commenced were similar to conditions believed to be conducive

to coccolithophore blooms elsewhere (Balch et al., 1992; Holligan et al., 1983;

Townsend et al., 1994b). However, we do not know why the eastern Bering

Sea blooms were initiated, or why they recurred over a several year period

when there was great variability in oceanographic conditions (Stabeno and
38 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Hunt, 2002). We also lack information on why they ceased to occur in 2002,

and on their role in energy flux to the food webs of the eastern Bering Sea

shelf.

2.4.3 Zooplankton

Microzooplankton are protists and metazoan organisms smaller than 200 μm

that are present in the plankton (Dussart, 1965). Because they are small

and individually inconspicuous, their role in the world ocean has been underappreciated until recently. Although microzooplankton are an abundant

element in the food webs of the southeastern Bering Sea, we are only just

beginning to investigate their role in this ecosystem (Howell-Kubler et al.,

1996; Olson and Strom, 2002). For example, in April 1992, microprotozoan

abundances ranged from 300 to 6233 organisms l−1 with a biomass of 0.58 to

9.73 μgCl−1 (Howell-Kubler et al., 1996). These biomass levels were similar

to those of other oceanic regions, and were estimated to be sufficient to meet

the metabolic needs of first-feeding larval pollock, though observations to

determine if pollock use this resource are not available.

Microzooplankton were an important component of the southeastern

Bering Sea food webs in the summer of 1999. Within the coccolithophore

bloom, 75% of the total chlorophyll a (Chl a) came from cells >10 μm

(mostly the diatom Nitzschia spp.), and average growth rates for cells >10 μm

and <10 μm were nearly equal (Olson and Strom, 2002). Within the bloom,

microzooplankton grazing rates were only 28% of the growth rates of phytoplankton growth rates. In contrast, for the shelf as a whole, grazing by

microzooplankton accounted for 110% of the growth of cells >10 μm and

only 81% of the growth of cells <10 μm. This preferential grazing on the

larger cells may help to explain the persistence of the coccolithophore bloom

and is contrary to the belief that microzooplankton are constrained to diets

of nannophytoplankton (Olson and Strom, 2002). These findings emphasize

the need for a thorough examination of the role of microzooplankton both in

the summer, for which we have evidence that they may form an important

link between phytoplankton and mesozooplankton, and during the spring

bloom, when their role has yet to be evaluated.

Traditionally, calanoid copepods have been believed to be the major

agents of energy transfer between large-celled diatoms and upper trophic

level consumers such as planktivorous fish (e.g., Hood, 1999). Although

emerging data now suggest that in boreal oceans these large copepods have

a large dietary component of microzooplankton (Capriulo et al., 1991; Sherr

and Sherr, 1992; Rivkin et al., 1999), the large copepods are still important

prey for fish, whales and seabirds. In the eastern Bering Sea, the copepod

communities of the basin and outer shelf are dominated in spring by large

species of Neocalanus (N. cristatus, N. plumchrus, and N. fleminergi) and

Eucalanus bungii, and the middle and inner shelf by the smaller Calanus

marshallae, Pseudocalanus spp. and Acartia spp. (Cooney and Coyle, 1982;

Smith and Vidal, 1986).

The most complete time series of zooplankton abundances in the eastern

Bering Sea depend on data gathered in summer by the T/S Oshoro Maru,
The Southeast Bering Sea Ecosystem—Part 2 39

which has documented declines in zooplankton biomass from the basin between the late 1960s and the early 1990s (basin, Fig. 2.18) (Sugimoto and

Takadoro, 1997). In the basin, they also found a biennial fluctuation in

zooplankton biomass that was negatively correlated with the catch of Asian

pink salmon (Oncorhynchus gorbuscha). Sugimoto and Takadoro interpreted

these two patterns as indicating a bottom-up control of the zooplankton on

a decadal-scale, and a top-down control on an annual scale. In contrast to

the results obtained from the basin, examination of Oshoro Maru data from

the shelf showed neither a long term trend, nor evidence of biennial cycles

in summer zooplankton biomass (Fig. 2.18) (Hunt et al., 2002a; Napp et al.,

2002). Although there was considerable interannual variation, no discernable

temporal trend was detected.

For spring, two sets of studies show that the abundance of small shelf

species of copepods varies with sea temperature. In 1980, the upper layer

of the middle and outer shelves of the southeastern Bering Sea warmed

slowly as compared to 1981 (Smith and Vidal, 1986). In May 1981, small

copepods of the middle shelf were more abundant than in 1980 (Table 2.3),

and Calanus marshallae was observed to have two generations in 1981, rather

than the expected one (Smith and Vidal, 1986). In the 1990s, there was

also the opportunity to compare a very cold year (1999) with two years

in which water temperatures were high (1997, 1998). For species of small

copepods over the inner and middle shelf areas, June abundances in 1999

were reduced by up to 90% compared to the two warmer years (Table 2.4)

(Coyle and Pinchuk, 2002b). Although Smith and Vidal hypothesized that

differences in predation on the copepods as well as temperature might have

affected the differences in abundance between 1980 and 1981, there was no

indication in the data of Coyle and Pinchuk (2002b) that chaetognaths were

responsible for the declines in copepod abundance observed in 1999. Coyle

and Pinchuk (2002b) provide compelling evidence that, even on a stationby-station basis, there was a strong relationship between the numbers of

copepods present and integrated water temperatures (e.g., Fig. 2.19). They

estimated that secondary production of calanoids in spring 1999 was about

3–4% that which occurred in the warm years of 1997–1998. Interestingly,

by August–September, there were no consistent significant differences in the

biomass of small copepods between 1999 and the two warmer years (Coyle

and Pinchuk, 2002b).
40 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Figure 2.18: Changes in zooplankton biomass in the deep basin and in the outer, middle, and coastal

domains of the southeastern Bering Sea sampled by the Oshoro Maru summer cruises. Data from 1994 to

1997 from Sugimoto and Tadokoro (1998). Data from 1995 to 1999 from Dr. N. Shiga (unpublished). Means

with standard errors. From Hunt et al. (2002a).
The Southeast Bering Sea Ecosystem—Part 2 41

Table 2.3: Responses of calanoid copepods to interannual variation in water temperature during the spring

bloom in the Bering Sea. Copepod data are numbers m−3 from the middle shelf in May 1980 and 1981.

Year

Variable 1980 1981 Difference

Onset of Bloom 25 April 5 May

Termination of Bloom 28 May 29 May

Temperature (◦C) Prior to Bloom—top 20 m 0.97 3.04 2.07

Temperature During Bloom—top 20 m 2.22 5.06 2.84

Acartia spp. All copepodids 18.9 8.5 –55%

Arcatia spp. Adult males and female 9.4 2.8* –70%

Pseudocalanus spp. All copepodids 83.1 308.5* +270%

Calanus marshallae All copepodids 31.7 30.6* –3.5%

Calanus marshallae Adult females 0.1 0.8* +700%

Metridea pacifica All copepodids 1.6 20.3* +1169%

Oithona spp. 269.6 233.4 –13.4%

* = difference significant p < 0.05. Data from Smith and Vidal (1986).

Table 2.4: Responses of calanoid copepods to interannual variation in water temperature during the spring

bloom in the Bering Sea. Copepod data are numbers m−3 from the middle shelf and inner shelf in June

1997, 1998, and 1999.

Change in 1999

Year from mean of

Variable 1997 1998 1999 1997 + 1998

Onset of Bloom Mid April Early May Late March

Temperature during June,

integrated water column ◦C 3.76 3.45 0.32* 3.29

Acartia spp. 961 711 64* –92%

Pseudocalanus spp. 1168 893 240* –77%

Calanus marshallae 34 72 3.7* –93%

Calanoid nauplii 616 626 322* –48%

Oithona similis 99 219* 28 –82%

Chaetognaths 43 12* 28 +1.8%

* = difference significant p < 0.05. Data from Coyle and Pinchuk (2002b) and Hunt et al. (2002a).
42 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Figure 2.19: June abundances of Acartia spp. (top) and Pseudocalanus spp. (bottom) in relation to the

integrated water temperatures at the stations where they were collected 1997–1999. Dashed lines are 95%

confidence intervals around the regressions. Coyle, unpublished data.
The Southeast Bering Sea Ecosystem—Part 2 43

Table 2.5: Comparison of acoustically determined euphausiid biomass among the years 1997, 1998, and

1999, mean and 95% confidence interval.

June 1997 June 1998 June 1999

Mean g m−2 Mean g m−2 Mean g m−2

Transect location (95% Cl) (95% Cl) (95% Cl)

Port Moller, Line A 2.3 (1.94–2.57) 0.77 (0.54–0.99) 17.27* (13.27–21.27)

Port Moller, Line C 2.98 (2.36–3.61) 0.34 (0.25–0.44) 15.76* (14.60–17.46)

Port Moller, Line E No Data 0.73 (0.62–0.83) 5.29* (4.30–5.65)

Cape Newenham, Line C 1.91 (1.14–1.70) 5.50* (4.71–6.22) 2.75 (1.59–3.91)

Nunivak Island, Line C 1.58 (1.46–1.70) 2.38 (2.26–2.49) 6.82* (5.91–7.72)

Nunivak Island, Line E 1.25 (1.13–1.36) 1.93 (1.62–2.19) 5.64* (4.89–6.02)

* = Statistically significant at p < 0.05. Data from Coyle and Pinchuk (2002a).

The effect of temperature on euphausiids appeared to be the inverse of

its effect on copepods; in 1999, the acoustically measured biomass of adult

euphausiids on the inner and middle shelf was significantly higher than in

1997–1999 (Table 2.5) (Coyle and Pinchuk, 2002a). However, Coyle and

Pinchuk point out that this difference may be related to a delay in euphausiid

breeding in the cold year, which would result in more adults remaining in the

water column when they were sampled in June than in a warm year when

most adults would have spawned and died prior to June. Coyle and Pinchuk

(2002a) noted that there were significantly higher densities of euphausiid

eggs and larvae present in 1999 (the cold year) compared to the warm years

of 1997 and 1998. Coyle and Pinchuk (2002a) concluded that there were

no significant differences in the biomass of euphausiids over the inner and

middle shelf in 1997 and 1998 compared to earlier periods for which data

were available. However, if euphausiid spawning is completed in early spring

and there is a reduced availability of late spawning adults in summer, this

change in the timing of availability could have a negative impact on predators, such as short-tailed shearwaters (Puffinus tenuirostris) that depend on

euphausiids for a significant portion of their diet (Baduini et al., 2001a,b;

Hunt et al., 2002b).

In the last three decades, gelatinous zooplankton, in particular large

scyphomedusae, have gone through a remarkable increase in biomass and

then crash (Fig. 2.20) (Brodeur et al., 1999a, 2002). The cause (or causes)

of the outbreak of jellyfish is not known, though it has been hypothesized

that changing climate and ocean temperatures may have been the trigger

(Brodeur et al., 1999a). It has also been hypothesized that a decrease in

forage fish over the southern portion of the shelf in the early 1980s may

have contributed to the jellyfish increase by releasing them from competition (Brodeur et al., 2002). Currently, there are no hypotheses to explain

why they suddenly decreased. At the Pribilof Islands, the dominant scyphozoan, Chrysaora melanaster, was estimated to consume about one third of

the standing stock of crustacean zooplankton and 4.7% of their annual production. Additionally, these jellyfish were estimated to consume about 3%

of the standing stock of age-0 pollock in the vicinity of the Pribilof Islands

(Brodeur et al., 2002).
44 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Figure 2.20: Biomass of large medusae in the National Marine Fisheries Service

bottom trawl surveys of the southeastern Bering Sea. Data courtesy of G. Walters

and R. Brodeur.

2.4.4 Fish

During the late 1970s and early 1980s, several stocks of groundfish and noncrab invertebrates showed strong changes in biomass in the eastern Bering

Sea (Conners et al., 2002). Conners et al. identified three sites that had been

surveyed consistently since about 1965 using comparable gear. Patterns of

change in biomass for all species combined were similar across these three

areas (Fig. 2.21), and timing of changes in both commercially exploited and

non-exploited species were similar. Pacific cod and several species of flatfish

showed changes of 300 to 600 percent, whereas the biomass of Greenland

turbot decreased by 90 percent (Fig. 2.22). These changes resulted in a

marked shift in the species composition of ground fish and benthic invertebrates in shelf waters. Conners et al. noted that the timing of the change in

CPUE in the trawls was consistent with the timing of the major regime shift

of 1976–1977, and that there was little evidence of responses to the later,

weaker, regime shifts in 1989 and 1998.

The female spawning biomass of the pollock stock increased strongly in

the 1980s because of the growth and survival of the strong year classes that

started in 1972 (Fig. 2.23). The biomass continued to be above the long-term

average during the 1990s because of the strong year-classes that followed the

exceptionally strong 1989 year-class. The increases were apparently due

to a combination of favorable ocean conditions and the conservative fishery

management practices put in place by the North Pacific Fishery Management

Council. The stock showed fluctuations as the young recruits grew in size

and were taken by the fishery or died through natural mortality (Fig. 2.22).
The Southeast Bering Sea Ecosystem—Part 2 45

Figure 2.21: Time series of bottom trawl surveys in three regions of the southeastern Bering Sea that have been consistently surveyed with similar gear since the

mid-1960s. From Conners et al. (2002).

Year-class success represents an annual estimate of productivity and survival for a species. During the period from 1963 to 2001, year-class strength

of walleye pollock in the eastern Bering Sea varied from a low of 3.6 billion

age-1 fish in 1963 to a high of 66.0 billion in 1978 (Fig. 2.23). Hollowed and

Wooster (1995) classified years into those warmer and colder than the longterm mean for the North Pacific Ocean from 1946 to 1990. They found that

during a warm period, the year-class strength of many stocks of groundfish

were stronger, while during a cool period, the same stocks showed weaker

year-class strength. Exceptionally strong year classes of pollock occurred in

10 years (Fig. 2.23, Table 2.6). There were two or three banner year classes

per decade with intermediate years showing average to weak year classes

(NPFMC, 2000).

Other species of groundfish in the eastern Bering Sea showed the same

general pattern as walleye pollock of increase in biomass during the early

1980s due to strong year classes following the 1976–1977 climate regime

shift. As examples (Fig. 2.22), Pacific cod (Gadus macrocephalus), yellowfin

sole (Limanda aspera), northern rock sole (Lepidopsetta polyxystra), Alaska

plaice (Pleuronectes quadritubeculatus), flathead sole (Hippoglossoides elassodon), arrowtooth flounder (Atheresthes stomias) and Greenland turbot

(Reinhardtius hyppoglossoides) follow this pattern (NPFMC, 2000). These

trends were due to the increased productivity that was manifest as year-class

success (Fig. 2.23).
46 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Figure 2.22: Spawner biomass of selected ground fish in the eastern Bering Sea. Data from NPFMC SAFE

(2000).
The Southeast Bering Sea Ecosystem—Part 2 47 Age 1 fish (billions)

Year of hatching

10

30

50

70

65 70 75 80 85 90 95

Walleye pollock

60 00

0

2

4

65 70 75 80 85 90 95

Yellowfin sole

60

1

Year of hatching

3

00

0

.4

.6

75 80 85 90 95

Arrowtooth flounder

70

.2

Year of hatching

60 65 00

0

1

2

3

75 80 85 90 95

Flathead sole

Year of hatching

60 65 70 00

0

20

40

70 75 80 85 95

Greenland turbot

Year of hatching

60

80

100

60 65 90 00

0

.5

1

70 75 80 85 95

Alaska plaice

Year of hatching

1.5

2

2.5

90

3.0

60 65 00

Age 3 fish (billions) Age 2 fish (billions) Age 3 fish (billions)

Year of hatching

.4

.8

1.2

1.6

80 85 90 95

Pacific cod

0 60 65 70 75 00

Age 1 fish (billions)

2

4

6

75 80 85 90

Rock sole

Year of hatching

0

60 65 70 95 00

Age 3 fish (billions) Age 1 fish (millions) Age 1 fish (billions)

Figure 2.23: Year-class strength for selected species of groundfish in the eastern Bering Sea. Data from

NPFMC SAFE (2000).
48 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Table 2.6: Strong year classes (≥20% above the mean from 1975 to 1999) and weak year classes (≤20%

below the mean) by species in the Bering Sea. Years not shown had average year classes (mean ±20%).

Data from NPFMC, 2000.

Group Strong Year Classes Weak Year Classes

Group A

Walleye Pollock 77, 78, 79, 80, 84, 89, 92, 96 75, 76, 81, 83, 85, 87, 88, 91, 93, 94, 98, 99

Alaska Plaice 75, 76, 77, 78, 79, 80, 82, 88, 90 83, 84, 85, 86, 87, 94, 95, 96, 97

Yellowfin Sole 75, 76, 79, 81, 83, 91 78, 82, 84, 85, 86, 89, 90, 94

Flathead Sole 77, 79, 80, 81, 84, 85, 87 75, 76, 88, 90, 91, 92, 93, 94, 95, 96, 97

Pacific Cod 77, 78, 79, 82, 84, 89, 92 81, 83, 86, 87, 88, 91, 93, 94, 95, 97, 98, 99

Group B

Rock Sole 80, 81, 83, 84, 85, 86, 87, 88, 90 75, 76, 77, 78, 79, 91, 92, 94, 95

Arrowtooth Flounder 80, 81, 83, 84, 86, 87, 88, 91 75, 76, 78, 79, 82, 93, 94, 96, 97

Greenland Turbot 75, 76, 77, 78, 79 81, 82, 83, 86, 87, 88, 89, 91, 92

Two alternative recruitment patterns seem to have emerged. There are

those species that show strong year classes in the late 1970s and early 1980s,

and there are species with a delayed run of strong year classes beginning

in the late 1980s and continuing through the early 1990s (Table 2.6). Both

patterns show a reduction of year-class strength in the late 1990s. Fishes

with strong year-class productivity in the late 1970s and 1980s include Pacific

cod, yellowfin sole, flathead sole, and Alaska plaice. Fishes without strong

recruitment in the 1970s, but having strong year classes in the 1980s, are

rock sole and arrowtooth flounder (Fig. 2.23). Greenland turbot are unique

in that they had strong year classes in the late 1970s, but not in the 1980s

and 1990s. There are years that stand out as being characterized as having

coincident strong or weak year classes among eight species of groundfish

(Table 2.6). The years during which over half of the species had strong year

classes were 1977 (5/8), 1979 (6/8), and 1984 (5/8). Two years had weak

year classes for over half of the species, 1986 (5/8) and 1994 (7/8).

An important difference between walleye pollock and other groundfish is

the continued high biomass levels of walleye pollock in the Bering Sea in the

1990s, which contrasts with patterns of decline for many other economically

important species (Fig. 2.22). Walleye pollock is unique in its continued

production of young through the decade of the 1990s (Fig. 2.23, Table 2.6).

The pattern of changes in biomass available to the fisheries has followed

the availability of recruits, not the catch of the fishery. However, these stock

changes greatly affected the fishery landings and sustainable yields. The

most notable series of stock increases came after the 1976–1977 regime shift

that resulted in favorable ocean conditions north of the state of Washington.

During the 1980s, simulations showed that wind-driven surface currents in

the southeastern Bering Sea were mostly eastward, and flatfish as well as

pollock enjoyed high levels of recruitment (Wilderbuer et al., 2002). However, in the 1990s, surface currents were westerly (seaward), and flatfish

recruitment was weaker. For the flatfish, this would have resulted in advection to favorable nursery grounds (Wilderbuer et al., 2002); for pollock,
The Southeast Bering Sea Ecosystem—Part 2 49

advection into Bristol Bay would result in separation of larvae and juveniles

from cannibalistic adults (Wespestad et al., 2000).

The late 1970s and early 1980s were also a period of major change in

the management of the fisheries. In 1977, the extended fishery jurisdiction

of the United States and Canada came into force. International fishing

was severely curtailed and fishery management became progressively more

conservative. There was a transition period of “joint venture” fishing during

the late 1970s and early 1980s, after which only domestic fishing was allowed.

Also, because of the cooperative interaction between the International Pacific

Halibut Commission and the North Pacific Fishery Management Council,

the catch of cod and flatfish has been conservative. Thus, fishery takes

have affected the total biomass of groundfish in the eastern Bering Sea,

and patterns in change appear to be driven by ecological determinants of

recruitment.

Another group of species that are of great importance to both the commercial sector and to subsistence users are the Pacific salmon. The world’s

largest runs of wild sockeye salmon (Oncorhynchus nerka) occur in Bristol

Bay (USGS, 2004), and important runs of other species occur in most of

the drainages of western Alaska. Several of the runs showed remarkable

increases in the 1970s (e.g., Yukon River chum [O. keta] and Bristol Bay

sockeye), but returns from these and other stocks in western Alaska plunged

precipitously in the late 1990s (Fig. 2.24). The reasons for these declines are

not apparent, but the most likely hypotheses focus on ocean survival (Kruse,

1998). In recent years, the ocean ecology of salmon in the eastern Bering

Sea has received little attention, and we have few data on their impact on

the ecosystem there (e.g., Nishiyama, 1974), nor on factors that might affect

their ocean ecology. Work recently begun by the North Pacific Anadromous Fish Commission (the BASIS program, NPAFC, 2001), will begin to

remedy this lack of information by completing an international survey of

Bering-Aleutian salmon.

In addition to work on the commercially important groundfish and salmon

species, there have also been significant gains in our knowledge of the distribution, abundance, and ecology of forage fishes. These fish include both

small species, such as sand lance (Ammodytes hexapterus), capelin (Mallotus villosus), Pacific herring (Clupea pallasii), smelts, and mesopelagic groups

such as Myctophids (lanternfish) and Bathylagidae (deep-sea smelts), as well

as the young of larger species such as age-0 and age-1 walleye pollock. These

species are eaten by large fish, including Pacific cod, Pacific halibut (Hippoglossus stenolepis) and walleye pollock, and are also important components

of the diets of marine mammals and seabirds. Brodeur et al. (1999b) summarized data collected in 1987 in a joint program with Russian colleagues

that resulted in a pair of surveys that covered most of the Bering Sea. They

found that age-0 and age-1 pollock were more abundant on the eastern shelf

than elsewhere in the Bering, that herring and capelin were most abundant

on the northern and western shelves. They also compared the distribution of

various species, as sampled in the National Marine Fisheries Bottom Trawl

Surveys, in a warm year (1987) and in a cold year (1986). They found that
50 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

200

150

100

50

Yukon River Chinook Salmon

0

Bristol Bay Sockeye Salmon

Catch (millions of fish)

50

30

20

10

40

0

1890 1910 1930 1950 1970 1990

Year

1910 1930 1950 1970 1990

Year

Yukon River Summer Chum Salmon

.5

1.5

1.0

2.0

0

1960 1970 1980 1990

Year

1965 1975 1985 1995

Catch (thousands of fish)

Figure 2.24: Time-series for three western Alaska salmon runs. From Kruse

(1998).

some forage fishes, such as age-1 pollock, avoided regions with cold bottom

temperatures, whereas others did not (e.g., capelin).

A group of little-studied forage species that deserve particular attention

are the mesopelagic fish and squid, which are important forage for upper

trophic level consumers including large fish, marine mammals, and seabirds.

Many mesopelagic species migrate into the upper mixed layer each night

to forage on euphausiids and copepods, returning to depth during the day.

This behavior provides a mechanism for rapid downward transport of production from the upper ocean. One species, Stenobrachius leucopsarus, perhaps the ecologically most important, may have a biomass of 21 million tons

in the North Pacific sub-arctic gyres (including the Bering Sea and the Sea

of Okhotsk) (Beamish et al., 1999a). Sinclair et al. (1999) assembled data on
The Southeast Bering Sea Ecosystem—Part 2 51

mesopelagic fishes and squids from the Bering Sea to provide a description

of the species composition, distribution, and relative abundances of members of this important fauna. They noted that, of the squids, members of

the families Gonatidae and Cranchiidae appeared in the highest numbers

in both trawl surveys and predator diets, while of the fishes, Myctophidae

and Bathylagidae were most common. Sinclair and Stabeno (2002) sampled basin waters near the edge of the southeastern Bering Sea shelf. They

found unexpectedly high concentrations of biomass, driven primarily by the

deep-sea smelt Leuroglossus schmidti. Their work revealed a new species of

gonatid, a range extension for the snailfish (Paraliparis paucidens), and new

size records for several species of fish and squid. Discoveries of this nature

demonstrate that this ecologically important group of organisms has been

neglected. Given their large biomass compared to that of most fish species,

we must devote more effort to learning about them. Because of mesopelagics’ strongly dial cycles of behavior, study of their ecological role will require

examination of trophic relations both during the daylight and at night.

The waters around the Pribilof Islands have been thought to provide

a uniquely productive habitat for juvenile pollock because of the potential

for enhance production associated with frontal processes there. During the

1990s, several projects examined the ecology of age-0 walleye pollock there.

These are referenced in Section 2 (Pribilof Islands) of this report and will

not be discussed here.

2.4.5 Marine mammals and seabirds

Dramatic population declines of pinnipeds and seabirds in the eastern Bering

Sea and Gulf of Alaska since the mid 1970s contrast with their stability elsewhere in the North Pacific Ocean. Previous studies have proposed a combination of natural (climatic) and anthropogenic (fisheries) factors that may

have altered the structure of the food web in the eastern Bering Sea (Piatt

and Anderson, 1996; NRC, 1996; Merrick, 1997; Springer, 1998; Anderson

and Piatt, 1999; Hunt et al., 2002a) and thus affected population stability of

apex predators. Although the mechanisms of change unique to the Bering

Sea are not fully understood (Hunt and Byrd, 1999; Hunt et al., 2002a),

changes in the populations of marine birds at the Pribilof Islands are likely

the result of declines in prey availability, as there is little evidence that these

seabird species are subject to significant levels of predation.

Marine mammal populations. Approximately 80% of the world

population of northern fur seals (Callorhinus ursinus) breeds on the Pribilof

Islands where population estimates have been conducted since the turn of

the century, and with consistent methodology for the past 60 years (York

and Hartley, 1981). There are currently an estimated 800,000 animals remaining from an estimated population of 2 million as recently as the 1950s.

The species is listed as depleted under the Marine Mammal Protection Act

(NMFS, 1993). Unlike most other apex predators in the Bering Sea, the

pattern of decline among northern fur seals is one of steady to severe reductions in population numbers followed by some years of stability (Fig. 2.25). A

steep decline began in the mid 1970s that was the effect of either reduced pup
52 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Fur seal pups St. Paul Island, 1970–2000

Fur seal pups St. George Island, 1970–2000

300

200

100

65

50

35

20

Thousands of Pups Thousands of Pups

1970 1975 1980 1985 1990 1995 2000

1970 1975 1980 1985 1990 1995 2000

Year

Year

Figure 2.25: Time series of pup counts at St. Paul Island and St. George Island,

Pribilof Islands. From NMFS (2002).

production or decreased juvenile survival during their pelagic phase (York

and Kozloff, 1987). Declines continued into the mid-1980s, after which the

St. Paul Island population remained stable at 33% of its 1970s population,

while the smaller population of St. George Island stood at 60% of its 1970s

level and continued to decline incrementally into the 1990s (York et al., 2000;

Loughlin et al., 1994). The most recent population count in 2002 indicates

that an annual decline of 5% has been occurring on both islands since 1998

(NMFS, 2002).

Steller sea lions (Eumetopias jubatus) currently are divided into two

stocks. The western stock ranges from 144◦W in the Gulf of Alaska westward
The Southeast Bering Sea Ecosystem—Part 2 53

0

1000

2000

3000

4000

5000

1960 1970 1980 1990 2000

Individuals

0

100

200

300

400

Individuals

Figure 2.26: Changes in the number of adult (left scale, filled dots) and pup

(right scale, open dots) Steller sea lions counted on Walrus Island, Pribilof Islands.

Data from National Marine Fisheries Service, National Marine Mammal Laboratory,

Seattle, Washington. From Hunt et al. (2002a).

across the Aleutian Island chain and includes Walrus Island in the Pribilof

Island group. After a continual population decrease of 5% per year since the

1970s, the monitored portion of the stock in the Gulf of Alaska and along

the Aleutian Islands chain showed an increase of 5% between 2001 and 2002.

The population of Steller sea lions on Walrus Island (Pribilof Island group)

has been in steady decline since the 1960s and is considered a nearly extinct

rookery (Fig. 2.26). The western stock of Steller sea lions is currently listed

as endangered under the U.S. Endangered Species Act. The causes of the

decline in the western stock of Steller sea lions remain controversial (NRC,

2003).

Populations of large cetaceans in the eastern Bering Sea were greatly

reduced by commercial whaling that continued through about 1970 (NRC,

1996). In contrast to declines in pinniped and seabird populations (see below), populations of large baleen whales, which were almost extirpated in

shelf waters of the eastern Bering Sea by the 1960s (NRC, 1996; Merrick,

1997), may be increasing. Although in the mid 1970s it was rare to encounter any large cetacean over the eastern Bering Sea shelf (Leatherwood

et al., 1983; Brueggerman et al., 1987), since the 1980s, whales have been

encountered over shelf waters with increasing frequency (Baretta and Hunt,

1994). Significant numbers of large cetaceans are now present in shelf waters

during summer (Tynan, 1999; Moore et al., 2000, 2002).

Pinniped diets. The composition of the diet of northern fur seals in

the Bering Sea has been inferred from material obtained from stomachs of

animals shot at sea, from stomach contents and vomits of animals on land,

and most recently from analysis of hard parts remaining in scats (feces)

(Sinclair et al., in preparation). Although not completely comparable because of differences in the likelihood of detection of different dietary items,

the time series for fur seals provide evidence of significant changes in the

composition of their diets over the past forty years (see Hunt et al., 2002a

for a review of earlier work). Most significantly, in the 1960s, cold-water

species such as capelin and Greenland turbot were important, whereas by
54 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

the 1980s, these had disappeared from fur seal diets at the Pribilofs, and

diets were dominated by pollock ≤2 years of age.

During the 1990s, analyses of scats on St. Paul and St. George Islands

revealed that fur seals there take at least four species of squid and approximately 30 species of fish. Primary prey (those species that occurred with

a frequency of 10% or higher on either island, in any single year) included

the four species of squid and five species of fish. On St. Paul Island, only

walleye pollock ranked above 10% for all sample years combined. In all years

combined on St. George Island, pollock, salmon, and the gonatid squid Gonatopsis borealis/Berryteuthis magister ranked as primary prey items. Since

1988, the number of northern fur seals on the Pribilof Islands consuming

squid has decreased, while the number consuming walleye pollock (Theragra

chalcogramma) and northern smoothtongue (Leuroglossus schmidti) on both

islands, and Pacific salmon (Salmonidae) on St. George Island has increased

(Fig. 2.27). While northern fur seals have relied primarily on juvenile pollock

(ages-0 and -1) and to a lesser degree age-2+ since the 1980s (Sinclair et al.,

1994, 1996; Antonelis et al., 1997), in the 1990s, there has been an increase

in the use of age-3+ pollock, and possibly a decrease in the consumption of

age-0 pollock, particularly on St. Paul Island (Fig. 2.28). Over the decade of

the 1990s, fur seals on St. George have had a somewhat higher consumption

of age-3+ pollock compared to those on St. Paul Island, which is in line with

the greater densities of adult pollock around St. George Island relative to

St. Paul (NMFS, 1993).

Adult fur seals are typically specific to summer feeding locations (Robson,

2002) depending upon the rookery of their birth; adult females from St.

George Island can be generally characterized as off-shelf feeders compared

to those from St. Paul Island (Sinclair et al., 1996; Goebel, 2002). Off-shelf

feeding is indicated by consumption of salmon, northern smoothtongue, and

the Gonatid squid Gonatopsis borealis/Berryteuthis magister.

Scats collected from Steller sea lions on Walrus Island in 1994 (n = 43)

and 2001 (n = 44) contained 11 species (or taxonomic groups) of fish prey

that ranked above 10% in frequency of occurrence (FO) in one or both years.

Irish Lord (Hemilepidotus hemilepidotus), Pacific cod, walleye pollock, and

rock sole ranked above 10% FO in both sample years. With the exception

of salmon, all the species consumed by Steller sea lions on Walrus Island are

demersal or semi-demersal in habit. Both juvenile and adult pollock were

present in Steller sea lion scats with an emphasis of age 5+ in year two.

Seabird populations. During the 1970s and early 1980s, the abundance of piscivorous seabirds breeding at the Pribilof Islands declined (Figs.

2.29, 2.30) (Decker et al., 1995; Springer, 1998; Dragoo et al., 2001). Kittiwake (Rissa spp.) and common murre (Uria aalge) declines were especially

severe on St. Paul Island during the 1970s and 1980s. During that time,

common murres became more abundant on St. George Island. It is unclear

why population declines in murres were more severe on St. Paul Island than

on St. George Island.

Schmutz (Appendix 1 in Sinclair et al., in preparation) partitioned the

causes of kittiwake and murre population changes on the Pribilof Islands

between reproductive success (productivity) and other factors that include
The Southeast Bering Sea Ecosystem—Part 2 55 080604020 100 1989 91 93 95 97 99 SNG - Pollock y = 0.9459x - 1827.4 R2= 0.0918 SNG - Salmon 080604020 100 1989 91 93 95 97 99 y = 1.0187x - 2016.5 R7 = 0.3707 SNG - Atka mackerel 6040200 1989 91 93 95 97 99 y = -0.059x + 119.86 R2= 0.0344 SNG - N.Smoothtongue 0604020 1989 91 93 95 97 99 y = 1.0289x - 2045.7 R2= 0.539 080604020 100 1989 91 93 95 97 SNP - Pollock y = 1.5661x - 3053.4 R2 = 0.3973 99 SNP - Salmon 080604020 100 1989 91 93 95 97 99 y = 0.3147x - 620.26 R2 = 0.0764 SNP - N.Smoothtongue 0604020 1989 91 93 95 97 99 y = 0.444x - 883.38 R2= 0.55 SNP - Atka mackerel 0604020 1989 91 93 95 97 99 y = 0.3786x - 751.89 R2= 0.1086

Percent Frequency of Occurr ence

Percent Frequency of Occurr ence

SNG - Sandlance

0

60

4020

1989 91 93 95 97 99

y = -0.5146x + 1088.9

R2= 0.3534

SNP - Sandlance

0

60

40

20

1989 91 93 95 97 99

y = -0.176x + 359.11

R2= 0.0202

SNG - Herring

0

60

4020

1989 91 93 95 97 99

y = -0.4264x + 853.15

R2= 0.253

SNG - Gm/Gm

0

60

4020

1989 91 93 95 97 99

y = -0.9598x + 1920.3

R2= 0.4321

SNP - Herring

0

60

4020

1989 91 93 95 97 99

y = -0.1582x + 320.99

R2= 0.0156

SNP Gm/Gm

0

60

4020

1989 91 93 95 97 99

y = -1.3034x + 2606.4

R2= 0.6629

SNG - Gb/Bm

0

60

4020

1989 91 93 95 97 99

y = -0.5382x + 1095.8

R2= 0.1127

SNP - Gb/Bm

0

604020

1989 91 93 95 97 99

y = 0.1417x + 277.39

R2 = 0.072

Percent Frequency of Occurr ence

Percent Frequency of Occurr ence

Figure 2.27: Changes in prey composition for northern fur seals on the Pribilof Islands in the late 1980s and 1990s. Gm/Gm: “shelfsquid,” one or both of two species Gonatus middendorffi and/or G. madokai. Gb/Bm: “slope squid” Gonatopsis borealis and/or Berryteuthismagister. SNP = St. Paul and SNG = St. George. From Sinclair et al. (in preparation).
56 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

0

20

40

60

80

100

1988 90 92 94 96

SNP Age-0

y = -1.5261x + 3115

R2

 = 0.239

98

0

20

40

60

80

100

1988 90 92 94 96

SNG Age-0

y = -1.2424x + 2538.8

R2 = 0.0578

98

0

20

40

60

80

100

1988 90 92 94 96

SNG Age-1

y = 2.0223x - 4007.2

R2

 = 0.126

98

0

20

40

60

80

100

1988 90 92 94 96

SNP Age-1

y = -0.409x + 860.09

R2

 = 0.0109

98

0

20

40

60

1988 90 92 94 96

SNG Age-3,4,5

y = 1.2453x - 2443.3

R2

 = 0.0398

98

0

20

40

60

1988 90 92 94 96

SNP Age-3,4,5

y = 2.5054x - 4974.9

R2

 = 0.4886

98

Percent Frequency of Occurr encePercent Frequency of Occurr ence

Figure 2.28: Changes in the age composition of walleye pollock in the diets of northern fur seals on the

Pribilof Islands in the late 1980s and 1990s. Data from Sinclair et al. (in preparation).

Population Trends

Black-legged Kittiwake, SP

Population Trends

Black-legged Kittiwake, SG

Population Trends

Red-legged Kittiwake, SP

Population Trends

Red-legged Kittiwake, SG

(A) (B)

(C) (D)

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0 1975 1980 1985 1990 1995 2000 1975 1980 1985 1990 1995 2000

1975 1980 1985 1990 1995 2000 1975 1980 1985 1990 1995 2000

Percent of Maximum Percent of Maximum

Percent of Maximum Percent of Maximum

Figure 2.29: Population trends in black-legged kittiwakes and red-legged kittiwakes on the Pribilof Islands.

From Dragoo et al. (2001).
The Southeast Bering Sea Ecosystem—Part 2 57

Population Trends

Common Murre, SP

Population Trends

(A) (B) Common Murre, SG

Population Trends

Thick-billed Murre, SP

Population Trends

(C) (D) Thick-billed Murre, SG

1975 1980 1985 1990 1995 2000

1975 1980 1985 1990 1995 2000

1975 1980 1985 1990 1995 2000

1975 1980 1985 1990 1995 2000

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

Percent of Maximum Percent of Maximum

Percent of Maximum Percent of Maximum

Figure 2.30: Population trends of common and thick-billed murres on the Pribilof

Islands. From Dragoo et al. (2001).

post-fledging mortality and emigration. He found that for the four species

of kittiwakes and murres, productivity accounted for one-third (34%) of the

variation in population change. The majority (66%) of variation in population growth rate was attributable to mortality and emigration, but distinction between these two demographic processes was not possible for most

species due to lack of data. However, other survival and population data for

the geographically restricted red-legged kittiwake (Rissa brevirostris) indicated that, for this species, variations in survival have likely been a principal

demographic force.

Marine bird diets. Sampling of marine bird diets has shown that

there are consistent, significant differences in diet components between seabirds

nesting on St. George and St. Paul islands, and that, since the mid 1970s,

there have been significant changes of diet components for seabird species

nesting on St. George and St. Paul islands (Sinclair et al., in preparation).

During the 1970s and 1980s, black-legged kittiwakes (R. tridactyla) and common and thick-billed murres (U. lomvia) used more invertebrates (e.g., euphausiids, squid) on St. George than on St. Paul Island. Similarly, during

the 1990s, the proportion of black-legged kittiwake and thick-billed murre

diets consisting of invertebrates was higher on St. George than on St. Paul

Island. During the 1970s and 1980s, there were also differences in the proportions of four species of forage fishes used (Sinclair et al., in preparation).

Capelin composed a higher proportion of kittiwake diets on St. Paul Island

than on St. George Island. Sand lance were a larger proportion of murre

diets on St. George Island than on St. Paul Island. During the 1990s, all

four seabird species took a higher percentage of gadids on St. Paul Island

than they did on St. George Island.

The differences in diets between the islands are important. They indicate

that kittiwakes and murres on St. George have had access to a wider variety

of prey, in particular, invertebrates including squids and euphausiids, than

seabirds on St. Paul Island. And, seabirds on St. Paul Island have been more

dependent on juvenile gadids and capelin than birds on St. George Island
58 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

where seabirds apparently had more ready access to a wider variety of forage

fishes including sand lance and myctophids.

During the 1970s and 1980s, gadids (mainly walleye pollock) were the

primary component of black-legged kittiwake, common murre, and thickbilled murre diets. Red-legged kittiwakes took mainly lanternfish. There

was a significant decline between the 1970s and 1980s in the proportion of

gadids in kittiwake diets on St. Paul, but not St. George Island (Fig. 2.31),

and in thick-billed murre diets on St. George Island. Likewise, the diet of

red-legged kittiwakes during the early season contained a higher proportion

of gadids during the 1970s than they did during the 1980s.

Seabird reproduction in an ecosystem context. Sinclair et al. (in

preparation) evaluated the spatial scale at which causal factors appeared

to have influenced demographic parameters in Pribilof Islands’ seabirds. If

population declines were related to processes that operate on large (regional)

spatial scales, such as prey availability over the shelf, then seabird reproductive success and colony attendance should have been correlated between St.

Paul and St. George Islands. If populations responded to processes that operated on small (local) spatial scales, such as prey availability associated with

oceanographic features near an island, then seabird demography should have

differed between the islands. Because colony attendance and reproductive

success of black-legged kittiwakes and red-legged kittiwakes were correlated

between populations on St. Paul and St. George Islands, as were interannual variations in the diets of these species on the two islands, Sinclair et

al. (in preparation) concluded that population dynamics in kittiwakes were

influenced by processes that occurred over large spatial scales (shelf-wide),

rather than at the local (island) level. In contrast, for both common and

thick-billed murres, interannual variability in colony attendance, and reproductive success in thick-billed murres, were not correlated between islands,

suggesting that the sizes of populations of two murres were influenced by

factors that operated on a local scale.

At about the time of the 1976–1977 regime shift, and concurrent with

the declines in piscivorous pinnipeds and seabirds at the Pribilof Islands,

there were significant decreases in the biomass of age-1 walleye pollock on

the Bering Sea shelf (Brodeur et al., 1999b; Hunt et al., 2002a; Sinclair et

al., in preparation). In some areas, abundance decreased by as much as 95%

(Springer, 1992; Sinclair et al., 1996; Brodeur et al., 1999b; Hunt et al.,

2002a). Likewise, there is evidence for significant declines in the abundance

of capelin, an important forage fish for seabirds (Hunt et al., 1996a). Because

seabirds whose populations declined had diets consisting primarily of age1 pollock (Springer, 1992; Decker et al., 1995; Hunt et al., 1996c, 2002a),

and the proportion of these declined in their diets, it was hypothesized that

declines in the abundance of age-1 pollock and other forage fishes reduced

the foraging success and, consequently, reproductive success and population

size of kittiwakes and murres at the Pribilof Islands (Hunt and Byrd, 1999).

A shift from use of age-1 to age-0 pollock during this period (Hunt et al.,

1996b) would have been particularly challenging, as age-0 pollock are both

smaller (about 2 g) than age-1 pollock in spring (about 19 g) and of lower

energy density (about 2% fat vs. about 5% fat) (Iverson et al., 2002).
The Southeast Bering Sea Ecosystem—Part 2 59

Black-legged Kittiwakes

St. Paul Island, Late Season

Black-legged Kittiwakes

St. George Island, Late Season

Red-legged Kittiwakes

Both Islands, Late Season

Sand lance

Lanternfish

Capelin

Gadid

Sand lance

Lanternfish

Capelin

Gadid

Sand lance

Lanternfish

Capelin

Gadid

Mean % Biomass or Volume Mean % Biomass or Volume Mean % Biomass or Volume

100

80

60

40

20

0

100

80

60

40

20

0

100

80

60

40

20

0

1975 1978 1981 1984 1987 1990 1993 1996 1999

1975 1978 1981 1984 1987 1990 1993 1996 1999

1975 1978 1981 1984 1987 1990 1993 1996 1999

88

72

77

47

21

40

5

26

18

31

53 47

25

23

7

9 9

3

9

5

31 10

28 9

11

10

Figure 2.31: Changes in the species composition of the diets of black-legged and red legged kittiwakes at

the Pribilof Islands, 1975–1999. Data from Sinclair et al. (in preparation).
60 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

The decreases in at least black-legged kittiwakes may be related to an

increase in adult pollock on the shelf and competition with these fish for

age-1 pollock and other forage fish. The biomass of adult (age 3+) walleye pollock on the Bering Sea shelf increased during 1976–1999 (r2 = 0.30,

P = 0.006, n = 24), with the steepest increase in their biomass occurring

during 1979–1982 (Sinclair et al., in preparation). The annual variability

in reproductive success in black-legged kittiwakes was negatively correlated

with the biomass of adult walleye pollock on the Bering Sea shelf (r = –0.68,

p <0.001) (Livingston et al., 1999; Hunt and Stabeno, 2002; Sinclair et al.,

in preparation). There was no correlation between reproductive success in

red-legged kittiwakes (r = –0.35, p = 0.160), common murres (r = 0.18,

p = 0.626), or thick-billed murres (St. Paul: r = –0.419, p = 0.136; St.

George: r = 0.19, p = 0.440) and the biomass of adult walleye pollock on

the shelf (Sinclair et al., in preparation).

Prey availability and seabird mortality at the Pribilof Islands.

Although Sinclair et al. (in preparation) found little correlation between juvenile pollock abundance on bottom trawl surveys and seabird reproductive

success on an interannual basis, changes in the availability of young pollock (and possibly other prey) to seabirds during the late 1970s probably

did result in poor foraging conditions for seabirds. Inter-colony comparisons suggest that seabird productivity at the Pribilof Islands is limited by

the availability of food. For example, since the mid 1970s, the long-term

(1976–1999) mean reproductive success of black-legged kittiwakes on St.

Paul Island is 0.28 chicks nest−1 year−1, and on St. George Island, it is

0.22 chicks nest−1 year−1. In contrast, mean reproductive success over the

same period for black-legged kittiwakes at Cape Lisburne, northern Bering

Sea, is 0.80 chicks nest−1 year−1, and in some areas, black-legged kittiwakes

raise two or more chicks per year. At the Pribilofs, kittiwakes raise at most

one (Dragoo et al., 2001). Likewise, reproductive success is also low among

murres at the Pribilof Islands relative to murres at other colonies in the North

Pacific. Since the mid 1970s, common murres on St. Paul Island fledged an

average of 0.53 chicks nest−1 year−1, and, on St. George Island, the average is

0.52 chicks nest−1 year−1. In contrast, common murres at Cape Thompson,

in the northern Bering Sea, fledged an average of 0.70 chicks nest−1 year−1.

At Kodiak Island (E. Amatuli), in the Gulf of Alaska, they fledged an average

of 0.71 chicks nest−1 year−1 (Dragoo et al., 2001). The ability of kittiwakes

and murres at other colonies to consistently raise more young than they do

at the Pribilof Islands suggests that competition for food is greater around

the Pribilofs or foraging conditions around the Pribilofs are poor relative to

other regions in the Bering Sea.

If prey availability to seabirds at the Pribilof Islands is relatively low

during the breeding period, as suggested in the preceding discussion, reproduction may be an especially stressful time for Pribilof seabirds. Kitaysky

and colleagues (1999a,b, 2000, 2001a) demonstrated the exacerbating effect that a limited food supply can have on acute stress to seabirds during

the reproductive phase. Kitaysky et al. (1999b) found that adult blacklegged kittiwakes breeding at a colony in Cook Inlet, Alaska, had elevated

corticosterone levels during reproduction, and that stress levels were nega-
The Southeast Bering Sea Ecosystem—Part 2 61

tively associated with food availability near the breeding colony (Kitaysky

et al., 1999b). In addition, corticosterone concentrations in birds increased

throughout the breeding season, indicating that stress can become increasingly severe for seabirds as they progress from the egg-laying to the chickrearing periods (Kitaysky et al., 1999b). Using captive kittiwakes, Kitaysky

et al. (1999a, 2001a) showed that food scarcity and food of poor nutritional

quality increased circulating concentrations of a stress-response hormone,

corticosterone, in black-legged and red-legged kittiwake chicks. Corticosterone indicates catabolic activity of metabolic pathways, signaling reliance

by seabirds on endogenous energy reserves.

Increasing levels of corticosterone in plasma may affect not only the behaviors of the birds involved, but also their probability of survival. Kitaysky

et al. (2000) showed experimentally that adult black-legged kittiwakes with

elevated levels of corticosterone had significantly lower probability of returning to their colonies over the 2 years subsequent to implantation of corticosterone than did birds with shame operations. These results support the

hypothesis that when adults have to work hard to raise chicks, either because

food may be in short supply (Kitaysky et al., 2000), or because the begging

of hungry chicks forces them to work harder (Kitaysky et al., 2001b), adult

survival may be compromised (Golet et al., 1998; Golet and Irons, 1999;

Kitaysky et al., 2001b). In the laboratory, Kitaysky et al. (2003) showed

that young red-legged kittiwake chicks with elevated levels of corticosterone

resulting from implants were less able at learning to find food than were

birds that were shame-operated. Here the implication is that young kittiwakes that have had less food than optimal while growing may have difficulty

learning to feed themselves once fledged and independent. These birds would

be exceptionally vulnerable to starvation during the first few months of independence. There is considerable evidence that young seabirds have a more

difficult time catching prey than adults. Learning deficits imposed by lack

of adequate feeding in the nest and the consequent high stress levels would

exacerbate this situation.

Thus, mortality among seabirds at the Pribilof Islands may have varied

with the abundance of prey near the islands and may have been most severe

during the post-breeding period, when deaths were difficult to observe. By

that time, adults would have spent a considerable amount of energy on

reproduction and fledglings would have had to learn to forage for themselves

or perish. Storm activity is also greater during the post-breeding period

(i.e., autumn), and inclement weather can have both direct and indirect

implications for chick survivorship and adult mortality, as stormy weather

impairs foraging by some seabirds. At the Pribilof Islands, Braun and Hunt

(1983) found that in black-legged kittiwakes on St. Paul Island, chick loss

due to ejection of second-hatched young by an older sibling happened more

often than expected by chance during inclement weather. Work by others

has shown that these behaviors result from the older chick being unable to

satisfy its nutritional needs (Mock et al., 1990; Irons, 1992).

Inability to obtain adequate prey has also affected migrant, non-breeding

species in the Bering Sea. Each year millions of short-tailed shearwaters

(Puffinus tenuirostris) migrate from Australia to the Bering Sea to forage
62 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

over the inner shelf. The evolution of this annual trans-equatorial migration

implies that usually extraordinary amounts of prey must be readily available

to these birds in the Bering Sea. During summer and fall of 1997, possibly

as many as 10% of the 16 million short-tailed shearwaters present in the

southeastern Bering Sea died (Vance et al., 1998; Baduini et al., 2001a,b).

Between 1 August and 1 September, reports of moribund and dead shearwaters came from both sides of the Alaska Peninsula, the Aleutian Islands as far

west as Adak, Bristol Bay, the Bering Sea north to Cape Anadyr (Russia),

and the eastern Chukchi Sea. At least several hundred thousand shearwaters

washed ashore. Compared with previous large-scale seabird die offs in the

Bering Sea (Bailey and Davenport, 1972; Oka and Maruyama, 1986; Hatch,

1987; Piatt and Van Pelt, 1997), the 1997 event in the southeastern Bering

Sea was probably one or two orders of magnitude larger (105–106 vs. 104

birds dying). Apparently they were unable to obtain sufficient of the usual

prey, euphausiids, in 1997. In 1998, shearwaters were again emaciated, but

few died, perhaps because the summer was windier, and they needed less

energy to fly between places where food was obtainable. Their diets also

shifted, with a greater proportion of fish and zooplankton being taken than

was true in the same region in earlier years (Hunt et al., 2002b).

In spring 1998, shearwaters had lower body masses than in spring 1997.

Because the seasonal progression of stratification, bloom, and euphausiid

mating occurred earlier in 1997, foraging for shearwaters arriving in May

1997 was apparently good, and birds were fat in June. In contrast, in 1998,

the seasonal progression was late, and euphausiids were only beginning to

form mating swarms in June. Consequently, shearwaters did not put on

the level of fat reserves seen in June 1997. Perhaps because euphausiid

mating came early in 1997, by late July, few near-surface swarms of mating

euphausiids were available to shearwaters. If we are correct in our assessment

that euphausiid maturation was delayed in 1999, and that the biomass of

euphausiids was large compared to 1997, then swarms should have been

available late into the summer and late-season body mass of shearwaters

should be high. In the cold spring of 1999, euphausiids were abundant

well into summer (Coyle and Pinchuk, 2002a), and shearwaters gained mass

through the summer. Thus, it appears that temperature, through its affects

on the timing of the availability of prey, may have important impacts on the

survival of top predators.

2.5 What Controls Recruitment of Walleye Pollock?

Walleye pollock dominate the biomass of the eastern Bering Sea ecosystem

and the trophic guilds in which they participate (Schumacher et al., 2003).

They are harvested in greater quantity than any other species of fish or

shellfish, and made up >65% of the total groundfish biomass harvested during the 1980s (Napp et al., 2000). Pollock, because of their vast numbers,

are a nodal species for the Bering Sea, i.e., they are an integral part of the

food chain for lower and higher trophic prey and predators. Thus, in any

examination of the ecosystems of the eastern Bering Sea, factors that influ-
The Southeast Bering Sea Ecosystem—Part 2 63

ence the abundance, distribution, and ecology of walleye pollock are of key

importance for understanding the function of the ecosystem as a whole.

Walleye pollock were not always so abundant. In the early 1960s, their

populations were small by comparison with those of the 1980s and 1990s.

The reason for their initial increase in the mid to late 1960s is not known,

but one hypothesis is that the increase was a result of a trophic cascade that

occurred when large, plankton-eating whales and Pacific Ocean perch (Sebastes alutus) were harvested to “commercial extinction” in the late 1960s

and early 1970s, thereby releasing vast amounts of zooplankton from consumption (Merrick, 1997; NRC, 1996). This newly available zooplankton

resource then fuelled the expansion of the pollock population. The removal

of the whales may also have released age-0 and age-1 pollock from predation

pressure, as fin (Balaenoptera physalus) and minke (B. acutorostrata) whales

would have most likely consumed young pollock along with zooplankton.

During the 1970s and early 1980s, the biomass of walleye pollock first

declined and then increased explosively. In the mid 1970s, excessively high

harvest rates imposed by the foreign fishing fleets caused the stock to decline.

This foreign fishery ceased as of the establishment of the 200-mile Exclusive

Economic Zone in 1977. In 1978, pollock produced an enormous year-class

that numbered 63.5 billion age-1 fish in 1979 (NMFS, 2002). As these fish

grew to adult size, their biomass came to dominate the eastern Bering Sea

ecosystem. The reasons for this second spurt in population growth are not

known. It is possible that this was a continuation of the ecological release of

the pollock population subsequent to the removal of the whales and fish with

which they are believed to have competed for prey. An alternative, but not

mutually exclusive, hypothesis for this population growth is that the strong

year classes of 1977 and 1978 were the result of the 1976–1977 regime shift

that resulted in earlier ice retreat and warmer water temperatures in spring,

conditions favorable for zooplankton production that would have provided

abundant prey resources for growing pollock (Francis et al., 1998; Hunt et

al., 2002a). Indeed, there is a general acknowledgment that warm years are

conducive to enhanced pollock recruitment (Bulatov, 1995; Hollowed and

Wooster, 1995; Bailey et al., 1995, 1996; Quinn and Niebauer, 1995; Walsh

and McRoy, 1986; Blood, 2002). The cessation of the foreign fishery did not

cause the strong year class of 1978. The increase was driven by exceptional

early survival and recruitment. Had the fishery continued into the mid 1980s,

there might not have been as great a growth in pollock biomass, but the 1978

year class still would have been exceptional.

Throughout the 1980s, the biomass of walleye pollock in the eastern

Bering Sea remained high, though between 1990 and 1998 there was a substantial decline (Fig. 2.32) (NMFS, 2002). During the 1980s, there were

three strong year classes, and during the 1990s, there were two (Table 2.6;

Fig. 2.32). It is these strong year classes, as they matured, that sustained

the biomass of the stock and its ability to support commercial fisheries.

Understanding of the determination of year-class strength is a key to understanding much of what occurs in the southeastern Bering Sea marine

ecosystem. Section 4 of this report discusses some factors in detail.

Table 2.7 lists several hypotheses that have been put forward as expla-
64 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Year

1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999

1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001

75,000

50,000

25,000

0

10.0

8.0

6.0

4.0

2.0

0.0

Numbers at age 1 Millions of tons

Year class index

Bottom trawl survey biomass estimates

Figure 2.32: Top: year class strength of walleye pollock; Bottom: biomass of

pollock on the eastern Bering Sea shelf (NPFMC, 2002).

nations for variation in pollock year-class strength. They can be divided

into four groups. Group A focuses on the importance of primary or secondary production for the survival of young pollock; Group B focuses on

the importance of cannibalism and mechanisms that result in the separation

of adult and juvenile pollock; Group C hypothesizes that variation in risk

of predation and variation in the abundance of zooplankton are causal, and

Group D relates survival of young pollock to variation in the amount of turbulence when feeding. Ladd et al. (ms) have modeled the amount of mixing

across the seasonal pycnocline expected during each summer over the past

decades, and a preliminary comparison of their results with the distribution

of strong and weak year classes in Table 2.6 suggests that variations in primary production forced by summer entrainment of nutrients is not likely a
The Southeast Bering Sea Ecosystem—Part 2 65

good predictor of year-class strength in pollock. The abundance of small

shelf copepods does vary significantly with integrated water temperature

(Tables 2.3 and 2.4), and if prey is a limiting factor in pollock year-class

strength, then this remains a viable hypothesis. Cannibalism is recognized

as a major source of mortality for young pollock (Dwyer et al., 1987; Wespestad, 1994; Balykin, 1996; Livingston and Lang, 1996; Wespestad and

Quinn, 1996; Livingston and Methot, 1998; Lang et al., 2000; Livingston

and Jurado-Molina, 2000), and mechanisms for separating vulnerable individuals from cannibalistic older fish, such as on-shelf advection (Wespestad

et al., 2000), are likely to be of importance, regardless of what other factors

are at play. For instance, Cooney et al. (2001) found that in Prince William

Sound, adult pollock would feed on large Neocalanus copepods in spring if

the copepods were abundant, but if not, the adult pollock would prey upon

juvenile salmon and herring. Finally, variations in the amount of turbulence may influence foraging success of larval pollock by either enhancing

the encounter rate with food particles at intermediate rates of turbulence,

or depressing foraging success with either too much turbulence or insufficient

turbulence (Megrey and Hinckley, 2001). Most of these hypotheses remain

to be tested in the field, and it is likely that some combination of them together will provide the key to understanding the occurrence of strong year

classes.

It is also likely that, as pollock populations wax and wane, the mechanisms responsible for population control will shift. Hunt et al. (2002a) hypothesized that under environmental conditions in which zooplankton were

scarce, such as during a prolonged series of cold springs, walleye pollock

populations would be controlled from the bottom-up because there would

be insufficient zooplankton for larval or juvenile pollock (Fig. 2.33). In contrast, in years when pollock populations were large, cannibalism would exert

strong top-down control on the population, as it apparently does at present.

In periods under top-down control, large year classes could still occur, but

to do so there would have to be some mechanism to promote separation of

the young fish from the adults (e.g., Hypotheses 3 and 4, Table 2.7). When

there is a switch from a period of years with cold springs to warm springs,

one can expect large year classes, as there should be plentiful zooplankton

and low risk of cannibalism, while during a switch from a prolonged period

of warm springs to cold springs, year-class strength might be expected to be

low because of both bottom-up and top-down effects.

The distributions and abundances of adult and juvenile pollock (age-0

and age-1) have important impacts on other elements of the eastern Bering

Sea ecosystem. Juvenile pollock are a major source of prey for not only

large piscivorous fishes such as adult pollock, Pacific cod and arrowtooth

flounder, they are also staples in the diets of several species of marine birds

and pinnipeds, such as northern fur seal, harbor seal, and Steller sea lions

(see above). There is evidence for a negative relationship between the reproductive success of black-legged kittiwakes on St. George Island and the

biomass of adult pollock on the eastern Bering Sea shelf (Livingston et al.,

1999; Hunt and Stabeno, 2002; Sinclair et al., in preparation). During the

period of population decline in the 1990s, northern fur seals at St. Paul
66 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Table 2.7: Hypotheses for explaining variation in year-class strength of walleye pollock in the eastern Bering

Sea.

Hypothesis Citations Tests

A. Production-Based Hypotheses

1. Decreases in wind mixing

negatively influence post-bloom

primary production.

Sambrotto et al. (1986) Compare wind mixing in years with

strong and weak year classes; Ladd

et al. (unpublished manuscript).

Probably no significant relationship

with summer mixing.

Hunt et al. (2002a)

Springer, this report, Part 4

Determine if there is strong

coupling between spring and

summer primary production,

zooplankton abundance, and

pollock year-class strength.

2. Late ice retreat results in cold

water blooms and reduced

zooplankton production.

Stabeno et al. (1998) Compare zooplankton abundances

in years with early and late ice

retreat; Coyle and Pinchuk (2002b)

show significant differences.

Hunt et al. (2002a)

Hunt and Stabeno (2002)

Determine if zooplankton

abundance limits age-0 or age-1

pollock survival.

B. Cannibalism-Based Hypotheses

3. When the cold pool is large, age-1

pollock move to the shelf edge

where adults abound.

Dwyer et al. (1987) Compare cold pool size in years

Ohtani and Azumaya (1995) with strong and weak year classes.

Wyllie-Echeverria (1995)

4. Favorable winds in early spring

may transport eggs, larvae, and

small age-0 pollock deep onto the

shelf, thus separating them from

adults.

Wespestad et al. (2000) Compare wind forcing in years with

strong and weak year classes.

C. Mixed Production/Cannibalism Hypothesis

5. Adult pollock switch prey types

such that when zooplankton are

scarce in spring, they increase

cannibalism.

Cooney et al. (2001) Since zooplankton are less

abundant in cold years, compare

the amount of cannibalism in years

with cold and warm spring

temperatures.

D. Mixing Process Hypothesis

6. Intermediate levels of turbulence

are required for optimal foraging

conditions.

Megrey and Hinckley (2001) Compare turbulence levels in years

with strong and weak year-classes.
The Southeast Bering Sea Ecosystem—Part 2 67

Cold Regime (Bottom-Up Regulation)

Beginning of Warm Regime (Bottom-Up Regulation)

Warm Regime (Top-Down Regulation)

Beginning of Cold Regime (Both Top-Down and Bottom-Up Regulation)

Oscillating Control Hypothesis

Zooplankton Larval Survival Abundance of Piscivorous Adult Fish Juvenile Recruits

Figure 2.33: Schematic of the function of the Oscillating Control Hypothesis. For an explanation, see text.

(From Hunt et al., 2002a).

Island reduced their use of age-0 and age-1 pollock, sand lance, and slope

squids, but increased their use of age-3, -4, and -5 pollock. It is difficult to

know the causes of these shifts in fur seal diets.

The distribution of age-1 pollock may be affected by not only water temperature (Hypothesis 3, Table 2.7), but also their exposure to predation,

either through removals, or through behavioral responses to the presence of

predators (Sogard and Olla, 1993). Displacements of juvenile pollock from

surface waters will make them unavailable to predators such as kittiwakes

that are restricted in their foraging to the very surface of the water column, while displacements in the horizontal from the vicinity of rookeries

and colonies will diminish their value to central place foragers as a function

of the distance travelled from the central place. After the biomass of adult

pollock increased near the Pribilof Islands, the biomass of age-1 pollock there

dropped precipitously (Hunt et al., 2002a, Fig. 2.12). There remains a need

to understand the role that adult pollock play in determining not only the

abundance, but also the distribution of forage fishes, including age-1 pol-
68 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

lock, and their availability to upper trophic level predators including marine

mammals and seabirds.

2.6 Summary of Advances

Many of the important advances in our understanding of the coupling of

regional atmospheric processes, physical processes in the Bering Sea, and

biological responses to these forcing mechanisms have been the subjects of

recent review (Stabeno et al., 2001; Overland et al., 2002; Schumacher et al.,

2003). Among these advances in our knowledge are:

2.6.1 Marine climate

1. A new understanding of the importance of warm-season climate (Overland et al., 2001; Bond and Adams, 2002).

2.6.2 Basin and slope waters

1. An improved understanding of the function of the Aleutian North

Slope Current and the Bering Slope Current (Reed and Stabeno, 1999;

Stabeno et al., 1999a; Johnson et al., 2004), and their potential importance to fish (Reed, 1995);

2. Documentation of eddies over the slope (Schumacher and Stabeno,

1994; Okkonen, 2001) and along the outer shelf (Reed, 1998) and their

importance as habitats for fish and for the transport of small pollock

onto the shelf (Schumacher and Stabeno, 1994);

3. Development of a model that resolves eddies and suggests that the

source of eddies in the southeastern Bering Sea basin is in the Bering

Slope Current (Hermann et al., 2002).

2.6.3 Shelf waters

1. Discovery of mean flows across the shelf (Reed and Stabeno, 1996;

Reed, 1998), which for short intervals in summer can result in replenishment of salts and transport of oceanic zooplankton to the Inner

Front (Stabeno et al., 2001; Coyle and Pinchuk, 2002b);

2. A clarification of the role of the timing of ice retreat for the timing

and fate of the spring bloom (Stabeno et al., 2001; Hunt et al., 2002a;

Hunt and Stabeno, 2002);

3. Understanding of the relative importance of winter and warm season

conditions for determining the timing and strength of spring stratification and the potential for summer mixing across the pycnocline (Ladd

et al., unpublished manuscript);

4. An understanding of the temporal and spatial variability of the Inner

Front and its role in nutrient fluxes from depth (Kachel et al., 2002);
The Southeast Bering Sea Ecosystem—Part 2 69

5. A greater appreciation for the role of water temperature in the distribution of fish, especially pollock (Ohtani and Azumaya, 1995; WyllieEcheverria, 1996; Wyllie-Echeverria and Wooster, 1998), the hatching

time of pollock eggs (Blood, 2002), and the population dynamics of

zooplankton (Coyle and Pinchuk, 2002a, b);

6. A greater appreciation for the role of episodic events in structuring the

ecosystem;

7. A new hypothesis concerning the role of wind-forced transport for the

survival of juvenile walleye pollock (Wespestad et al., 2000).

2.6.4 Biological components of the ecosystem

1. Evidence that the net annual primary production in the northern

Bering Sea may be declining (Schell, 2000; Grebmeier and Cooper,

1994; Grebmeier and Dunton, 2000), whereas in the southeastern Bering

Sea, increases in standing stocks of fish and non-crab benthic invertebrates do not suggest a decline in production (Conners et al., 2002);

2. Commencement of research on the role of nanno- and microplankton,

including coccolithophores, in the marine ecosystem of the southeastern Bering Sea (Howell-Kubler et al., 1996; Olson and Strom, 2002);

3. Re-examination of the Oshoro Maru zooplankton time series shows

no evidence for a significant decline in zooplankton biomass within

samples stratified by the domain in which they were collected (Hunt

et al., 2002a; Napp et al., 2002);

4. Immense increases in the biomass of large jellyfish and then a rapid

decline to levels seen in the early 1980s (Brodeur et al., 1999a, 2002);

5. Observations of responses of crustacean zooplankton to variations in

water temperature, including apparent shifts in the timing of breeding

in shelf euphausiids (Coyle and Pinchuk, 2002a), and the abundance

of small shelf copepods (Coyle and Pinchuk, 2002a; Napp et al., 2002);

6. Determination that large cetaceans are now encountered more frequently in shelf waters than in the 1970s, and that substantial numbers

are present over the shelf (Baretta and Hunt, 1994; Moore, 2000);

7. Progress in developing understanding of climate effects on flatfish recruitment (Wilderbuer et al., 2002);

8. Progress in defining multiple hypotheses concerning the control of yearclass strength in walleye pollock (Table 2.7; Section 4 of this report);

9. Improved understanding of the foraging habits and diets of marine

birds, northern fur seals, and Steller sea lions at the Pribilof Islands;

the potential relationships between the abundance of adult pollock over

the shelf and the reproductive success of black-legged kittiwakes at the

Pribilof Islands (Sinclair et al., in preparation);
70 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

10. Evidence that sub-lethal food stress during the breeding season can

lead to elevated levels of corticosteroids, and possibly elevated levels

of post-breeding season mortality in seabirds (Kitaysky et al., 1999b,

2001a, 2002);

11. Development of a mechanistic explanation of how variations in climate

could cause shifts in the mechanisms controlling walleye pollock populations (Oscillating Control Hypothesis, Hunt et al., 2002a).

2.7 Areas of Research Requiring More Attention

Although a great deal of progress in understanding the forcing mechanisms

and the ecosystem responses of the southeastern Bering Sea has been made

since the early 1990s, there are still many unanswered questions that demand

attention if we are to provide good stewardship for the resources of the Bering

Sea shelf. Answers to these questions go beyond academic value; we need

to understand the processes and mechanisms that determine how changes in

global climate and the associated physical forcing affect the flow of energy

to upper trophic level organisms including commercially valuable fish and

shellfish, marine mammals, and seabirds. Below are listed a number of

questions that require attention.

1. How do atmospheric forcing mechanisms influence the distribution and

transport of salts, heat, nutrients, and organic matter in the eastern

Bering Sea?

(a) How does atmospheric forcing influence the distribution, abundance, and magnitude of eddies?

(b) How does atmospheric forcing affect cross-shelf flow? Do the

mechanisms whereby climate affects cross-shelf flow vary seasonally? What are the time scales of primary importance?

(c) How does one scale up and down between large-scale atmospheric

processes and the biological processes critical to the production

regimes of the eastern Bering Sea?

2. How is the timing and magnitude of cross-shelf flux controlled?

(a) What are the relative contributions of summer and winter crossshelf fluxes for renewal of nutrients on the eastern Bering Sea

shelf?

(b) What is the relative importance of flow up canyons, movement in

eddies, and other mechanisms of cross-shelf flux?

(c) What is the relative importance of nutrients provided by wintertime on-shelf advection and nutrients that have been remineralized by benthic processes? Are the estimates provided by Sambrotto et al. (1986) still useful?

3. How do different patterns in the formation, thickness, extent, and meltback of sea ice influence the ecosystems of the eastern Bering Sea shelf?
The Southeast Bering Sea Ecosystem—Part 2 71

(a) How does the pattern of ice melt-back affect the timing and type

of phytoplankton bloom? Do these mechanisms work in similar

ways in the southern and northern Bering Sea?

(b) Do open-water blooms occur in the northern Bering Sea? If so,

are they as important as the ice-related blooms?

(c) Are ice-related blooms shorter and more intense than open-water

blooms? When both are potentially present, which is likely to fix

more carbon?

(d) How do ice formation and melt-back interact with cross-shelf advection of warm water in winter to determine the temperature

and extent of the southern cold pool?

4. What controls the amount and fate of primary production?

(a) Has primary production decreased over the southeastern Bering

Sea shelf?

(b) For the cold-adapted species of the Bering Sea, are zooplankton

growth and production more sensitive to temperature than phytoplankton productivity, as is assumed in the hypothesis of Walsh

and McRoy (1986)?

(c) Does significantly more phytoplankton sink to the bottom in cold

years than in warm years, as hypothesized by Walsh and McRoy

(1986)?

(d) How do the nanno- and microplankton affect the flow of energy

in the southeastern Bering Sea? Are they directly or indirectly

important prey for either larval or juvenile walleye pollock?

(e) How do coccolithophores affect energy flow in the southeastern

Bering Sea? Why did they suddenly bloom, and then as suddenly

disappear after blooming consecutively for 5 years?

5. What controls the biomass and productivity of upper trophic level

organisms?

(a) How do plankton predators such as chaetognaths, ctenophores,

and larger jellyfish affect the flow of energy in the eastern Bering

Sea? Why did the population of large jellyfish increase and then

crash? Are they significant competitors or predators on juvenile

pollock and other forage fish?

(b) How does temperature affect the role of chaetognaths, ctenophores

and larger jellyfish as predators of crustacean zooplankton?

(c) How can one account for the simultaneous increase of round-fish,

flatfish, and non-crab invertebrates over the shelf of the southeastern Bering Sea (Conners et al., 2002)?

(d) How variable is the abundance of mesopelagic fish and squids in

the eastern Bering Sea? What are the ramifications of this variability for their use by commercially valuable fish, marine mammals and seabirds?
72 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

6. How can we translate information about Bering Sea ecosystems into

products useful to those who are managing the fisheries of the eastern

Bering Sea?

(a) What factor or factors control year-class size in walleye pollock?

The most promising hypotheses, such as those listed in Table 2.7,

need to be tested systematically.

(b) There is a need to test the underlying assumptions and predictions

of the Oscillating Control Hypothesis (Hunt et al., 2002a). In particular, are zooplankton grazers closely coupled with phytoplankton in any season, and does this coupling affect their availability

to larval or juvenile pollock? Likewise, are larval or juvenile pollock closely coupled to zooplankton in any season?

2.8 Acknowledgments

Many scientists provided text and figures that have been incorporated into

this report. Included are Nick Bond, Ric Brodeur, Liz Conners, Susan Henrichs, Anne Hollowed, Jim Overland, Beth Sinclair, Phyllis Stabeno, and Al

Tyler. Financial support for this review was provided in part by the NOAA

Coastal Ocean Program, Southeast Bering Sea Carrying Capacity program.
The Southeast Bering Sea Ecosystem—Part 3 73

Part 3—Forecasting Abundance of Walleye

Pollock: Indices for Juvenile Abundance

Results from the Indices Working Group: Development and

Application to Fishery Management and Ecosystem Information

Issues of Environmental Indices in the Eastern Bering Sea

Principal Authors: J.D. Schumacher and S.A. Macklin

With participation and contributions from the Indices Working Group:

Kerim Aydin, Nick Bond, Cathy Ferrar, Al Hermann, Anne Hollowed, Jim

Ianelli, Nancy Kachel, Carol Ladd, Pat Livingston, Allen Macklin, Bern Megrey,

Jeff Napp, Ron Reed, Dylan Righi, Sigrid Salo, Jim Schumacher, Alan Springer,

Phyllis Stabeno, Peggy Sullivan, Terry Whitledge, and Tom Wilderbuer

3.1 Introduction

The goal of the Southeast Bering Sea Carrying Capacity program was to document the role of juvenile pollock in the eastern Bering Sea ecosystem. This

included examination of factors that affect their survival and development

and testing of annual indices of pre-recruit (age-1) abundance (SEBSCC,

1995). Within this framework, the purpose of the Indices Working Group

(IWG, which was developed at the SEBSCC Principal Investigators meeting

held in January 2001; membership as listed above) was defined as follows.

Based on the best understanding of ecosystem dynamics, identify potential

single- or multi-parameter constructs or indices (e.g., wind mixing, time of

spring bloom, etc.) that lead to development of survival indices for pollock in

the egg, larval, and young-of-the-year life history stages. Further, this information will provide input to the National Marine Fisheries Service (NMFS)

stock assessment model and/or models of juvenile pollock for use by fisheries

scientists at the Alaska Fisheries Science Center (AFSC)/NMFS and others

interested in changes in the ecosystem.

In Section 3.2, we first present the conceptual framework for development

of indices, including discussion of the time series of estimates of age-1 pollock recruitment (Fig. 3.1), which provides a base time series that indices can

be evaluated against. Section 3.3 contains contributions from IWG members regarding development of potential indices (Table 3.1). Using a onedimensional mixed layer model, Ladd and Stabeno (Section 3.3.1) developed

an index of stratification. Stratification can influence larval and juvenile

pollock survival through its impact on nutrient limitation, temperature and

its inherent effects on physiological processes of the pollock and of their

zooplankton prey. Megrey and Bond (Section 3.3.2) examine the potential

impact of wind speed, a surrogate for mixing, as it might affect the ability of

pollock larvae to feed. Napp and Kachel (Section 3.3.3) present time series

of average net short wave radiation that has a direct impact on the timing of

the spring phytoplankton bloom. This, in turn, has ramifications throughout the entire ecosystem (Hunt et al., 2002a) and thus serves managers of

components of this ecosystem by providing an index of the status and po-
74 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Table 3.1: Environmental indices described in Section 3.3.

Index 1996 1997 1998 1999 2000 2001 2002

Wind Mixing

Low 0.35 0.61 0.35 0.35 0.45 0.42 0.26

Optimum 0.65 0.26 0.58 0.61 0.42 0.58 0.55

High 0.00 0.13 0.06 0.03 0.13 0.00 0.19

Stratification

Maximum 0.692 0.855 0.770 0.679 0.677 0.709 0.852

Date May 17 ice bloom June 16 ice bloom May 19 May 15 June 4

Mixing Events 6 1 1 0 3 0 1

Temperature 1.6 ice bloom 2.9 ice bloom 1.0 1.9 2.5

Light

[60◦N, 170◦W] −150.3 −153.9 −141.2 −143.1 −145.5 −152.9 #N/A

[57.5◦N, 165◦W] −180.6 −196.3 −176.2 −187.1 −187.0 −188.7 #N/A

[58◦N, 159◦W] −185.0 −188.6 −175.3 −168.5 −181.0 −180.3 #N/A

Sea Ice 0.012 0.466 0.002 0.346 0.055 #N/A #N/A

Cold Pool

T1 1.9 7.3 3.2 6.3 2.1 #N/A #N/A

T2 1.0 5.3 0.0 16.8 8.9 #N/A #N/A

Advection #N/A 0.059 0.003 0.000 0.045 0.003 #N/A

Age-0 Index 2.68 0.14 0.75 1.13 #N/A #N/A #N/A

Age-1 Abundance 26.6 42.3 17.5 17.5 24.5 25.7 11.1

Explanation of Indices

Wind Mixing: Fraction of period May 15 to June 15 when daily average surface wind speed (m s−1) was <4.8

(low), 4.8–9.5 (optimum), >9.5 (high). [Estimated from NCEP winds.]

1-D Mixed Layer Model:

Stratification: Maximum (May–June) modeled stratification (◦C m−1) (3-y running mean).

Date: When maximum stratification occurred.

Mixing Events: Number of times between stratification setup date and August 31 that modeled 3-day smoothed

mixed layer depth increased >5 m in a day.

Temperature: Mixed layer temperature at max stratification date.

Light: Average net shortwave radiation (W m−2) from April 1 through June 30 at [60◦N, 170◦W], [57.5◦N, 165◦W],

and [58◦N, 159◦W] [from NCAR/NCEP Reanalysis].

Sea Ice: Average per cent concentration of sea ice within the region bounded by [58◦N, 170◦W] and [57◦N, 165◦W]

during the period 15 March to 15 April.

Cold Pool: Estimated vertical areal slice of cold pool (<2◦C) water (km2) contained along transect (on T1 during

1996 and 1999, the cold pool continued into shallower water).

Advection: Fraction of 735 simulated drifters that arrived after 90 days in the region between 57◦–58◦N and

165◦–170◦W.

Age-0 Index: Young-of-the-year abundance estimate (arbitrary units) from independent Pribilof survey.

Age-1 Abundance: Abundance estimates of 1-year old eastern Bering Sea pollock (billions) from Model 1 [2002

Stock Assessment].
The Southeast Bering Sea Ecosystem—Part 3 75

tential trends in ecosystem structure. Sea ice influences the ecosystem via

bottom-up (e.g., timing of peak phytoplankton production) and top-down

(e.g., habitat) impacts. Salo and Stabeno (Section 3.3.4) developed time

series of sea ice concentrations in various strata that establish the existence

of strong spatial and interannual patterns. Because the extent and timing of

sea ice largely determine the extent and yearly thermal characteristics of the

cold pool, Sullivan (Section 3.3.5) developed time histories of water column

temperature from the annual bottom trawl survey. These provide a different

and more direct measure of the thermal field than does the sea ice index.

The extent and magnitude of the cold pool may have implications for the

distribution of adult pollock and hence cannibalism on young of the year

fish (Wylie-Echeverria and Ohtani, 1999; Wylie-Echeverria, 1996; WylieEcheverria and Wooster, 1998). In Section 3.3.6, Ianelli considers using the

estimated abundance of age-0 pollock from the Pribilof Islands area of the annual stock assessment as an index for juvenile pollock abundance. Hermann

(Section 3.3.7) then provides a description of the Northeast Pacific Regional

Ocean Modeling System (NEPROMS), as hydrodynamic model simulations

can provide underpinning for the development of various indices. Righi (Section 3.3.7) presents an application of NEPROMS to the transport of pollock

eggs and larvae. The connection to survival in these scenarios is the extent

of overlap between the transported animals and older cannibalistic pollock.

Importantly, the transport trajectories from the simulations more closely

simulate actual conditions over the shelf than those used by Wespestad et

al. (2000) in their examination of year-class success of pollock. Macklin

(Section 3.3.8) presents a framework for cataloging indices.

Ianelli (Section 3.4) demonstrates how an index of early life history success is being used by NMFS for annual stock assessments. He presents an

index of separation between juvenile and adult fish. Importantly, he points

out that variation in location and timing of spawn, as well as the location

in the water column of eggs and larvae must be accounted for in the development of more robust transport- and cannibalism-related indices. Springer

(Section 3.5) examines changing relationships between climate and biological

indices throughout the eastern Bering Sea, implying that different relationships likely apply during different phases of the climate regime. These results

demonstrate the potential for using environmental indices (e.g., selected sea

surface temperatures) as measures of ecosystem trends. Section 3.6 contains

a synthesis of results from indices development and provides a road map

for future development of indices. Section 3.6.3 presents concluding remarks

that call for the development of informational products from the NEPROMS

oceanographic model that would permit access to various model products by

resource managers and users.
76 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

0

10

20

30

40

50

60

70

1963196519671969197119731975

19771979

1981

19831985

19871989

1991

19931995

1997

1999

NUMBER OF POLLOCK (billions)

RECRUITMENT BY YEARCLASS

Age-1 BS Pollock

YEAR

Figure 3.1: Recruitment of age-1 pollock in the eastern Bering Sea estimated as

number of fish (from SAFE Report, 2001).

3.2 Conceptual Framework for Development of Indices

3.2.1 A pollock-centric perspective of the southeastern Bering

Sea ecosystem

As noted by Napp et al. (2000), pollock is the most abundant species harvested in the Bering Sea, accounting for >65% of the total groundfish biomass during the 1980s when the total biomass exceeded 20 million tons. The

biomass trends of three major trophic guilds in the eastern Bering Sea from

1979 to 1998 (Schumacher et al., 2003) show that while the total biomass of

pollock in the 1990s is less than in the 1980s, they still dominate biomass

in any of the guilds which include marine birds, mammals, other fishes, and

crabs. Walleye pollock is a nodal species in the food web (NRC, 1996) with

juveniles being the dominant prey of fishes, seabirds, and marine mammals

(Springer and Byrd, 1989; Livingston, 1993; Brodeur et al., 1996). It is

natural that pollock have been the focus of the Coastal Ocean Program’s

SEBSCC. The choice of developing a survival index for the early life history

stages (eggs through young of the year) allows an early forecast of potential

recruitment to the fishery and a metric that can be compared to existing

time series of age-1 abundance (Fig. 3.1).

A switch model was developed for survival of pollock in the eastern Bering

Sea (Megrey et al., 1996). The model identifies candidates for cause of mortality by life history stage, the mortality variability of each stage, and indication of the stage contributing the most variability in recruitment to the

fishery (Fig. 3.2). In this model, transport and turbulence have their greatest
The Southeast Bering Sea Ecosystem—Part 3 77

Spawning

Adults Eggs Yolk sac

larvae

Feeding

larvae

Juveniles One-year

olds

Recruits

Mortality/survival

process

little little some some most little little Mortality

variability

t = 0 t = 1 - 21 d t = 22 - 28 d t = 29 - 59 d t = 60 d - 1 yr t = 1 - 2 yrs t > 2 yrs

Ice cover / Cold pool

(vertical stratification)

Wind mixing

(turbulence)

Wind-driven

on-shore transport

Climate

Cannibalism

Figure 3.2: Switch model of pollock dynamics in the eastern Bering Sea. Processes thought to be important

regulators of survival variability are shown and the span of their interaction is indicated.

impact on mortality of yoke-sack through feeding larval stages. It is the vertical structure and horizontal distribution of temperature, however, through

their suggested influence on cannibalism, that have the greatest impact on

recruitment (see Section 3.4 for more detail on egg/larval transport and the

subsequent potential impact on cannibalism).

In a later version of the switch model, Napp et al. (2000) augmented

the candidates that regulate survival by adding the timing of preferred prey

production and the absence or presence of ice over the shelf region. Most

recently, these have been woven together with other environmental factors

to develop the Oscillating Control Hypothesis (OCH, Section 2.5), which examines how energy flow through the ecosystem may vary in different climate

regimes (Hunt et al., 2002a).

3.2.2 A generic model of pathways of energy flow through the

ecosystem

Although the focus on pollock is reasonable, and models that have pollock

as the central feature helped focus research in SEBSCC, a more inclusive

perspective also has value. This is particularly true when considering questions on the ecosystem scale, e.g., the dramatic decline in abundance of the

Steller sea lion. Identifying and understanding mechanisms that transfer

climate change via the ocean to biota is essential if we are to understand

ecosystem dynamics (Francis et al., 1998). Fluctuations in the physical environment can impact the ecosystem through both changes in the nutrientphytoplankton-zooplankton sequence (i.e., bottom-up control), and/or by

altering habitat resulting in changes in abundance and/or composition of

higher trophic level animals (i.e., top-down control). Hunt et al. (2002a) hy-
78 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

∆ Mixed

Layer D

Mechanistic Pathways from Changes in Climate to Marine Biota

 (after Francis et al., 1998)

∆ Atmospheric

 Pressure

∆ Mixed

Layer T

∆ Air-Sea Heat

Exchange

∆ Wind

Stress

∆ Vertical

 Mixing ∆ Sea Ice

∆ Sea Ice

∆ Horiz.

Flow

∆ Nutrients ∆ Vertical

Flow

∆ 1°

Prod. Store ∆ 2°Prod. Store

∆

Forage

Store

∆ 2°

Predators

Store

∆ 1°

Predators

∆ Mesoscale

Features

Figure 3.3: Conceptual model showing the pathways through which changes in

atmospheric features can influence oceanic and biological components of the ecosystem of the eastern Bering Sea (from Schumacher et al., 2003).

pothesize that the control of energy flow on the shelf (either top down or bottom up) depends on the timing of the nutrient-phytoplankton-zooplankton

sequence related to the presence/absence of sea ice.

More inclusive and general conceptual models have been developed for

the eastern Bering Sea and North Pacific Ocean. Schumacher et al. (2003)

adapted the Francis et al. (1998) conceptual model of the pathways by which

climate phenomena influence the biota to include sea ice, a dominant feature

of the ecosystem of the eastern Bering Sea shelf (Fig. 3.3).

This model shows that there are numerous ecosystem elements (Section 3.5 discusses relationships between adjacent and more widely separated

ecosystem elements) and connecting processes to consider when attempting

to understand the impact of changes in atmospheric pressure on survival

of young pollock. For example, a change in characteristics of the Aleutian Low will alter wind stress and the ensuing horizontal flow field (upper

layer currents). It also will impact vertical mixing and air-sea heat fluxes

and, thereby, the temperature of the upper mixed layer. These changes in

transport, turbulence and temperature can impact the biological ecosystem

element of interest either by enhancing or limiting survival, or both.
The Southeast Bering Sea Ecosystem—Part 3 79

 1

 2a

 2b

 3

 4

 5

 6

 8

Figure 3.4: Abundance of adult pollock in the unequal areas used in the development of the ice index (Section 4.3.4).

3.2.3 Other aspects of indices development

As expected, many questions were generated and addressed during IWG

workshops. One area of critical interest related to the potential time/space

scale mismatch or match among features of the ecosystem. From a physics

perspective, the eastern shelf has distinct regions or strata that are dominated by different physical processes. This is strongly evident in an acrossshelf direction going from the more oceanic-like outer shelf waters to the

strongly two-layered middle shelf and then shoreward into a water column

that often is without vertical structure. (Stabeno et al., 1999a) show that

differences in advection occur within the previously defined latitudinal strata

(southeastern, central and northern shelf; Schumacher and Stabeno, 1998).

For this reason, some of the physical observations were partitioned into areas within the NMFS strata (Section 3.3.4). Some data sets, however, would

become too noisy if the spatial domain is reduced beyond some limit. For

example, the estimates of pollock abundance from the bottom trawl survey

(Fig. 3.4) can be grouped in the same areas used for the ice index. However,

it was the consensus of the IWG that this time series contains too much

variation and that the integrated values (i.e., the commonly used time series

of adult or age-1 pollock throughout the eastern Bering Sea) provided the

appropriate time series of abundance.

The IWG members also felt that it was important to consider the local/regional impact of larger-scale indices, seeking relationships between

physics and biology. For example, when using a hemispherical index (e.g.,

Arctic Oscillation, PDO, NPI), one should first consider how it is manifested

on the regional or local scale. This also applies on finer spatial scales, e.g.,

how representative are winds measured at St. Paul Island to mixing processes

in inner Bristol Bay? While zero-order approaches have some value, their

results can be misleading. For example, several studies use the OSCURS

model to transport planktonic stages of various species. In these simulations, only one release site is used to initialize trajectories. As results from

the NEPROMS model have shown (Section 3.3.7), changes in initial location

can result in significantly different trajectories.
80 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

3.3 Development of Environmental Indices

3.3.1 Indices of stratification, potential supply of nutrients to the

upper mixed layer, and turbulence experienced by larval

pollock (Carol Ladd and Phyllis Stabeno)

We used the Price-Weller-Pinkel (Price et al., 1986) one-dimensional, mixed

layer model to simulate the establishment of stratification and subsequent

mixing over the southeast Bering Sea shelf during summer. We selected the

location (56.9◦N, 164.1◦W) to coincide with the site of FOCI biophysical

mooring 2. Observations collected at this mooring provide 7 years of data

that were used to validate model results. The possible mechanisms that

could relate mixed layer depth to differential survival of young-of-the-year

pollock include: (1) food limitation due to lack of renewal of nutrients from

deeper waters or mixing of food particles through a larger volume of water,

and (2) changes in the upper layer thermal field that impact physiological

processes (timing of prey production and duration of the development of

larvae to the first feeding stage) and community composition (appearance of

new predators).

The model calculates the density and wind-driven velocity profile in response to imposed atmospheric forcing (discussed below using the method

of Price et al., 1986). Model simulations were initialized with a vertical temperature profile derived from the NCEP Reanalysis (Kalnay et al., 1996).

We used the sea surface temperature (SST) from May 1 combined with a

weak thermocline (∆T = 0.5◦C) between 10 and 20 m. The model is then

forced with winds and heat fluxes from the reanalysis and run from 1 May

through 30 September 30 of each year. Comparisons with data show that

the model reproduces mixed layer depth, timing of stratification set-up, and

mixing events quite well.

The following assumptions and caveats should be noted:

• The model is one-dimensional. Hence, advection is assumed unimportant. Advection over the middle shelf is generally weak (Schumacher

and Kinder, 1983; Schumacher and Stabeno, 1998). At times, however,

currents have resulted in transport of salt and nutrients at this location (Stabeno et al., 2001). In general, the assumption of no advection

is likely good at this site, but it would have to be evaluated for other

locations.

• The model is initialized with 1 May SST from the NCEP reanalysis.

This time series has errors on the order of ±0.5◦C when compared

to in situ observations collected at site 2 between 1996–2001. These

errors are well within the magnitude of interannual variability in the

NCEP SST (the standard deviation over 40 years is ∼1◦C). Summer

heat content and SST of the model results, however, are dependent on

the initial temperature profile and will reflect errors in that profile.

• Based on comparisons between model simulations and observations at

site 2, salinity appears to have only a minor impact on stratification

and mixing processes. Waters of the middle shelf domain exhibit weak
The Southeast Bering Sea Ecosystem—Part 3 81

Maximum (May–June) stratification

(3-year running mean)

1960 1970 1980 1990 2000

1

0.8

0.6

0.4

0.2

0

Degrees C/m

Figure 3.5: Maximum over May/June of vertical stratification maximum.

horizontal and vertical gradients in salinity (Coachman, 1986), and the

time series of salinity measurements are sparse. Therefore, salinity is

kept constant (31.65 psu) throughout the model runs.

• The model includes a diffusive term that results in a slightly weaker

thermocline than observed. Without diffusion, the thermocline becomes too strong, resulting in SSTs that are too warm.

• Although the model simulates mixing processes in the upper layer quite

well, the bottom mixed layer is not reproduced. Over the middle shelf,

the water column exhibits a strongly stratified two-layer structure (in

temperature) with a wind-mixed surface layer and a tidally mixed bottom layer (Schumacher and Stabeno, 1998). The model does not include any tidal mixing.

Results. Table 3.1 contains the stratification indices developed from

this research. Interannual variability in mixing conditions can be evaluated

using the model hindcasts. Model results show that the stratification maximum during May/June of each summer has varied on an approximately

decadal time scale (Fig. 3.5) with maxima in 1967, the early 1980s, and

1997, and minima in the early 1970s and late 1980s. We note that this signal is not in phase with the accepted regime shifts (1976–1977, 1989, and

perhaps 1998) observed in physical and biological time series for the eastern

Bering Sea (Hare and Mantua, 2000).

When stratification is established in the spring, it provides an index of the

timing of the spring bloom (Fig. 3.6) based on classical critical depth theory

(Sverdrup, 1953). Interannual variability in the date of stratification is high

with little indication of variability on decadal time scales. The stratification

date averages 18 May and ranges between 10 May and 25 June.

The number of mixing events over the summer (Fig. 3.7) provides an

index of the potential (some nutrients must still exist in the lower layer)

amount of nutrient entrained into the mixed layer and of conditions that

relate to decreased survival of pollock larvae (Bailey and Macklin, 1994). A

high number of mixing events in a given year may indicate higher primary

production and increased mortality of pollock larvae.
82 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Figure 3.6: Date when maximum ∆T/∆z first is >0.2◦C/m and occurs at a depth

that is <25 m, for more than 1 day. Squares (ice data based on satellite images) and

triangles (ice data based on shipboard observations) represent years when there was

ice after 1 April, and an ice-associated bloom would have been expected (Stabeno

et al., 2001).

1955 1965 1985 1995 2005 1975

10

8

6

4

2

0

Figure 3.7: Number of times between the stratification setup date and 31 August

when 3-day smoothed MLD increases >5 m in a day.

Temperature is important to the development of pollock eggs with eggs

being slower to hatch in colder conditions (Blood, 2002). Temperature also

affects the production of zooplankton (Coyle and Pinchuk, 2002b). The

water column temperature on the stratification date (Fig. 3.8) may influence

the coupling between the spring phytoplankton bloom and zooplankton prey

for first feeding and older pollock larvae. Water column temperature on the

stratification date varies between less than 1◦C in 1991 to over 4◦C in 1987

with high interannual variability, especially after 1980.

Future work. In order to expand on the present results, the model

is being modified to track entrainment of water into the mixed layer. This

will enable quantitative estimates of nutrient flux into the mixed layer over

the summer. Another option would be to include a nutrient-like tracer, kept

constant in the bottom layer and depleted over some time scale in the mixed

layer.
The Southeast Bering Sea Ecosystem—Part 3 83

1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

4.50

4.00

3.50

3.00

2.50

2.00

1.50

1.00

0.50

0.00

Temperature (°C)

Figure 3.8: Water column average temperature on the stratification date. No

temperature is shown for years when ice was present after 1 April.

0

1000

2000

3000

4000

5000

6000

0 2 4 6 8 10 12 14 16

Wind Speed (m/sec)

Consumption (μg dry weight/day)

Figure 3.9: IBM output showing consumption as a function of wind speed.

3.3.2 An index related to wind turbulence and feeding success

(Bern Megrey and Nick Bond)

Using a process-oriented, individual-based model (IBM) of pollock fish larvae, which incorporated a detailed description of the turbulence/contact

rate/feeding success process (Megrey and Hinckley, 2001), we evaluated the

relationship between the model-predicted influences of wind-generated turbulence and feeding. Output from the model (Figs. 3.9 and 3.10) conforms

nicely to the theory (MacKenzie et al., 1994) by the indication of a welldefined peak in consumption at intermediate wind speeds. Because of the

high variability in the model data, we estimated the wind speed that generated optimum consumption (Fig. 3.11) by fitting a quadratic line through

wind speed and consumption data, averaged by Julian day over the period

DOY 102-164, the period when pollock are in their early larval stage. The

wind speed that generated maximum consumption was derived by taking

the derivative of the fit quadratic equation and setting it to zero. Optimum

feeding (540 μg dry weight per day per individual) was at a wind speed of

7.1 m s−1.
84 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

0

20

40

60

80

100

120

140

160

180

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Wind Speed (m/sec)

Figure 3.10: Frequency histogram of wind speeds used to force the IBM model.

Using Fig. 3.11, we arbitrarily defined the range of optimum feeding to be

±15% of the optimum level (486 μg dry weight per day per individual). The

wind speeds corresponding to a feeding level of 486 μg were 4.8 and 9.5 m s−1.

These points are indicated in Fig. 3.11 by the two circles on either side of

the optimum circle. Thus, the range of wind speeds that provide “favorable

feeding” would be 4.8 to 9.5 m s−1. At wind speeds >9.5 m s−1, increased

turbulence negatively affects feeding, and at wind speeds <4.8 m s−1, feeding

is less than optimal because wind speeds are not sufficient to enhance the

prey contact rate. Table 3.1 presents the indices developed for wind mixing.

There are some caveats that must be considered concerning this research.

First, results are from a model experiment, not a laboratory experiment.

Simulations were carried out on Shelikof Strait walleye pollock. However,

results should be applicable to Bering Sea pollock as the feeding/contactrate/turbulence processes should still be valid. Wind induced turbulence was

not the only major process in the IBM model. The IBM was very detailed

and included many other biological processes that would have affected the

outcome of the simulations.

3.3.3 Variations in net short wave radiation: Toward development of an index of when sufficient light exists for primary

production (Jeff Napp and Nancy Kachel)

The timing of the spring phytoplankton bloom is quite variable in the southeastern Bering Sea. It can occur anytime from the late winter to late spring,

but it appears potentially to be predictable from prevailing environmental

conditions (i.e., presence or absence of sea ice, frequency of storms, and erosion of the water column stability; Sambrotto et al., 1986; Niebauer et al.,

1995; Stabeno et al., 1998; Eslinger and Iverson, 2001; Hunt et al., 2002a).

Thus, it is conceivable to model the initiation of the spring phytoplankton

bloom, which has significance to ecosystem dynamics. The dominant path-
The Southeast Bering Sea Ecosystem—Part 3 85

3000

2500

2000

1500

1000

500

0

2 4 6 8 10 12 14 16

Wind Speed (m/sec)

Consumption (μg dry weight/day)

(4.8 486) (7.1 540) (9.5 486)

y=–9.8567x +141.62x+31.16 2

Figure 3.11: Estimates of the wind speeds required to generate optimum feeding.

ways of carbon cycling (pelagic versus benthic) as well as the temporal match

or mismatch of larval pollock and their prey are dependent on the timing

of the spring phytoplankton bloom (Napp et al., 2000; Hunt et al., 2002a).

A strong case has been made that the timing of the bloom is dependent on

the current atmospheric regime, and that it plays a key role in determining

whether the ecosystem is controlled by bottom-up or top-down processes.

Thus, prediction of the timing of the spring bloom would assist ecosystem

managers in their assessment of the status and trends of the Bering Sea

ecosystem (Hunt et al., 2002a). The goal of the present exercise was to predict the earliest date that available light is sufficient to support a bloom and

to begin an examination of interannual variation in this date.

Daily average net short wave radiation (NSWR) was extracted from the

National Center for Atmospheric Research/National Center for Environmental Prediction (NCAR/NCEP) Reanalysis data set. We examined average

NSWR for the period April 1–June 30 during 1972–2001 for three sites: inner

Bristol Bay (IBB) at 58◦N, 159◦W, middle shelf domain (MSD) at 57.5◦N,

165◦W, and northwest central shelf domain (NWCSD) at 60◦N, 170◦W. The

effect of latitude is evident, with the northernmost station receiving about

19% less energy (W m−2) from 1981 onward (Fig. 3.12, Table 3.1). The MSD

and IBB time series appear coherent, and are in phase with the NWCSD

station from 1972–1983 and 1995–2001. During the intervening period, the

time series appear to be out of phase by ∼180◦. Correlation between the

MSD and other two stations is significant and positive, although the amount

of variance explained between the MSD and IBB is higher (R2 = 0.58) than

the MSD and NWCSD (R2 = 0.43).

From our analysis, several conclusions can be drawn. First, if monitoring
86 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

–100

–120

–140

–160

–180

–200

1972 1976 1980 1984 1988 1992 1996 2000

year

nswrs (W m–2)

Average Net Short Wave Radiation (W m–2) from April 1–June 30

Av_nswrs(170wx60n)

Av_nswrs(165wx57.5n)

Av_nswrs(159wx58n)

Figure 3.12: Average net short wave radiation from 1 April–30 June (W m−2).

of a smaller set of stations is desired, then it is recommended that there is at

least one station in both the southeast and northwest portions of the eastern

Bering Sea shelf. This is due to both latitudinal and local meteorological

conditions, which appear to drive the southeast and northwest systems in

or out of phase. The second feature seen in the data is the large degree of

interannual variability. The data show frequent positive and negative deviations from the long-term daily mean. Of these two fluctuations, the negative

deviations qualitatively appear to be more frequent and pronounced than

the positive ones. The sustained positive deviations will tend to promote

an earlier bloom (ignoring wind mixing), and prolonged negative deviations

will tend to delay the bloom. See Section 3.6 for suggested further research.

3.3.4 Indices related to sea ice (Sigrid Salo and Phyllis Stabeno)

Sea ice is a prominent feature of the eastern Bering Sea shelf, and several

mechanisms exist that connect sea ice to biological processes. The phytoplankton bloom associated with the presence of sea ice (Stabeno et al., 1998)

influences the timing of zooplankton prey availability for first feeding pollock larvae. The size and magnitude of the following summer’s cold pool

depend on sea ice cover (timing and persistence) and can influence adult fish

distribution and intensity of cannibalism.

Several indices exist for assessing the annual influence of sea ice on the

continental shelf of the eastern Bering Sea. Niebauer (1998) and Niebauer

et al. (1999) used the percentage of ice cover over the entire shelf. Another

index, used to relate physical characteristics to changes in fish populations,

was based on the presence of ice along the longitude of 169◦W (WyllieEcheverria and Wooster, 1998). Both have shortcomings. The percent ice

cover provides no information regarding changes in the spatial patterns of

the sea ice, and the longitudinal index is not always spatially representative.

To examine sea ice characteristics on a finer spatial scale, we calculated
The Southeast Bering Sea Ecosystem—Part 3 87

7

6 4

8

5

3 9

1

2a

2b

160°W

170°W

180°

52°N

58°N

60°N

Figure 3.13: Strata used to calculate ice concentration.

ice concentration in the ten strata shown in Fig. 3.13. The ice concentration

data are on a 0.25◦ grid and were obtained from two sources. Data from

1972 to 1994 came from the compact disk of Arctic and Antarctic ice concentrations produced by the National Ice Center, Fleet Numerical Meteorology

and Oceanography Detachment (FNOC), and the National Climatic Data

Center. To extend the time series to 2001, we digitized charts from the Anchorage Weather Service Forecast Office. The National Ice Center offers one

chart of ice concentrations per week, and we continued to use that interval.

Using the results from area 9 (which includes longitude 169◦W that was

used in a previous index) and area 3, strong differences are evident as shown

in Figs. 3.14 and 3.15. This comparison clearly shows some common features,

i.e., the years of greatest ice extent occurred prior to the 1976–1977 regime

shift. For the most part, however, the sea ice concentration is much less in

Area 9 than in Area 3. Analyses of spatial patterns in sea ice cover have

revealed both earlier transition to spring over the middle and outer shelf. The

ice concentration results for all strata are presented in some detail below in

order to highlight the rich nature of the original time series and the inherent

spatial variability that exists over the shelf:

Area 7: Ice remains for a long period, occurs earlier and retreat is latest
88 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Oct Nov Dec Jan Feb Mar Apr May Jun

Ice Concentration, Area 3

1995

1985

1975

0.65

0.55

0.45

0.35

0.25

0.15

0.05

0.95

0.85

0.75

Figure 3.14: The time series of sea ice concentration in Area 3.

Oct Nov Dec Jan Feb Mar Apr May Jun

Ice Concentration, Pribilofs

1995

1985

1975

0.65

0.55

0.45

0.35

0.25

0.15

0.05

0.95

0.85

0.75

Figure 3.15: The time series showing ice concentration in Area 9.

of all the areas. The date of last ice is typically about 15 June, with the ice

disappearing over a long period (about 1 month from full to no coverage).

Most of the change among years occurs on the interannual time scale rather

than decadal.

Area 2b: Some decadal period in the ice coverage, and disappearance

occurs quicker than in Area 7.

Area 2a: very similar to 2b.

Area 4: Ice concentration shows the greatest variability here of all the

areas. There is similarity in ice concentration with patterns in 2a. There

appears to be little difference between these two areas in the length of time

that the ice remains; the major difference is the later arrival of ice in Area 4.

The above are the four areas with the most extensive ice.
The Southeast Bering Sea Ecosystem—Part 3 89

Area 6: The concentration of ice changes immensely each year. This area

is dominated by interannual variation, and it has distinctly less ice than the

other areas. In some years, there is little or no ice at all.

Area 8: (contains mooring site 4) The concentration of ice here shows

large variability in percent of concentration. The time of maximum concentration varies throughout the years: one year it occurred in January, while

in another year it occurred about May 1.

Area 3: (contains mooring site 2) We know most about what is occurring at this location from the long-term moored instruments. Interannual

variability is the dominant mechanism in the spectrum of ice concentration

here.

Area 1: This area is in a region where ice can form locally. The ice observations show that there is huge interannual variability in the concentration

of ice, which provides a most surprising result.

Pribilof I.: Some of the variation in ice concentration here occurs because

of the influence of the islands on regional and tidal currents. St. George

Island typically has minimal ice compared to St. Paul Island. The patterns

of ice concentration are very distinct from other areas. For example, in

Area 7 there is ice 5–6 months of year, while there may be none at all

around the Pribilof Islands.

Area 5: The warmer slope waters bathe this area, and ice is a transient

feature. There were significant concentrations in the early 1970s (cold period), and, after the regime shift of 1976/77, ice was an infrequent visitor

to this area. The dominant energy of this area occurs in the year-to-year

variability.

For indices of sea ice (Table 3.1), the average percent concentration of sea

ice within a rectangular region bounded by 58◦N, 170◦W and 57◦N, 165◦W

was computed during the period 15 March to 15 April over the active research

years of SEBSCC.

Sea ice has a marked impact on the ecosystem of the eastern Bering

Sea. It provides habitat for marine mammals, and its presence is related

to timing and magnitude of primary production (e.g., Stabeno et al., 1998;

Hunt et al., 2002a). While a single index of ice can provide an indication

of ecosystem characteristics, this analysis demonstrates that a single index

of ice concentration is not valid for the entire SEBSCC study area. Because

time series observations exist, indices can be created for any region of interest

over the shelf.

3.3.5 Indices related to the cold pool and adjacent fronts (Peggy

Sullivan)

Cooling and mixing associated with sea ice advance help to condition the entire water column over the Middle Shelf Domain, generating the cold lower

layer of water known as the “cold pool” (Schumacher and Stabeno, 1998;

Stabeno et al., 1998). With seasonal heating, the lower layer becomes insulated and temperatures often remain below 2.0◦C. The area of this cold pool

varies by ∼2.0 × 105 km2 between maximum and minimum extent. The
90 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

mechanisms that connect the cold pool to biota include changing habitat

location (species composition) and alteration of biological rates.

Temperature cross sections along two southeastern Bering Sea transects

are assessed over the years, 1996, and 1998–2000. MicroBathythermograph

(MBT) temperature profile data from the Alaska Fisheries Science Center

(AFSC) annual trawl surveys were used. Fig. 3.16 shows the areal extent

and well-gridded structure of the trawl survey data, with data-collection sites

shown by blue triangles, and red triangles marking the two transects which

were processed and used for present purposes. The transects were chosen to

represent distinctive regions near the perimeters of the trawl area, while still

residing within solid data-coverage range for the entire area. Transect 1 (T1),

the more northwesterly line, optimally contains nine data-collection points,

while Transect 2 (T2), the more southeasterly line, optimally contains 11

data-collection points. Not all points exist for each year. Kachel et al. (2002)

provide insight into processes related to the cold pool and inner front based

on temperature, salinity, and dissolved nutrient observations, and Stabeno

et al. (2002) provide insight into processes along the 70-m isobath.

Observing successive years across the same transects allows a comparative view of the behavior of the middle shelf cold pool, its year-to-year

variations and overall shelf temperatures. The cold pool is well defined for

all years along T1 (Figs. 3.17–3.20). This transect is not close to shore, so

the coastal shelf is not well represented. The warm years of 1996, 1998, and

2000 had relatively warmer surface waters. During 1999, overall temperatures are colder, and the cold pool is cooler and shifted more shoreward. For

both 1999 and 2000, the cold pool is shallower than in 1996 and 1998.

At transect 2, Fig. 3.21 shows a well-defined cold pool in 1996, deeper

than in subsequent years, with horizontal temperature variability and moderately warm temperatures. During 1998–2000 (Figs. 3.22–3.24), the cold pool

is more horizontally dispersed. The year 1998 had warmer overall temperatures, and the colder years 1999 and 2000 showed cold water more shoreward

into the vertically mixed coastal shelf. The cold pool index in Table 3.1 shows

the estimated area (km2) of water colder than 2◦C in the vertical section of

the T1 transect.

It should be noted that MBT trawl survey data from the AFSC are the

subject of a data rescue, and, when completed, will be a rich data resource.

Data profiles from 1992 through 2000 number approximately 3000. The

physical data portion of the survey is being processed with normal PMEL

methodologies, quality control, and web placement.

3.3.6 Pribilof age-0 pollock as an index of pollock year-class strength

(Jim Ianelli)

Each year the EBS pollock stock is analyzed for fishery management purposes. This analysis consists of an integrated statistical assessment based on

several different types of observations. The primary data analyzed include

annual bottom-trawl surveys of the entire shelf area (within the U.S. EEZ),

bi-annual echo-integration trawl (EIT) surveys of this region, annual fishery

catch data from scientific observers aboard fishing vessels, and estimates of
The Southeast Bering Sea Ecosystem—Part 3 91

62°N

60°N

58°N

56°N

54°N

178°W 174°W 170°W 166°W 162°W 158°W

Figure 3.16: Trawl survey data collection grid in the Bering Sea. Data locations from the 1996 data set

were used. Red triangles indicate transects 1 and 2.

Transect 1

July 8–14, 1996

173°W 172°W 171°W 170°W

0

20

40

60

80

100

120

140

160

180

200

220

0 40 80 120 160 200

7.00

5.00

3.00

1.00 I-26 J-25 K-24 L-23 M-22 N-21 O-20

DEPTH (M)

Figure 3.17: Temperature contours along T1 for 1996.
92 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Transect 1

July 8–19, 1998

0

20

40

60

80

100

120

140

160

180

200

220

DEPTH (M)

8.00

6.00

4.00

2.00

173°W 172°W 171°W 170°W 169°W

I-26 J-25 K-24 L-23 M-22 N-21 O-20 P-19 Q-18

Figure 3.18: Temperature contours along T1 for 1998.

Transect 1

June 14–26, 1999

0

20

40

60

80

100

120

140

160

180

200

220

DEPTH (M)

173°W 172°W 171°W 170°W 169°W

I-26 J-25 K-24 L-23 M-22 N-21 O-20 P-19 Q-18

4.00

2.00

0.00

Figure 3.19: Temperature contours along T1 for 1999.

biological characteristics (e.g., age composition, growth, and maturation).

These assessment analyses lead to recommendations on risk-averse harvest

levels for the coming year.

The risk-averse aspects of harvest recommendations include levels of uncertainty in both the observations and the physical processes affecting the

population. For example, pollock in this region are well known to have highly

variable year classes (coefficients of variation on the order of 60%). As this

variability propagates through the population, a key variable in quota determination is affected: estimated pollock biomass. It is therefore useful to be

able to estimate reliably the abundance of incoming (pre-recruit) pollock.

One suggestion is that for a relatively small sampling effort, abundance of

age-0 pollock around the Pribilof Islands can provide a useful index of yearclass strength. Preliminary comparisons (Swartzman et al., 2002) of such an
The Southeast Bering Sea Ecosystem—Part 3 93

Transect 1

June 18–July 14, 2000

8.00

6.00

4.00

2.00

0

20

40

60

80

100

120

140

160

180

200

220

DEPTH (M)

L-23 N-21 O-20 P-19 Q-18

171°W 170°W 169°W

Figure 3.20: Temperature contours along T1 for 2000.

Transect 2

June 12–27, 1996

0

20

40

60

80

100

120

140

160

180

200

220

DEPTH (M)

166°W 165°W 164°W 163°W 162°W 161°W

7.50

5.50

3.50

1.50

6.50

4.50

2.50

0 100 200 300

A-02 B-03 C-04 D-05 E-06 F-07 G-08 H-09 K-12 I-10 J-11

Figure 3.21: Temperature contours along T2 for 1996.

index with results from assessment analysis (based on age compositions from

the fishery and two independent surveys, abundance indices from two surveys, growth rates, maturity information, natural mortality rates, and catch

of the fishery; Ianelli et al., 2001) suggest that an index of this type might

prove useful (Fig. 3.25, Table 3.1). This is particularly appealing when one

examines similarly constructed indices with other pollock surveys since both

the bottom trawl and EIT surveys provide some indication of pre-recruit

abundance at later stages (Figs. 3.26 and 3.27). The root mean squared

errors for these three indices (relative to the assessment model prediction in

log-space) are:

Note that in the assessment analyses, the overall abundance estimates

(e.g., the area-swept estimate of total population numbers) are treated in-
94 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

0

20

40

60

80

100

120

140

160

180

200

220

DEPTH (M)

Transect 2

June 13–27, 1998

8.00

6.00

4.00

3.00

7.00

5.00

166°W 165°W 164°W 163°W 161°W 162°W

B-03 C-04 D-05 E-06 F-07 H-09 I-10 J-11 K-12

Figure 3.22: Temperature contours along T2 for 1998.

Transect 2

May 25–June 7, 1999

0

20

40

60

80

100

120

140

160

180

200

220

DEPTH (M)

5.00

3.00

1.00

0.00

4.00

2.00

166°W 165°W 164°W 163°W 161°W 162°W

A-02 G-08 B-03 C-04 D-05 E-06 F-07 H-09 I-10 J-11 K-12

Figure 3.23: Temperature contours along T2 for 1999.

Pribilof Age 0 Age 1 Bottom trawl survey Age 3 EIT survey

0.725 0.532 0.713

dependently from estimates of the age composition. This appeals to the

way the population is sampled and sub-sampled for age compositions and

avoids independence problems of statistically fitting several separate sets of

indices-at-age. Furthermore, the indices as presented above are only for representation and do not reflect the fact that the assessment model allows for

changes in age-specific availability over time (Ianelli et al., 2001).
The Southeast Bering Sea Ecosystem—Part 3 95

Transect 2

May 26–June 12, 2000

0

20

40

60

80

100

120

140

160

180

200

220

DEPTH (M)

166°W 165°W 164°W 163°W 161°W 162°W

5.00

3.00

1.00

6.00

4.00

2.00

A-02 G-08 B-03 C-04 D-05 F-07 H-09 I-10 J-11 K-12

Figure 3.24: Temperature contours along T2 for 2000.

0.0

0.5

1.0

1.5

2.0

2.5

3.0

1993 1994 1995 1996 1997 1998 1999 2000

Index

Model

Figure 3.25: Pribilof young-of-the-year index relative to assessment results from

Ianelli et al. (2001). Units are arbitrary.

3.3.7 Model simulations

What is NEPROMS? (Al Hermann). The Regional Ocean Modeling System (ROMS) is a versatile, state-of-the-art, free-surface, hydrostatic,

primitive equation, ocean circulation model developed at Rutgers University

and UCLA. Details can be found in Haidvogel et al. (2000), Shchepetkin and

McWilliams (1998), and Marchesiello et al. (2001), and on the ROMS web

site (http://marine.rutgers.edu/po/index.php). The ROMS user community now numbers more than 200 scientists worldwide. The version of

ROMS used for our simulations achieves distributed memory parallelism using the Scalable Modeling System developed at NOAA’s Forecast Systems

Laboratory.

Using this parallel version of ROMS, we have been implementing a suite

of basin-, regional-, and local-scale circulation models, linked via one- (and
96 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

0

0.5

1

1.5

2

2.5

3

1982 1984 1986 1988 1990 1992 1994 1996 1998 2000

Age 1 Bottom trawl index

Model

Figure 3.26: NMFS bottom trawl survey age-1 index relative to assessment results

from Ianelli et al. (2001). Units in numbers rescaled to have a mean value of 1.

0

0.5

1

1.5

2

2.5

3

3.5

1979 1984 1989 1994 1999

EIT Age 3

Model

Figure 3.27: NMFS EIT survey age-3 index relative to assessment results from

Ianelli et al. (2001). Units in numbers re-scaled to have a mean value of 1. Note

that the observations do not occur in every year.

eventually two-) way coupling. Figure 3.28 shows the set of nested modeling

domains in current use, including: a basin-scale model encompassing the

North Pacific Basin at 20–40 km resolution (NPac), a regional model at ∼10-

km resolution spanning the Northeast Pacific (NEP), and finally local models

at ∼3-km resolution in regions of specific interest [California Current System

(CCS) and the Coastal Gulf of Alaska (CGOA)]. The NEP grid spans the

area from Baja California up through the Bering Sea at approximately 10-km

resolution, and was used for the simulations of the Bering Sea reported here.

Additional information on these models (with snapshots and animations

of model output) is available at http://www.pmel.noaa.gov/∼dobbins/

nep.html.

The NEP model is forced with daily average wind (converted to wind

stress) and heat fluxes (sensible, latent, longwave, and shortwave) from the
The Southeast Bering Sea Ecosystem—Part 3 97

Figure 3.28: Nested modeling domains in current use for Pacific modeling, including: a basin-scale model encompassing the North Pacific Basin at 20–40 km

resolution (NPac, red), a regional model at ∼10 km resolution spanning the Northeast Pacific (NEP, green), and finally local models at ∼3 km resolution in regions

of the California Current System (CCS, light blue) and the coastal Gulf of Alaska

(CGOA, light blue).

NCEP reanalysis project. No tidal forcing is included in these simulations.

Model runs were initialized with Levitus monthly climatology for T and S

fields. Hindcasts for a particular year were achieved by spinning up the NEP

model from January of the previous year; for example, the model was spun up

using daily NCEP values from January 1999–March 2000 for hindcasts of the

period April–July 2000. Although even longer spin up would be preferable,

15 months is a sufficiently long period to evolve the appropriate mesoscale

detail in the boundary currents and gyres (which is not contained in the

coarse-scale Levitus-based initialization). It is also long enough to allow

NCEP forcing to produce conditions (e.g., SST and currents) appropriate

to the hindcast year (Fig. 3.29). Buoyancy input for the Gulf of Alaska

was derived from the analyses of Royer (1982 and Old Dominion University,

personal communication) and is added at the surface of the water column

along the coast in the CGOA. The weakly dissipative algorithms for tracer

advection in ROMS obviate the need for strong explicit mixing; here, explicit

Laplacian horizontal mixing coefficients were set at 50 m2 s−1 for momentum

and 25 m2 s−1 for temperature and salinity.

ROMS includes algorithms for passive float tracking in three dimensions.

Floats can be seeded at arbitrary locations and times, and re-seeded as

required for the numerical experiment. Output includes Lagrangian time
98 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

62

58

54

50

180 184 188 192 196 200 204 208

6

5.6

5.2

4.8

4.4

3.6

3.2

2.8

2.4

2

1.6

1.2

0.8

0.4

0

–0.4

–0.8

–1.2

–1.6

–2

4

Figure 3.29: Model-generated sea surface temperature in the southeastern Bering

Sea for 15 April 2000. Axes denote latitude (◦N) and longitude (◦E). Bathymetry

is contoured in meters. Note the penetration of warmer waters from the Gulf of

Alaska through Unimak Pass.

series of position, depth, salinity, temperature, and density. We have utilized

this feature to track the temperature history of passive particles during NEP

runs, as an approximation to the life history of passive planktonic organisms,

e.g., pollock larvae.

ROMS simulations of pollock egg and larvae transport (Dylan

Righi). Several mechanisms can connect transport of eggs and larvae to

survival, including: transport to regions where predation is great due to cannibalism, and transport to regions where primary and secondary production

is minimal (reduced prey availability). In this contribution, only the characteristics of the transport itself are addressed. The Northeastern Pacific

Regional Ocean Model System (NEPROMS) was used to simulate drifter

trajectories in the southeastern Bering Sea. Drifter tracking in ROMS is

done using a fourth-order, predictor-corrector scheme. Drifters are allowed

to move vertically. We currently have results for the years 1997–2001.

The simulated drifters are initialized over the eastern Bering Sea shelf just

north of Unimak Island and to the northeast of Unimak Pass. This region
The Southeast Bering Sea Ecosystem—Part 3 99

Figure 3.30: Simulated drifter initial horizontal (left) and vertical (right) positions.

is known to be an area of strong spawning for walleye pollock. The initial

drifter positions fill out a 7 × 7 grid with horizontal separations of about

10 km (Fig. 3.30). Vertically, there are 15 drifters initialized at each grid

point with maximum depths just over 40 m. The drifter initial positions are

denser near the surface, replicating vertical egg distribution data collected

in the Bering Sea (Kendall, 2001). Drifters are released on 1 April of each

year and are tracked for 90 days.

Endpoints after 90 days for drifter trajectories from the 1997–2001 runs

are shown in Fig. 3.31 (this plot shows all drifters at all depths). In all

years, there is a strong tendency for trajectories to move to the northeast up

the Alaskan Peninsula. The other common path is movement to the northwest along the 100-m isobath. These patterns are qualitatively similar to

circulation schematics that were constructed from current, satellite-tracked

drifter and hydrographic observations (e.g., Schumacher and Stabeno, 1998).

The split between these two paths is seen clearly in the 1998, 1999, and 2001

drifter endpoints. In 1997, the full trajectory plots (not presented here) show

that a subset of the drifters begin following the standard 100-m isobath path,

but then currents change and drive them up the shelf to the northeast. The

endpoints in 2000 are the result of strong turning to the northwest of trajectories that previously had been moving up the Alaskan peninsula. Further

study of possible forcing mechanisms is needed to understand what leads to

these years departing from the archetypal two-limbed flow.

The initial goal of this work was to compare simulated trajectories from

a full primitive equation model with those from the Ocean Surface Current

Simulations (OSCURS) numerical model. OSCURS computes daily surface

current fields using daily sea level pressure and long-term mean geostrophic
100 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

current data. As such, it is a simpler model in terms of the physics involved

and is much less computationally expensive. Wespestad et al. (2000) used

OSCURS to create simulated trajectories in the Bering Sea. The initial grid

used here was centered on the initial release point they used. Our trajectories

for drifters released near the surface (0 to 5 m depth) show good agreement

with the OSCURS results. However, our results show variation of trajectory endpoints with changes in both horizontal and vertical initial position.

Figure 3.32 shows the full trajectories for the 2001 simulated drifters.

The upper left panel of Fig. 3.32 shows the tracks of all the drifters

released for the run. The upper right and the bottom panels show drifter

tracks as a function of their release depth. The first facet of these plots is

that a drifter’s endpoint depends largely on its initial horizontal placement.

The next point is that there is a strong dependence on release depth.

The OSCURS 2001 trajectory moves a short distance to the northeast up the

Alaskan Peninsula, as do the majority of the NEPROMS drifters released

in the upper 5 m of the water column (upper right panel of Fig. 3.32).

Nevertheless, with deeper release points comes a stronger divergence of the

trajectory fates. In the 5–20 m and 20–40 m release bins, there are significant

numbers of drifters that join the 100-m isobath flow to the northwest, with

some even moving through Unimak Pass before turning back. OSCURS

results completely miss this variation in particle fates.

As a working index of advection, Table 3.1 shows the fraction of each

year’s 735 simulated drifters that landed in the region bounded by 57◦–58◦N,

165◦–170◦W after 90 days.

3.3.8 A matrix for cataloging indices (Allen Macklin)

Early in the brainstorming process, members of the Indices Working Group

suggested that a matrix would be a valuable data cataloging tool and an aid

in developing and testing indices. At a minimum, a matrix would help with

the housekeeping tasks of data tracking. At its fullest development, a matrix would be a multi-dimensional catalog of SEBSCC (and other relevant)

data, accessible through the Internet, with direct links to referenced data

collections. Presently, the character of the matrix is somewhere between the

extremes. It is viewed as a dynamic document that is one of SEBSCC’s

legacies for future research in the region.

Figure 3.33 illustrates the Microsoft Excel version of the matrix. It is

available by linking to “Phase III Working Groups” on the SEBSCC web

site at http://www.pmel.noaa.gov/sebscc. Note that each index or data

variable in column 1 (scrollable) is cataloged in two dimensions: time and

space. Each index also contains a description, e.g., the yellow box in Fig. 3.33

describes the “Thermocline” index. Passing the computer’s mouse over the

cell containing the index name activates the description. To the right of

column 1 are scrollable, color-coded columns for the years contributing to

the matrix, 1972–2001. Each color-coded column is divided into four sub

columns: annual average, spring bloom, summer, fall overturn. These represent the important time scales and seasonal periods for the matrix. Cells
The Southeast Bering Sea Ecosystem—Part 3 101

Figure 3.31: Endpoints for 90-day drifter trajectories for 1997–2001.
102 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Figure 3.32: Full trajectories for the 2001 90-day simulated drifters. The upper left panel shows all drifters,

while the other panels show drifters divided as a function of initial release depth.

in the matrix contain values for that index during that season of each year

or links to data that provide values. Not all cells are occupied at this time.

At the bottom of Fig. 3.33 are labeled tabs that represent the spatial

dimension of the matrix. The matrix encompasses the same geographic

domains that are discussed elsewhere in this document (Section 3.3.4) and

described by Hunt et al. (2002a). Clicking on a tab brings to the screen an

identical grid of indices and years, but the cells are populated with values

specific to the region selected.

Besides the indices matrix, there is also a matrix of data available for
The Southeast Bering Sea Ecosystem—Part 3 103

Figure 3.33: A portion of the SEBSCC indices matrix showing some indices for the South Outer Shelf

region during 1997 and 1998.

the Pribilof Islands. This matrix, too, can be found by linking to “Phase III

Working Groups” on the SEBSCC web site.

3.4 Application of a Transport and Predation Index to Recruitment for Stock Assessment Purposes (Jim Ianelli)

3.4.1 Background

Recruitment or year-class strength (the numbers of fish successfully surviving larval and post-larval stages, e.g., to age 1) is a fundamental part of

the population model used for pollock stock assessments (e.g., Ianelli et al.,

1998). The recruitment estimates provide the basis for population numbers

that are tracked over time and modified by various natural and fishing mortality sources. The statistical assessment models used in these analyses are

similar to state-space models where unknown and unobservable quantities

(e.g., numbers of fish) are modeled separately as “state” variables. “Observation” equations are then constructed to translate the state variables into

model predictions. These predictions are then compared with actual data

to tune the state variables (i.e., the fundamental parameters) using statistical methods such as maximum likelihood. Schnute (1994) provides a good

description of this general approach.

Estimates of year-class strengths (e.g., Fig. 3.34) thus are derived from
104 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

a variety of information used in the assessment model. This information

includes annual bottom-trawl surveys of the entire shelf area (within the

U.S. EEZ), bi-annual echo-integration trawl (EIT) surveys of this region,

annual fishery catch data from scientific observers aboard fishing vessels,

and estimates of biological characteristics (age composition, growth, and

maturation, etc.). These assessment analyses lead to recommendations on

risk-averse harvest levels for the coming year. The recommendations are influenced by estimates of the underlying stock-recruitment relationship within

the integrated assessment model (the so-called stock-recruitment sub-model;

Fig. 3.35). Further, the stock-recruitment sub-model can be modified to incorporate environmental indices. Because the framework of the integrated

model separates the state dynamics from the observations, it is relatively

straightforward to test alternative hypotheses. We illustrate one such modification to evaluate the impact of progressively adding recruitment effects

beginning with the simplest form (a mean and deviation) and then additional

terms related to the stock size and any environmental terms and process errors. These can be written as recruitment state variables:

R = Mean + ξ, (1)

R = f(S) + ξ, (2)

R = f(S) + E, (3)

R = f(S) + E + ξ, or (4)

R = f(S) + E+ ξ, (5)

where R is an actual (unobserved) state variable (log-recruitment) for a

given year, and the error term ξ can be thought of as one that encompasses

all “natural” variability that is not described by other terms in the above

sub-models. For example, sub-model (3) is written such that all recruitment variability is determined by the stock-recruitment function f(S) and

an environment term E. This simply means that the there is no residual

process error in recruitment specifications (i.e., the model terms determine

all of the variability in recruitment. For sub-model (5), the conditions for

the environment term E(described below) are “smoothed” with respect to

spatial correlation.

In searching for the appropriate environmental term(s), water temperature, turbulence, and transport have been related to success of recruitment

for many marine species (e.g., Quinn and Niebauer, 1995). Here, we follow

the construct that the transport of planktonic stages is important to establish separation of young pollock from their cannibalistic adults (Wespestad

et al., 2000). We used the Ocean Surface Current Simulations (OSCURS)

numerical model (Ingraham and Miyahara, 1988) as a means to represent the

effective interannual variability of advective egg and larval transport. The

model simulates transport trajectories for passive, floating (or near-surface)

objects that are propelled by ocean surface currents forced by wind.
The Southeast Bering Sea Ecosystem—Part 3 105

0

25,000

50,000

75,000

1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999

Year Class

Numbers at age 1

Figure 3.34: Time series of year-class abundance at age 1. Error bars represent

approximate 95% univariate confidence bounds. Note that covariance estimates

among adjacent year classes is typically highly negatively correlated. Source: Ianelli

et al. (2001).

0

60

0 1,000 2,000 3,000 4,000

Female spawning biomass (thousands t)

Age 1 Recruitment (billions)

0

20

40

80

0 1,000 2,000 3,000 4,000

Figure 3.35: Stock-recruitment relationship showing the fitted effect due to spawning biomass (thick line) and estimates from the stock assessment model. Note that

the large dots are used to tune the stock-recruitment sub-model while the smaller

dots represent points from the regime prior to 1977. Dashed lines represent approximate 95% confidence bands about the curve. Source: Ianelli et al. (2001).

3.4.2 An application

The following serves to illustrate one method for incorporating environmental information within an integrated stock assessment approach such as that

currently used for EBS pollock. Incorporation of any index can be done in a

similar fashion. The integrated assessment model is a good way to evaluate

candidate indices because consistency with all available data can be easily

evaluated. In addition, compared to methods simply using recruitment residuals, the integrated model provides a more statistically defensible approach

as fewer assumptions are required (e.g., analyses of residuals done separately
106 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

typically fail to account for the uncertainty of the estimates of the residuals

themselves). A disadvantage of the integrated assessment approach is that

a fair amount of specialization is required to run the models.

The OSCURS model was run using a single launch site (55.5◦N, 165◦W)

for 90-d trajectories starting 1 April. This location and date were selected

based on the distribution and timing of the main concentrations of spawning

pollock. The variability of this location appears to be moderate based on the

catch-distribution of the fishery for pre-spawning pollock (Fig. 3.36). However, this observed variability is partly attributed to fishing fleet behavior

under variable ice conditions and management measures.

Simulations were generated for the years 1964 to 1998 (Fig. 3.37). The

endpoints of these trajectories suggest that the young-of-year pollock are

either just seaward of the inner front or in the coastal domain as defined in

Kachel et al. (2002). To quantify the effect these trajectories may have on

recruitment success, we chose to compute the average location of the 90-day

drifter. The average location represents the characteristics of the advective

forces better than the endpoints and reduces the impact of drift outliers (i.e.,

high-levels of drift over a few days). These average locations were then placed

in the context of a grid (Fig. 3.38) in order to geo-reference the advection

by year. Each year, therefore, was assigned to a grid-cell (but not all gridcells had observations). The integrated assessment model was then used to

estimate the “effect” a particular grid-cell has relative to recruitment success.

In other words, each cell was estimated as being favorable, unfavorable, or

neutral to pollock year-class strength.

To make the model well conditioned, two penalty weights were added to

the (negative-log) likelihood function. First, a very small grid-cell penalty

was added so that for cells with no observations, the estimated recruitment

effect would be zero (yet still with the desired high variance). The second

(more important) penalty effectively conditions the grid surface values to

have a non-parametric surface such that there is some degree of spatial correlation. The value of this penalty weight affects the “smoothness” of the

surface by way of multiplying the sums of squared third-differences between

grid-cell values in both the lateral and longitudinal directions (Ianelli et al.,

1998). In the limit, as the value of this multiplier becomes large, the effect

of the grid on explaining recruitment variability goes to zero (i.e., the twodimensional surface is represented by a single mean value). As the smoothness penalty multiplier tends toward zero, the cells become independent from

contiguous (and all other) cells. Alternative values of this multiplier have

been explored and are shown in Figs. 3.39a and b. Note that in Fig. 3.39a,

the “effect” of larval drift is more irregular compared to the model where

smoothness in space is assumed (Fig. 3.39b). Root-mean squared errors of

all five sub-models for this application are shown in Table 3.2.

3.4.3 Discussion

For the eastern Bering Sea, wind-driven advection of surface waters containing planktonic stages of pollock (Wespestad et al., 2000) and Tanner crabs

(Rosenkranz et al., 2001) appears to account for some of the observed fluc-
The Southeast Bering Sea Ecosystem—Part 3 107

(a)

(b)

Figure 3.36: Top panel (a): Average catch distribution by 10-day periods, 1992–2002. Bottom right corner

is average over all years combined. Bottom panel (b): Average catch distribution of the pollock fleet targeting

pre-spawning pollock by year, 20 January–28 February.
108 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

60°N

58°N

56°N

54°N

170°W 165°W 160°W

Figure 3.37: Trajectories resulting from 90-day simulated wind drift from the initial launch site (annotated

with a star). The size of the labeled year indicates the relative strength of that year class.

58°N

56°N

54°N

170°W 165°W 160°W

Figure 3.38: Arbitrary grid locations (crosses) and average location of 90-day simulated drifters (begun at

the star from 1 April) indicated by two-digit year (number).
The Southeast Bering Sea Ecosystem—Part 3 109

(a)

(b)

58°N

56°N

54°N

58°N

56°N

54°N

58°N

56°N

54°N

58°N

56°N

54°N

168°W 164°W

168°W 164°W

Figure 3.39: Graphical results of fitting a drift component within the integrated

stock assessment model. Red areas indicate higher recruitment success while blue

represents lower values. Panel (a) is the result for Model 4 (not smoothed) compared

with Model 5 (where some degree of spatial correlation is specified).

tuations in year-class strength. In these studies, the biological process that

links advection to year-class strength is differential survival.

The role of cannibalism in influencing population dynamics resulting

from the strong year class has been examined (Livingston and Methot, 1998).

For the age-0 pollock, predation by older pollock can have a strong influence

on year-class strength.

A number of caveats exist in the above analysis. For example, the mechanistic model is one that deals with the negative effects of spatial overlap

between age-1 juveniles with pollock that are generally older than age 2
110 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Table 3.2: Root-mean squared errors of recruitment sub-model results.

Model

Model component 1 2 3 4 5

Mean 0.00 — — — —

Stock-recruitment function, f(S) — 0.04 0.08 0.09 0.09

OSCURS avg. drift — — 0.48 0.21 0.11

Residual (ξ) 0.75 0.72 — 0.21 0.28

Total 0.75 0.75 0.56 0.51 0.47

(Fig. 3.40). Analyses of an index of separation (Equation 6) may provide

some insight on whether other data are consistent with this process.

Ii =

ni

all stationsi

(page2+ − page1)2

√ni

(6)

where page1 and page2+ are the proportions at age 1 and ages 2 and older,

respectively. These values were computed at each station sampled during a given survey (Wespestad et al., 2000). Results compared with logrecruitment suggest a very weak relationship between separation index and

year-class strength (Fig. 3.41).

Another caveat for this approach is that there are physical and biological assumptions imbedded in how accurately OSCURS simulates the actual

transport of pollock eggs and larvae. From a purely physical perspective,

we accept that OSCURS is a surface wind-drift model most applicable to

approximately the upper 10 m of the water column and to regions where the

ambient subtidal currents are weak. For much of the southeastern shelf, the

climatological mean currents are weak, yet more active flow has been observed along the 100-m isobath and over the outer shelf/slope (Schumacher

and Stabeno, 1998; Reed and Stabeno, 1996; Reed, 1999; Stabeno et al.,

2001). One solution is to compare the results from OSCURS to those from

more sophisticated hydrodynamic models. Early indications suggest that

there is good agreement, except in areas where depth dynamics are particularly acute, e.g., around the Pribilof Islands.

From a purely biological perspective, the question of location of planktonic stages in the water column, and selection of a single launch site must

be examined. We show some indication of interannual variability of spawning location (Fig. 3.36) that may be useful to improve launch-site locations.

Additionally, multiple launch sites in space and time may also better represent the effect of advection on pollock eggs and larvae. While extensive work

has not been conducted on the vertical location of eggs and larvae, over the

shelf, larvae do exist in the upper 10 m, but they are often found at greater

depths (Napp et al., 2000). This raises the question on what fraction of the

planktonic life history stages would follow the wind-drift.

Spawning also occurs near Bogoslof Island and other locations over the
The Southeast Bering Sea Ecosystem—Part 3 111

0

50

100

150

200

250

300

10 20 30 40 50 60 70 80

Predatory pollock (cm FL)

Length of consumed pollock (mm SL)

 n = 6,723

Figure 3.40: Length of pollock consumed compared to the length of the consuming

pollock based on NMFS food-habits studies (Troy Buckley, NOAA/AFSC, personal

communication). 10.5 10.0 9.5 9.0 8.5 ln (Abundance)

123456

Separation Index

1982 1989

1992

1995 1990

1991

1999 1981 19981997

1983 19851993

1988

1987

1996

1984

1986

1994

Figure 3.41: Separation index relative to estimates of the log-pollock abundance

at age 1, 1982–1999. Numbers plotted represent year-class.

slope and basin of the eastern Bering Sea. These regions were hypothesized

to be a source of larvae for the southeastern shelf (Francis and Bailey, 1983).

If significant spawning occurs off the shelf where high concentrations of larvae have been observed (Schumacher and Stabeno, 1994), then the simple

set of initial conditions for the drift-effect on recruitment are further complicated. It appears that larvae in these waters are likely to be deeper in the

water column (Napp et al., 2000) and the background currents are typically

stronger than over the middle shelf (Stabeno et al., 1999a). Alternative drift

locations to represent the starting points of egg and larval stages may require

further exploration, particularly if there is a significant amount of pollock

egg production from the Aleutian basin region.
112 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

3.5 Changing Relationships between Climate and Biological

Indices in the Eastern Bering Sea (Alan Springer)

3.5.1 Introduction

Climate change, whether considered at local or global spatial scales and annual or millennial time scales, is among the most controversial and intriguing

environmental issues confronting us today. The obvious concerns over climate change are effects it has on ecosystem structure and function, food

web productivity, the abundance of individual species, and the economies

and livelihoods of people the world over.

Nowhere have the effects of climate change on marine ecosystems been

better documented than in the North Pacific Ocean (e.g., McGowan, 1990;

Ebbesmeyer et al., 1991; Beamish, 1995; Mantua et al., 1997; Francis et al.,

1998; Klyashtorin and Rukhlov, 1998; Springer, 1998; Welch et al., 1998;

Anderson and Piatt, 1999; Hare et al., 1999; Hare and Mantua, 2000). The

common approach in most analyses of climate and other ecosystem components has been to compare slopes of parameter values over time, slopes that

change when the climate changes from one quasi-stable state, or regime, to

another. Regime shifts are defined by abrupt, sustained polarity reversals of

the Pacific Decadal Oscillation (PDO) (Trenberth and Hurrell, 1994; Mantua

et al., 1997).

In contrast, there have been comparatively few analyses of relationships

between two variables when time was not one of them. In order to better

understand ecosystem processes that respond to climate change and regime

shifts, it would be helpful to have correlations between things that are, or

might be, functionally related. For example, correlations between fluctuations in primary physical forcing parameters, such as the Aleutian Low

pressure field, and some response variables, such as wind, mixing depth, and

primary productivity, as was modeled for the Gulf of Alaska by Polovina

et al. (1995); and temperature and zooplankton population dynamics in the

Gulf of Alaska (Mackas et al., 1998).

A small assortment of data sets for the Bering Sea can be grouped in

ways that yield marginally miscellaneous and eclectic, but compelling, relationships. These relationships provide insight into time and space scales of

interest and the sensitivity of ecosystem components and processes to climate change. This includes changes in the general climate state, e.g., PDO,

and to interannual fluctuations in climate within and across regimes. Some

of the relationships are between adjacent ecosystem elements that could be

thought of as functional cause and effect relationships. Other relationships

are separated by more than one element. Such ostensibly functional relationships are useful to know, even if the variables are separated by more

than one trophic level, since they may give clues about where and how to

look for processes responsible for biomass yield at any trophic level.

3.5.2 Observations

The time series used to develop and examine potential relationships are:
The Southeast Bering Sea Ecosystem—Part 3 113

a. North Pacific Index (NPI)—area averaged mean sea level pressure anomaly

over the region 30◦ to 65◦N, 160◦E to 140◦W (Trenberth and Hurrell,

1994; Minobe, 1999);

b. Sea surface temperature (SST)—mean temperature of a 5◦ × 5◦ block

centered on (55◦N, 170◦W) (Cayan, unpublished data);

c. Bering Sea wind measured at St. Paul Island by the U.S. National Weather

Service (Note, the wind speed has been cubed so that it provides an

index of mixing.);

d. Sea-ice coverage over the eastern Bering-Chukchi shelf (Niebauer, 1998

and unpublished data);

e. Bering Strait transport—annual northward transport through Bering Strait

(Roach et al., 1995; T. Weingartner, unpublished data);

f. Global ocean temperature (NCDC, 2004);

g. Bowhead whale baleen stable carbon isotope ratios (∂13C) (Schell, 2000);

h. Steller sea lion abundance (NMML, 1994 and unpublished data; Trites

and Larkin, 1996);

i. Age-1 pollock abundance and age-3+ pollock biomass (J. Ianelli, unpublished data);

j. Red-legged kittiwake productivity at St. George Island, Pribilof Islands

(Dragoo et al., 2001);

k. Seabird abundance at the Pribilof Islands and Aleutian Islands (Dragoo

et al., 2001);

l. Fur seal pup production on St. Paul Island, Pribilof Islands (York and

Hartley, 1981; York and Kozloff, 1987; A. York, unpublished data);

m. Fur seal growth rates (A. Trites, unpublished data); and

n. Common murre and thick-billed murres relative abundance at Walrus

Island, Pribilof Islands (Peterson and Fisher, 1955).

3.5.3 Physical-physical relationships

Despite considerable uncertainty about the relative importance of processes

governing community structure and the abundances of individual species,

e.g., top-down versus bottom-up interactions, much which transpires in ecosystems begins with physics. Francis et al. (1998) have presented a conceptual model of the pathways by which changes in atmospheric features can

influence biota, and Schumacher et al. (2003) have modified this model for

the Bering Sea (Fig. 3.3). Both of these models show that physics can influence an ecosystem in either top down or bottom up modes. For the North

Pacific and eastern Bering Sea, much of the physics begins with the Aleutian

Low pressure system that can be scaled by the North Pacific Index (NPI).
114 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Winter sea ice extent and duration in the Bering Sea are related at

the decadal scale to mean atmospheric forcing (Niebauer, 1998; WyllieEcheverria and Wooster, 1998; Stabeno et al., 2001). Sea ice influences

the ecosystem through at least two pathways. First, the presence of sea over

the southeastern shelf in March (when ambient light is adequate to sustain

primary production) can result in an early phytoplankton bloom (Stabeno

et al., 1998). An early bloom may result in a greater energy flow to the

benthic community, whereas a bloom later in spring likely favors pelagic

pathways (Walsh and McRoy, 1986; Hunt et al., 2002a). The extent of sea

ice is also vital to the subsequent development of the middle shelf cold pool

that appears to influence the distribution of pollock and Arctic cod. The

timing and magnitude of the summer retreat of sea ice is related to summer climate, which can be indexed by the NPI (Fig. 3.42). Recent results

indicate that regions of the Bering Sea have shifted toward an earlier spring

transition (Stabeno and Overland, 2001). The retreat and melting of sea ice

also have ecosystem effects as they alter the availability and distribution of

essential habitat for symphagic food webs and pagophilic marine mammals.

In addition, they are related to energy flow through the ecosystem as noted

above.

One of the hallmark features of the Bering Sea is the transport of water

north through Bering Strait into the Chukchi Sea. This transport occurs because of differential atmospheric pressure across the western Arctic that tilts

sea level down toward the north (Coachman et al., 1975). Not surprisingly,

then, variability in annual transport is related to variability in atmospheric

pressure, as reflected again by the NPI (Fig. 3.43). A long-term decline in

the mean strength of the Aleutian Low may explain the decline in transport

in the past five decades. This, in turn, would be expected to have a large

effect on production budgets across the northern Bering-Chukchi shelf, because nutrients and biota carried in the current transform the region into

one of the most highly productive regions in the world (Springer et al., 1989;

Springer and McRoy, 1993).

Wind measured at St. Paul Island is related to the atmospheric pressure

field (Fig. 3.44). The dramatic drop in the mean winds during the mid1970s to late 1980s corresponded to a similar change in the NPI and to a

pronounced increase in average global ocean temperature (Fig. 3.45).

St. Paul wind is also related to ocean temperature in the Bering Sea

(Fig. 3.46). However, the correlation is only apparent within a given regime

and changes from one regime to the next. That is, wind and SST were

generally out of phase prior to 1977, were generally in phase between 1977

and 1989, and then generally out of phase again after 1989. Interannual

fluctuations in wind and SST are not always proportional, indicating, not

surprisingly, that other factors are important in determining magnitudes of

the two variables.

3.5.4 Physical-biological relationships

Carbon stable isotope ratios (∂13C) of baleen from bowhead whales exhibit considerable interannual variation since the late 1940s, including a
The Southeast Bering Sea Ecosystem—Part 3 115

5

10

15

20

25

30

35

15

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17

1950 1960 1970 1980 1990 2000

Smoothed values

5

10

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25

30

35

14

15

16

17

18

1950 1960 1970 1980 1990 2000

Raw values

Ice

NPI

NPI NPI

Sea Ice (% coverage) Sea Ice (% coverage)

Figure 3.42: Summer extent of sea ice scaled as percent coverage of the Chukchi-Bering shelf (see Niebauer,

1998) and mean July–September NPI.

0.5

0.6

0.7

0.8

0.9

1.0

11

12

13

14

1940 1950 1960 1970 1980 1990 2000

Smoothed values

0.5

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1.0

1.1

10

11

12

13

14

15

1940 1950 1960 1970 1980 1990 2000

Raw values Transport

NPI

NPI NPI

Transport (106 m3 s–1) Transport (106 m3 s–1)

Figure 3.43: Annual northward transport through Bering Strait and the mean annual NPI.
116 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

400

500

600

700

800

900

1000

9

10

11

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13

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1960 1970 1980 1990 2000

R aw values

W ind

NPI

500

600

700

800

900

10

11

12

13

14

15

1960 1970 1980 1990 2000

S moothed values

NPI NPI

Wind (m s )3 –1 Wind (m s )3 –1

Figure 3.44: Mean annual wind at St. Paul and the NPI.

500

600

700

800

900

–0.1

0

0.1

0.2

0.3

0.4

1960 1970 1980 1990 2000

W ind

Temperature

Wind (m s )3 –1

Temperature anomaly (°C)

Figure 3.45: Mean annual wind at St. Paul and the global ocean temperature

anomaly. Wind data smoothed.

pronounced overall shift that has been proposed as evidence of a decline in

primary productivity of some 30% in the Bering Sea (Schell, 2000). The

pattern in baleen ∂13C variability matches the pattern in winds over the

same interval when baleen is lagged by 1 year to account for the ecological

separation between whales and physical forcing (Fig. 3.47).

Wind also is correlated in a general way with sea lion abundance at

several colonies in the Bering Sea range of the endangered western stock

(Fig. 3.48). That is, sea lion abundance declined abruptly and precipitously

at the same time that winds declined in a similar manner in the Bering Sea.
The Southeast Bering Sea Ecosystem—Part 3 117

4.5

5.0

5.5

6.0

6.5

7.0

7.5

400

500

600

700

800

900

1000

1950 1960 1970 1980 1990 2000

Raw values

SST

W ind

Wind (m s )3 –1

SST (°C)

4.5

5.0

5.5

6.0

6.5

7.0

7.5

SST (°C)

400

500

600

700

800

900

1000

Wind (m s ) 3 –1

1950 1960 1970 1980 1990 2000

Smoothed values

Figure 3.46: Mean annual wind at St. Paul and sea surface temperature (SST) in

the eastern Bering Sea. Vertical lines show regime shifts in 1977, 1989, and 1998.

Sea surface temperature (SST) has varied since the late 1940s and is

highly correlated with at least four biological variables in the Bering Sea:

the abundance of age-1 pollock, the biomass of age-3+ pollock, and the

productivity of black-legged and red-legged kittiwakes on the Pribilof Islands. However, except for age-3+ pollock biomass, the correlations exist

only within regimes as defined by the PDO, where they are very strong but

have alternating signs from regime to regime (Figs. 3.49, 3.50). Pollock biomass was positively correlated with spring SST for 25 years when SST is

lagged by four years to account for separation between given SST years and

the integrated effect expressed in the pollock age classes where most biomass

resides (Fig. 3.51). Does the 4-year lag also suggest that most of the impact of SST (or for whatever it is surrogate) on pollock populations occurs

between egg and age-0 fish? If so, one could infer many possible pathways

from the correlation.

3.5.5 Biological-biological relationships

The most conspicuous correlations between biological variables in the Bering

Sea are trends in abundances of piscivorous seabirds and fur seals on St.

Paul Island and St. George Island (Pribilof Islands) and Bogoslof Island

and Buldir Island in the Aleutian Islands (Table 3.3). Trends are uniformly

negative on St. Paul and uniformly positive on Bogoslof and Buldir. On

St. George, both species of kittiwake and fur seals are declining as at St.
118 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

–19.5

–19

–18.5

–18

–17.5

–17

–16.5

500

600

700

800

900

1950 1960 1970 1980 1990 2000

S moothed values

–19.5

–19

–18.5

–18

–17.5

–17

–16.5

400

500

600

700

800

900

1000

1950 1960 1970 1980 1990 2000

R aw values

Baleen

W ind

Wind (m s )3 –1 Wind (m s )3 –1

Baleen (13 C) Baleen (13 C)

Figure 3.47: Bowhead whale baleen ∂13C (lagged 1 year) and mean annual wind at St. Paul.

400

500

600

700

800

900

1000

1960 1970 1980 1990 2000

W ind

0

1000

2000

3000

4000

0

1000

2000

3000

4000

5000

6000

1960 1970 1980 1990 2000

S ea lions

Akun I.

B ogoslof I.

Amak I./Sea lion R ocks

Number (Amak & Bogoslof)

Number (Akun)

Wind (m s )3 –1

Figure 3.48: Sea lion abundance at rookeries in the eastern Aleutian Islands and wind at St. Paul Island.

Heavy line in wind panel is the smoothed trend.
The Southeast Bering Sea Ecosystem—Part 3 119

Table 3.3: Trends in populations of seabirds and fur seals at four locations in the Bering Sea.

Location Species Interval n % Change r2

St. Paul I.

(Pribilofs) Black-legged Kittiwake 1976–1999 12 –73 0.79

Red-legged Kittiwake 1976–1999 12 –70 0.72

Common Murre 1976–1999 12 –62 0.62

Thick-billed Murre 1976–1999 12 –44 0.72

Northern Fur Seal 1975–2000 24 –37 0.6

Mean (SE) -57 (7.1)

St. George I.

(Pribilofs) Black-legged Kittiwake 1976–1999 11 –47 0.45

Red-legged Kittiwake 1976–1999 11 –52 0.63

Common Murre 1976–1999 11 +55 0.56

Thick-billed Murre 1976–1999 11 0 —

Northern Fur Seal 1975–2000 24 –70 0.84

Mean (SE) –23 (23)

Bogoslof I.

(E Aleutians) Black-legged Kittiwake 1973–2000 3 +150 0.8

Red-legged Kittiwake 1973–2000 3 +550 0.95

Tufted Puffin 1973–2000 5 +130 0.98

Northern Fur Seal 1980–1997 12 +2500 0.98

Mean (SE) +832 (564)

Buldir I.

(W Aleutians) Black-legged Kittiwake 1974–1996 10 +450 0.84

Red-legged Kittiwake 1974–1996 10 +200 0.81

Thick-billed Murre 1974–1996 10 +320 0.91

Mean (SE) +323 (72)

Paul. Thick-billed murres declined through the 1980s but have increased

since then, whereas common murres increased throughout this period. All

of these species are supported by a common suite of pelagic forage fishes and

squids that includes prominently juvenile pollock at the Pribilof Islands.

There is an obvious similarity between the pattern of change in fur

seal numbers at St. Paul—a prolonged decline punctuated by intervals of

increase—and the pattern of change in ∂13C of bowhead whale baleen

(Fig. 3.52). Were it not for the overriding effect of the female harvest on

population stability in the period 1956–1968 (York and Hartley, 1981), the

response of fur seals to an apparent environmental signal might have been

stronger. That is, the increasing trend in numbers in the mid-1960s may

have been attenuated by pressure on the population from excessive female

harvests. Otherwise, fur seal pup production and bowhead baleen seem to

be recording events that originate with fluctuations in climate and propagate

through the system.

Additional evidence of the sensitivity of fur seals to ecosystem state is

found in patterns of growth in body size over time. In the past century, fur

seals on St. Paul experienced periods of faster and slower growth (A. Trites,

unpublished data), with each period lasting about as long as a climate regime
120 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

0.0

20.0

40.0

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6

7

1960 1970 1980 1990 2000

Pollock

SST

1977 1989 1998

0

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70

3456

r2

 = 0.17

0

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3456

r2

 = 0.92

1978

0

10

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50

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70

3456

r

2

 = 0.71

Temperature (°C)

Abundance (x 1,000,000)

Abundance (x 1,000,000)

Temperature (°C)

1964–1976 1978–1988 1990–1997

(–1978)

Figure 3.49: Abundance of age-1+ pollock and mean April–June sea surface temperature (SST: lagged 1 year) on the eastern Bering Sea shelf. The transition years

(1977, 1989, and 1998) are not included in the regression, nor is 1978, as it was an

extreme in recruitment.

(Fig. 3.53). Faster growth occurred in regimes when common murres apparently predominated at Walrus Island, a small island in the Pribilof group

where murres once nested in immense numbers: murres were eliminated by

foxes that gained access to the island over winter ice in the early 1970s.

Conversely, slow growth occurred in regimes when thick-billed murres apparently predominated. The well-documented alternation between murre

species on Walrus Island (Peterson and Fisher, 1955) also corresponded to

shifts in climate regime (Springer, 1998).

Fur seals are ecologically more similar to common murres than to thickbilled murres. That is, fur seals and common murres are more dependent on

pelagic prey, whereas thick-billed murres incorporate a significant amount of

benthic prey in their diets (Springer, 1991; Sinclair et al., 1994; Springer et

al., 1996). Therefore, the correspondence of positive responses of fur seals

and common murres during periods when thick-billed murres apparently

responded negatively is theoretically tractable.

3.5.6 Discussion

Functional relationships should typically have just one sign—positive or negative. Among the examples presented here, it is reasonable to believe that

the common pattern of variability in meteorological conditions, as indexed by

the NPI, and regional wind (observed at St. Paul) represents a positive functional forcing-response relationship over the Bering Sea. It further can be
The Southeast Bering Sea Ecosystem—Part 3 121

0.0

0.1

0.2

0.3

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0.5

5.0

5.5

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7.0

1975 1980 1985 1990 1995 2000

SST

RLKI

0

0.1

0.2

0.3

0.4

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5.0 5.5 6.0 6.5

r 2 = 0.80

1976–1988

5.0 5.5 6.0 6.5

r 2 = 0.88

1989–1996

5 5.5 6 6.5

 r = 0.99

1997–2000

SST (°C)

Temperature (°C)

Productivity (chicks/nest)

Productivity (chicks nest ) –1

Figure 3.50: Red-legged kittiwake productivity at St. George Island and mean annual sea surface temperature (SST) in the eastern Bering Sea. Smoothed trends. A similar relationship exists for black-legged

kittiwakes.

0

2

4

6

8

10

12

3.0

4.0

5.0

6.0

1960 1970 1980 1990 2000

P ollock

SST

SST (°C)

Biomass (x 1,000,000 t)

Figure 3.51: Biomass of age 3+ pollock in the eastern Bering Sea and mean April–June sea surface

temperature (SST: lagged 4 years). Smoothed trends.
122 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

–19.5

–19

–18.5

–18

–17.5

–17

–16.5

100000

200000

300000

400000

500000

1950 1960 1970 1980 1990 2000

B aleen

Fur seal

Number of pups

Baleen (13 C)

Figure 3.52: Fur seal pup production on St. Paul Island and isotope ratios in

bowhead whale baleen. Smoothed trends. Dotted vertical lines at 1956 and 1968

mark interval of experimental female harvest.

–6

–4

–2

0

2

4

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Thick-billed Thick-billed

Common Common

Few murres

Fast Slow Slow Fast

Increasing Decreasing

Fur Seal Growth

NPI Anomaly

Figure 3.53: Fur seal growth at St. Paul Island and the relative abundance of common and thick-billed murres on Walrus Island. Dotted vertical lines mark regime

shifts in 1925, 1947, and 1977 (Mantua et al., 1997). Adapted from Springer (1998).

Heavy line is smoothed trend.

argued that basin-scale meteorology and thus regional winds are influenced

by changes in mean characteristics of the global-scale ocean-atmosphere interactions. Variability in the Aleutian Low and elements of physical oceanography, such as transport through Bering Strait and the summer retreat of sea

ice in the Chukchi Sea, must represent positive functional forcing-response

relationships across the atmosphere-ocean interface.

In contrast to these single sign relationships, the sign of the relationship

between wind and SST in the Bering Sea has alternated between generally

negative and generally positive depending upon regime. A negative relation-
The Southeast Bering Sea Ecosystem—Part 3 123

ship might always be expected, since during much of the year wind mixes

the upper layers of the ocean, countering effects of stratification and surface heating from the sun. Thus, it appears that while wind and SST in

the Bering Sea may be adjacent ecosystem elements under certain conditions, under other conditions they may be separated by one or more other

elements. Alternatively, it may be that under certain conditions, another element overrides the likely role of wind on SST, or perhaps wind and SST are

responding independently to some other feature(s) of the physical system.

Similarly, it appears that the relationship between summer atmospheric

forcing and summer retreat of sea ice was generally in phase during the 1950s

through 1980s, but has been out of phase since. This may also represent the

influence of another element dominating sea ice extent, e.g., an interaction

with the Arctic Oscillation. Even during the long interval of positive correlation, the pattern of the NPI did not always exactly match that of summer

sea ice extent, further implicating other factors in the full equation.

Biological variability should reflect physical variability. In the Bering

Sea, the 50-y time series of ∂13C in bowhead whale baleen, an ostensible

proxy of primary productivity, is well correlated with the NPI and wind.

At least two processes could explain these relationships, both of which begin

with meteorology. One is that baleen ∂13C is mediated by wind, which plays

major roles in primary and secondary production by creating turbulence that

affects mixed layer and nutrient dynamics and feeding efficiencies of planktonic organisms and micronekton. Wind is clearly important to primary

production in the Bering Sea (Sambrotto and Goering, 1983; Sambrotto et

al., 1986). Another possibility is the effect of atmospheric pressure on transport across the northern Bering-Chukchi shelf, where bowheads obtain the

bulk of their annual nutrition (Lee, 2000). Variability in northward transport leads to variability in primary production and the rate of advective

supply of zooplankton (Springer et al., 1989; Springer and McRoy, 1993)

that might be reflected in the ∂13C of bowhead baleen.

Members of two contrasting food webs—deep basin pelagic (red-legged

kittiwakes) and continental shelf pelagic (black-legged kittiwakes and juvenile pollock)—are extremely influenced by whatever it is that SST indexes.

The most intriguing aspect of this is that the sign of the relationship between

SST and juvenile pollock and kittiwakes changes between regimes, just as it

does with SST and wind. These correlations probably should not be considered functional relationships in the strict sense, since there are potentially

several ecological levels that are a bridge between them. It is more likely

that SST serves as a proxy for one or more other features of the ecosystem

(that change in fundamental ways between regimes relative to SST) that are

responsible for the variability in abundance and productivity of the birds

and pollock. Still, in a predictive sense, SST is a powerful index provided

one knows when a regime shifts.

SST was strongly predictive of age-3+ pollock biomass on the eastern

shelf for over two decades, and the sign was always positive regardless of

regime, i.e., it did not change after 1989. The significance of the apparently

negative relationship in the 1960s is unclear because of the uncertain effect

of the rapidly developing commercial fishery on the relatively small virgin
124 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

stock. However, the relationship may have again become negative since

the mid 1990s. The reason for differences between trends in abundance of

juvenile pollock and biomass of adult pollock in relation to trends in SST

and regime are not readily apparent.

Just as signs of several relationships change over time as regimes shift,

signs of trends in the abundance of seabirds and fur seals vary between

habitats within regimes (Pribilofs and continental shelf regions vs. Bogoslof

and oceanic regions), and ratios of congeneric murres on Walrus Island and

growth rates of fur seals vary within habitat between regimes. In the case

of fur seal growth rates, the effect is apparently manifested in the Gulf of

Alaska as the seals return to the Bering Sea in spring (Trites and Bigg, 1996).

The geographic distance between locations where fur seal annual growth is

determined and where various parameters are measured in the Bering and

Chukchi seas illustrates the spatial scale of influence of an overriding physical

forcing factor, which is most likely the Aleutian Low pressure system.

In aggregate, there are numerous correlations among a variety of ecosystem components from fundamental meteorological forcing (the Aleutian Low

pressure system) to primary physical response factors (winds, currents, and

SST), to secondary physical and biological response parameters (sea ice extent, surrogate primary productivity, and the abundances and productivity

of planktivorous fish and piscivorous marine birds and mammals). None

of the physical-physical or physical-biological correlations proves cause and

effect, but a considerable weight of evidence indicates that climate and climate change are extremely important in regulating production regimes in

the Bering Sea. Routes through the ecosystem taken by these supposed

chains of cause and effect are conjectural still, but several plausible scenarios come to mind. For example, fluctuating atmospheric pressure affects

winds and currents that alter sea ice extent and primary productivity (as

indexed by isotope ratios in bowhead baleen). Variable food web productivity, arising from the effect of wind on primary productivity or on transfer

efficiencies at low trophic levels, would be important at high trophic levels

and could be expressed in fur seal growth, sea lion abundance, and alternating dominance of one or the other species of murre at Walrus Island.

The biological-biological correlations presented here demonstrate common

responses to signals propagating through the ecosystem, rather than functional cause and effect relationships.

The emphasis of most recent analyses of effects of climate change has

been on comparisons of mean parameter values or slopes, e.g., relatively

higher or lower atmospheric pressure or increasing or decreasing abundance

of a species, between regimes. That is, a given parameter in a given regime

is reduced to a single value or sign (positive or negative). The data sets

presented here show, in addition, a considerable amount of interannual coherence within and across regimes, supporting the notion that several may

involve functional forcing-response relationships. Still, none of the supposed

independent forcing variables, e.g., the summer atmospheric pressure field,

explains all of the variability in the dependent response variables, e.g., summer sea ice retreat. In all cases, multiple forcing factors are at play.

Many things can affect any parameter’s value within a given year, such
The Southeast Bering Sea Ecosystem—Part 3 125

as measurement error and random events in the ecosystem. These can be

important, for example as when a severe summer storm with high wind

and heavy rain blows and washes away seabird chicks from nests, radically

lowering productivity from a level that otherwise could have been supported

by the system. Such random events are commonly unrelated to fundamental

trends in forcing factors and can blur the picture of fundamental form. Thus,

for example, the fundamental waveform of the longer-term oscillation of

summer NPI and sea ice, irrespective of regimes, can be more clearly seen

when the data are smoothed as in Fig. 3.42.

The relationships presented here implicate bottom-up control of various

processes linking the Aleutian Low to seabirds and marine mammals. However, this should not be construed to mean that all demographic changes at

high trophic levels represent responses to changing food web production and

the availability of prey to responding species. In the case of fur seals, for

example, variable growth rates likely do reflect changing prey abundance:

declining pup production could be caused by declining carrying capacity

affecting the ability of females to produce pups, or by the loss of females

brought on by starvation (a prey issue) or by predation. That species are

sensitive to fluctuations in productivity of supporting food webs is not surprising, nor is it surprising that they are sensitive to changes in predation

that might result from, or be exacerbated by, the same bottom-up pathways.

The tendency for the Bering Sea ecosystem to behave in particular ways

depending upon seasonal, annual, and mean decadal physical forcing also is

not surprising and is evident in numerous time series. What is less intuitive

are explanations of the changing signs of relationships between elements of

the ecosystem from one climate regime to another, such as between wind

and SST and between SST and the productivity of pollock and kittiwakes.

In all of these examples, other ecosystem elements must be involved in the

actual functional relationships. Nevertheless, it is puzzling how the signs of

these relationships change between positive and negative as climate regimes

change. Whatever the explanation may prove to be, in the meantime it is

important to be mindful of the potential for inconstant polarity, relative not

only to time but to other ecosystem attributes. It is especially important

when attempting to develop ways to predict an outcome, such as pollock production, based on one or a combination of indices. Ecosystems apparently

do have memory, or inertia, but the path taken by ecosystem processes is

steered in different directions under different climate situations, thus changing outcomes in ways that are surprising and unpredictable.

3.6 Summary

3.6.1 Progress accomplished in the development of indices

The goal of the IWG was to identify potential single- or multi-parameter

constructs or indices that lead to development of survival indices for pollock

in the early life history stages, and that this information would provide input to the National Marine Fisheries Service (NMFS) in stock assessment

of juvenile pollock for use by fisheries scientists at the Alaska Fisheries Sci-
126 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

ence Center (AFSC)/NMFS. Toward attaining this goal, extensive progress

was made in the development of indices (Table 3.1). The conceptual switch

model (Fig. 3.2) developed during SEBSCC Phase I was expanded to include timing of prey production (that, in turn, is related to the presence of

sea ice) for first feeding survival, thereby becoming a more complete mirror of the natural ecosystem. The development of an index of mixed-layer

characteristics (see Section 3.3.1) provided input to the development of the

Oscillating Control Hypothesis (Hunt et al., 2002a). This index of the physical environment, however, does not account for the observed variability in

age-1 pollock year-class strength. An index of wind turbulence versus larval feeding success was developed (see Section 3.3.2). The project ended,

however, prior to developing a time history of this index to compare with

recruitment estimates. For the first time in Bering Sea studies, variations in

net short wave radiation were considered as an index (Section 3.3.3). The

importance of such variations is only now being recognized world wide as

a crucial aspect of time varying forcing for climate and ecosystem change

(e.g., Foukal, 2003). A new index of sea ice was developed that led to new

insight regarding changes in timing of spring in the region (Stabeno and

Overland, 2001). The compilation of water temperature data from the annual trawl survey (Section 3.3.5) now permits further development of indices

related to thermal conditions. The model simulations (Section 3.3.6) show

that surface wind drift is not sufficient for simulating pollock egg and larval

drift. The use of NEPROMS to generate annual drift trajectories requires

further funding. A technique was developed that combines early life history

transport and predation by adult pollock (Section 3.4). This technique has

been transferred to NMFS and is an operational element of the annual stock

assessment. Further application of this technique awaits the use of more realistic transport simulations. An examination of a large set of biological and

physical indices showed that potential relationships could change sign with

regime shifts (Section 3.5), providing a clear warning that a simple linear

solution does not exist. The IWG concluded its deliberations by generating

a set of indices-related topics that need to be examined (Section 3.6.2), and

tools that need to be developed (Section 3.6.3).

The vast quantity of information collected during SEBSCC has led to new

understanding and hypotheses of how the southeastern Bering Sea ecosystem

functions. This information is providing input to the National Marine Fisheries Service’s stock assessment of juvenile pollock. At present, the pathway

to providing input is through a “grass-roots” approach; the integrative research method employed during SEBSCC included fisheries scientists at the

Alaska Fisheries Science Center whose tasks are directed toward status of

stocks. Some of these scientists were SEBSCC Principal Investigators and/or

members of the Indices and Pribilof Island Working Groups. They were the

ones who helped to develop both indices of potential survival of early life

histories of pollock and a formal technique to use such indices in stock assessment models. The quantitative use of annual metrics of the physical

and biological environment is the next logical step in the progression toward

improved forecasts of age-1 recruitment. As the management of fisheries

matures toward an ecosystem-based approach, the integrated biophysical
The Southeast Bering Sea Ecosystem—Part 3 127

knowledge attained by fisheries scientists during SEBSCC will prove to be

an invaluable foundation.

3.6.2 Further development of indices

Based on the results from four workshops, the members of the IWG believe

that the following are logical next steps in the development and refinement

of indices. These suggestions have been grouped into two categories: indices

that are related to changes in strength of a given year class of pollock and

an index that may provide guidance regarding interpretation of the annual

stock assessment from trawl results. While this later category was not in the

original charge to the IWG or a focus of SEBSCC, it is a vital component

of fishery management.

Indices for Estimating Recruitment of Age-1 Pollock

• Continue comparisons with other model simulations and observations

to provide verification of the Bering Sea model (NEPROMS).

• Develop a wind index of nutrient supply to the shelf from Bering

Canyon.

• Develop a user interface and/or other techniques so the NEPROMS is

more accessible as a tool.

• Examine various indices versus occurrence of northwest and southeast

centers of the adult pollock population.

• Extract an index of zooplankton abundance from ECOSYM.

• Develop a history of the time/space occurrence of the roe fishery to help

define time/space limits for pollock eggs as initial points for trajectory

simulations.

• Develop a similar product for spawning that occurs near Bogoslof Island.

• Use temperature observations from annual trawl surveys to develop an

index of the presence of the cold pool and the locations of the inner

and middle fronts (i.e., the boundaries of the middle shelf domain) in

given years.

• Use temperature observations to create annual distributions of temperature that, in turn, can be used to simulate zooplankton production.

An Index for Interpretation of the Annual Stock Assessment

• Examine the influence of changes in the physical environment (e.g.,

water temperature, location of fronts) on preferred habitat for pollock,

and how variations in these features affect estimates from trawl results.
128 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

3.6.3 Concluding remarks

Simulations from the NEPROMS are vital to further development of indices

for the Bering Sea ecosystem. Continued verification of simulations (both

velocity and water property fields) from this model and improvements to its

physics (i.e., inclusion of northward flow through Bering Strait) has the highest priority. The development of a wind-related index of potential nutrient

transport shoreward along the Alaskan Peninsula from Bering Canyon can be

accomplished independently of the NEPROMS. Comparisons between OSCURS and NEPROMS (Section 3.3.6.2) demonstrate that dynamics other

than direct wind forcing are critical throughout most of the water column.

Thus, model simulations of the integrated flux through appropriate cross

sections would provide a more complete measure of nutrient flux. Model

simulations can also be used to establish temperature distributions for a

time perhaps more appropriate for making estimates of zooplankton production (May–June) than the July–August data from the trawl survey.

Given the overall importance of NEPROMS simulations to help us understand physical mechanisms and their impact on biota, the utility of this

model must be improved. In addition, as techniques and understanding of

dynamics improve, this model should be updated to include as much physics

as is possible. How can the NEPROMS become a user-friendlier tool? This

may take the form of having made and stored average (e.g., daily) water

property and velocity field distributions. If these simulations were then coupled with an interactive interface that would allow a naive user to choose

particular simulations and then access them in selected time and space domains, then the NEPROMS would permit far more research into indices and

questions of how the ecosystem functions than is possible today.
The Southeast Bering Sea Ecosystem—Part 4 129

Part 4—Pribilof Islands: A Microcosm for the

Southeast Bering Sea

Results from the Pribilofs Working Group on the Ecosystem of

the Pribilof Islands with Respect to Habitat for, and Survival

of, Walleye Pollock

Principal Author: Gordie Swartzman

With participation and contributions from:

Ric Brodeur, Troy Buckley, Lorenzo Ciannelli, Ken Coyle, George Hunt, Jim

Ianelli, Allen Macklin, Jeff Napp, Rolf Ream, Bruce Robson, Sigrid Salo, Beth

Sinclair, Phyllis Stabeno, Gordie Swartzman, Lucy Vlietstra, and Andreas Winter

4.1 Overview of the Pribilof Island Ecosystem

The Pribilof Island ecosystem has been a major focus for the Southeast

Bering Sea Carrying Capacity Program (SEBSCC) and the Bering Sea

Fisheries-Oceanography Coordinated Investigations (BS FOCI) projects

sponsored by NOAA Coastal Ocean Program. The region was chosen, in

part, because it is thought to be a major nursery area for age-0 walleye pollock (Theragra chalcogramma), the target species for these projects. Early

hydroacoustic studies suggested higher than background abundance of juvenile pollock (Traynor and Smith, 1996). Satellite-tracked drifters released in

a pollock spawning area (Unimak Island) slowly drifted northwest toward the

Pribilof Islands, arriving in late summer and fall (Stabeno et al., 1999b), suggesting that young-of-the-year pollock spawned in the southeastern Bering

Sea might be transported preferentially to the Pribilofs.

The oceanographic region around the Pribilofs contains, in miniature,

all of the domains (coastal, inner front, middle shelf, middle transition zone,

outer shelf, outer front, slope, and basin; Section 2.1.3) present in the greater

southeastern Bering Sea. For this reason, scientific focus on the Pribilofs

should yield understanding of dynamics and processes affecting the greater

region. The Pribilofs, then, are a microcosm and natural laboratory for

investigation of the southeastern Bering Sea ecosystem. Under the auspices

of BS FOCI and SEBSCC, the area has undergone extensive survey during

late summer for 6 years, from 1994 through 1999. Acoustic surveys, with

concomitant physical and biological sampling, occurred along four transects

(Fig. 4.1). These transects were chosen to be representative of different

habitats around the Pribilof Islands.

This report section is a synthesis of our best understanding at present of

the relationship between juvenile walleye pollock of the Pribilofs and juvenile pollock in the remainder of the southeastern Bering Sea. Such an index

will prove valuable in helping to forecast future year classes in the important

pollock fishery. Work leading to this report was discussed at a series of three

workshops by a working group consisting of the editor, participants, and contributors to this report listed on the preceding page. The first workshop was

held at the Pacific Marine Environmental Laboratory (PMEL) in Seattle,
130 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Transect A

Transect B

Transect C

Transect D

200 ft

100 ft

St. Paul

St. George

Bering Sea 57°N

56°

171° 170° 169° 168°W

Alaska Peninsula

Pribilof Islands

Figure 4.1: Pribilof Island study region showing transects A-D repeatedly sampled

both day and night during September 1994–1999.

Washington, 20–21 September 2001, the second in Victoria, BC, Canada, on

12 October 2001, and the third at PMEL on 22–23 April 2002.

4.2 Working Group Products

The working group focused on a synthesis of available information concerning

the Pribilof Island ecosystem. To this end, we developed a web site containing a table of links to available data sets and to relevant supporting and

summarizing literature. The web site link is http://www.pmel.noaa.gov/

sebscc/PWG data matrix.html.

Issues addressed by the working group and used to structure this section

of the report are:

1. How large an area is the Pribilof region? How much area around

the islands is necessary to support local populations of seabirds and

mammals?

2. How much transport is there to this area from major pollock spawning

regions?

3. How does the abundance of juvenile pollock near the Pribilof Islands

compare with abundance along the Bering Sea shelf?

4. Is there a relationship between the abundance of age-0 pollock near

the Pribilof Islands and the diet breadth, feeding energetics, survival,

or recruitment of apex predators (seabirds, fish, and mammals)?
The Southeast Bering Sea Ecosystem—Part 4 131

5. How important is predation to pollock abundance near the Pribilof

Islands?

4.2.1 How large an area is the Pribilof region?

The Pribilof Island region is oceanographically complex, with several habitats in a small area. Large numbers of species inhabit the region, at least

for part of the year. To define a region of influence of the Pribilof Islands,

we used a biological assessment that relates the species composition at different distances from the islands with the energetic balances of those biota

in circular areas, centered on the islands, defined by radii equal to the different distances. The working region is then the one with the greatest selfcontainment; i.e., having the largest percentage of the energetics demand

satisfied within the region of interest. An energy balance model, ECOPATH

(Polovina, 1984; Ciannelli, 2002), was used to compute the percentage of

annual energetic demand for each species or functional group in the model

satisfied by feeding within the region around the Pribilofs. ECOPATH is

a linear, non-dynamic compartment model framework in which flow rates

between the compartments are obtained as parameter values by searching

the literature and available data for diet compositions for the groups in each

compartment. Biomass estimates for each compartment are made by multiplying population estimates by average weight. There are 41 compartments

in the Pribilof Island region model, including phytoplankton, small and large

jellyfish, fur seals, crabs, small and large flatfish, common and thick-billed

murres, juvenile gadids (over 95% of which are age-0 pollock) and macrozooplankton. The model produces a steady-state computation of how much

of each prey item is eaten by each predator. The ratio of output (amount

removed by predation or fishing) to input (amount eaten or synthesized),

the ecological efficiency, is computed for each compartment. An ecological

efficiency greater than 1.0 implies that some source of input outside the defined area must be available to maintain energetic balance. The model was

run for circular regions around the Pribilof Islands with radii of 50, 100,

and 150 nautical miles (nm; 1 nm = 1.85 km). The resulting computations

suggested that at a radius of 100 nm, the ecological efficiency is closest to 1.0

for the most groups, implying that this region is the most energetically self

contained. While we recognize that there is significant migration into and

out of the Pribilof region, we decided to define the region as that area within

100 nm of the midpoint between the Pribilof Islands. We use the 100-nm

boundary loosely, since a circle of radius this size around the Pribilof Islands

(Fig. 4.1) encloses basin, shelf, and frontal habitats, and the basin habit is

quite different from the shelf and frontal habitats. Thus, the Pribilof habitat

should not extend as far into the basin region. A 100-nm radius region is

supported by oceanographic and phytoplankton production considerations

(Flint et al., 2002), which suggest that the outer front regions around the

Pribilofs occur at about this distance. Also, this is close to the dimensions of

the expanded, slow circulation that frequently appears around the Pribilof

Islands during the summer (Stabeno et al., 1999b). As such, we will use this

region in considerations concerning biomass, production, and distribution
132 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

of biota and in comparing these quantities to other areas (i.e., how relatively productive is the Pribilof Island region compared to other areas of

the eastern Bering Sea, and how similar is the standing stock of dominant

species?).

4.2.2 How much transport is there to this area from major pollock spawning regions?

The Pribilof Islands are not permanent habitat for young-of-the-year pollock.

Larvae likely are transported to the region from pollock spawning grounds

to the south. These pollock are transients, and probably benefit from higher

food density, local water circulation patterns that concentrate food, and

lower predation. Later in their juvenile stage, they migrate to the northwest.

An indication of how many pollock larvae ended up near the Pribilof

Islands is available for most years from 1994–2000 based on drifters released

near the two major known pollock spawning areas in the eastern Bering

Sea—Bogoslof Island and the Unimak Pass area (Napp et al., 2000). Plots

of the paths of these drifters are available online at http://www.pmel.noaa.

gov/foci/visualizations/drifter. There is also a third large spawning

area near the mouth of the Pribilof Canyon, smaller than the other two

(Napp et al., 2000). Most drifters were drogued at 45-m depth during April

or early May. This depth appears to be somewhat deeper than the average depth of pollock larvae (Napp et al., 2000 report an April 1992 study

showing highest aggregations of pollock larvae at a depth of 30 m with

few larvae below 50 m). A significant fraction of these eventually drifted

near the Pribilofs and some were entrained in currents around the islands.

Stabeno (NOAA/PMEL, personal communication) estimated, for the years

1994–2000, the relative fraction of drifters that passed the Pribilof Islands.

The classes were low (less than 20%), medium (20–40%) and high (>40%).

All the years were classified as medium, except 1995 and 2000 (high) and

1998 (no data). In all cases, drifters released in April or May did not arrive near the Pribilofs until late August or September, at the earliest. Since

the Unimak Pass spawning generally occurs in April (Napp et al., 2000),

it is unlikely that the age-0 pollock observed in the Pribilof region in June

and July (see above) came from Unimak Pass. Bogoslof Island spawning is

generally earlier (March), and thus, it is feasible, if drifter data are representative of larval drift, that larvae could be advected toward the Pribilofs

and arrive there in June or July. However, this and other evidence on the

distribution of adult pollock to be presented later in this section (Fig. 4.6

and Section 4.2.5), suggest that the primary source for pollock observed in

June and July may be the Pribilof Canyon and other smaller spawning areas

closer to the Pribilof Islands. There may also be some genetic differences between pollock found northwest and southeast of the Pribilof Islands (Kevin

Bailey, NOAA/AFSC, personal communication), which may make possible

identification of the age-0 pollock spawning location.
The Southeast Bering Sea Ecosystem—Part 4 133

4.2.3 How does the abundance of juvenile pollock near the Pribilof

Islands compare with abundance along the Bering Sea Shelf ?

The region around the Pribilof Islands has been suggested as a region of unusually high production in the eastern Bering Sea, and it has been termed a

green belt (Springer et al., 1996a). This hypothesis is supported by evidence

from Sambrotto et al. (1986) and Flint et al. (2002). These studies showed

higher primary and secondary production in the Pribilof Island frontal regions. Flint et al. (2002) argue that current and mixing processes around

fronts bring higher nutrient concentrations to the euphotic zone, enhancing

production.

This section relates the importance of the Pribilof Island stock to the

total pollock abundance, and to how typical year-to-year changes in age-0

pollock abundance in the Pribilof region compare to changes in other areas

along the Bering Sea shelf. To establish a baseline for comparison with the

abundance of pollock in the Pribilof Island region, we found data sets that

either spanned the Pribilof Islands and neighboring areas or used similar

sampling methods to assess pollock abundance in other shelf areas of the

Bering Sea. The relative abundance of age-0 pollock in the Pribilof Island

area to other shelf and front areas throughout the eastern Bering Sea was

considered using:

1. Net samples for age-0 pollock taken at selected stations both near

the Pribilof Islands and beyond during July aboard the Japanese T/S

Oshoro Maru (Jeff Napp);

2. Net trawls for age-0 pollock taken aboard the R/VMiller Freeman during MACE acoustic surveys (Jim Ianelli and Neil Williamson);

3. Comparison of acoustic abundance near the Pribilof Islands with abundance on transects in the Inner Front, the Bering Sea shelf, and shelfbreak region running along the Alaska continental shelf (Andreas Winter and Ken Coyle).

Net samples from Oshoro Maru. Surveys of the southeastern Bering Sea shelf for juvenile (age-0) walleye pollock have been conducted in collaboration with scientists from the Faculty of Fisheries, Hokkaido University,

Japan every summer since 1995 (1995–2001), using the Fisheries Training

Vessel Oshoro Maru. The survey grid is fixed (approximately 30 nm between

stations), although not all stations are sampled every year. The time of the

cruise has not varied from late July. Double oblique tows of a rectangular,

midwater, framed trawl (5-m2 mount opening, 3 × 2-mm mesh in the body

of the net, and 1-mm mesh in the cod end) were used each year. A flow meter mounted in the mouth of the net recorded distance traveled. A “rough

count” of age-0 pollock was taken immediately after the tow using either

a sub-sample or the entire catch. The catch was then preserved and sent

to Poland for processing at the Polish Plankton Sorting and Identification

Center. Rough counts were standardized to 100-m2 area surveyed.

To test the hypothesis that catches of juvenile pollock were higher around

the Pribilof Islands than over other parts of the shelf, the survey area was
134 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

divided into two strata (Fig. 4.2). The size of the Pribilof stratum took into

account recent research (Ciannelli, 2002) that concluded that the influence

of the Pribilof Islands extended 100 nm around the islands (excluding the

basin region south of the Pribilof Islands). A fourth-root transformation was

applied to the age-0 pollock rough count data to eliminate heteroscedasticity. We then used an ANOVA to test for differences among years, between

the two strata, and interactions between strata and years. There were no

significant interactions between year and stratum (P = 0.101), but there

were significant differences among years (P = 0.011), and between the two

strata (P  0.001), with the mean value for abundance around the Pribilof

Islands significantly higher than abundance in the other stratum.

The ratio of abundance within the Pribilof region to that outside it,

which can be used as a rough index of the relative abundance, by year for

juvenile pollock in the Pribilof region to that in the surrounding eastern

Bering Sea shelf region, is given in Table 4.1. On average, the ratio of perarea abundance in the Pribilof vs. surrounding area was 2.95.

Net trawl data from the NMFS MACE survey group. Initial

analysis of oblique tow Methot data collected during summer MACE surveys

in 1982, 1984, and 1985 (Traynor and Smith, 1996) provided estimates of age0 pollock abundance in the eastern Bering Sea. Additional Methot samples

taken during summer 1999 were summarized by Honkalehto et al. (2002). All

these results show the Pribilof region to be highly variable in age-0 pollock

in July or late June, with abundance of age-0 pollock in 1984 and 1985

being higher than other regions, while in 1999 they were lower. Additional

data, not yet analyzed, are available from surveys in 1996, 1997, and 2000

using Methot trawls and in earlier years (e.g., 1991, 1994, and others) using

Marinovich and/or Methot trawls.

Traynor and Smith (1996) wrote that the center of abundance (based on

surveys in 1982, 1984, and 1985) was near the Pribilof Islands, but there

were also other areas of high abundance (Figs. 4.3 and 4.4). However, these

distributions may not be consistent from one year to the next. For example, Honkalehto et al. (2002) showed that from limited 1999 sampling, the

abundance of age-0 gadids (presumably primarily pollock) were distributed

in areas generally outside of the Pribilof Islands (Fig. 4.5). Note that this

appears somewhat inconsistent with the 1999 age-0 abundance presented in

Table 4.1. However, the two samples were taken at different times of the

summer, and, considering the likely transport of age-0 pollock from spawning areas near Bogoslof Island and Unimak Pass, high densities along the

shelf may end up by September near the Pribilof Islands. Continued sampling of age-0 pollock in the greater shelf region over the July–September

Table 4.1: Ratio of abundance per 10 m2 for the Pribilof Region to that in the

surrounding area based on Oshoro Maru surveys from 1995–2002.

Year 1995 1996 1997 1998 1999 2000 2001 2002

Abundance ratio 0.808 5.168 1.035 7.975 3.782 1.487 1.281 2.035
The Southeast Bering Sea Ecosystem—Part 4 135

1995

Pribilof

Is.

100 m

200 m

22 fish/10 m2

1998

100 m

200 m

12 fish/10 m2

2001 Pribilof

Is.

100 m

200 m

269 fish/10 m3

1996

Pribilof

Is.

100 m

200 m

47 fish/10 m2

1999

100 m

200 m

52 fish/10 m2

1997

100 m

200 m

14 fish/10 m2

2000

100 m

200 m

21 fish/10 m2

2002

100 m

200 m

21 fish/10 m2

58°N

57°

56°

55°

54°

58°N

57°

56°

55°

54°

58°N

57°

56°

55°

172° 170° 168° 166° 164°W 172° 170° 168° 166° 164°W

Age 0 pollock densities

No./10 m2

Pribilof Islands

Region

0

>0 -25

>25 - 50

>50 - 100

>100

Latitude

Pribilof

Is.

Pribilof

Is. Pribilof

Is. Pribilof Is.

Pribilof

Is.

Longitude

0

>0 - 100

>100 - 1000

>1000

1985-2000 2001-2002

Figure 4.2: Distribution of age-0 pollock from net hauls taken in July 1995–2001 from the T/S Oshoro

Maru.

period would help provide a better indication of interannual spatial variability, as well as within-season temporal variability—apparently essential for

constructing an accurate picture of the factors that contribute to pollock

year-class success in their first year of life. The timing of the samples is

important since there now appears to be some evidence of different spatial

distributions of adult pollock in years leading to strong year-classes (Buckley et al., 2001; Fig. 4.5), which may influence the dominance of different

spawning habitats, which in turn would affect the timing of arrival of different cohorts of age-0 pollock to the Pribilof region. That there is significant

change in the spatial distribution of age-0 pollock over the summer is supported by a comparison of the distribution of age-0 pollock in early July

1999 from the MACE survey (Fig. 4.5) with the same distribution from the

Oshoro Maru in late July 1999 (Fig. 4.2). Clearly, more pollock were close

to the Pribilof Islands later in the summer. The pattern may have been

different, however, in 1984 and 1985, when age-0 pollock abundance was

highest near the Pribilof Islands in early July (Fig. 4.3), and in 1987, when
136 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

0 - 999

1,000 - 9,999

10,000 - 49,999

Pribilof Islands 

Region

1984

Catch rates 

(number per hour)

64°N

62°

60°

58°

54°

0 - 999

1,000 - 9,999

10,000 - 49,999

50,000 - 49,999

> 100,000

Pribilof Islands 

Region

1985

Catch rates 

(number per hour)

64°N

62°

60°

58°

54°

56°

170°E 180° 176° 172° 168° 164° 160° 156°W

St. Paul

St. George

St. Paul

St. George

Figure 4.3: Catch-rate distributions for age-0 walleye pollock in the EBS during 1984 (upper panel) and

1985 (lower panel). Reproduced from Traynor and Smith (1996). A 100-nm radius region is shown around

the Pribilof Islands.

abundance was high near the Pribilof Islands, but higher still near Unimak

Pass (Fig. 4.4). Changes in spatial patterns of age-0 pollock in early summer

could result from changes in the spawning distribution of different cohorts

(Fig. 4.6).

Acoustic survey comparisons between Pribilof and Inner Front

surveys. A comparison of acoustic data between the Pribilof region and

several acoustic transects in the Inner Front region of the eastern Bering Sea

was conducted by Andreas Winter and Ken Coyle (Section 4.3). Direct comparisons of pollock and zooplankton abundance were made for 1997, 1998,
The Southeast Bering Sea Ecosystem—Part 4 137

64

63

62

61

60

59

58

57

56

55

54

53

52

189 186 183 180 177 174 171 168 165 162 159

Russia

Alaska

200 m

Latitude

Longitude

Age-0 Pollock (No./hr)

Bering Sea 1987 100,000

90,000

80,000

70,000

60,000

50,000

40,000

0

10,000

20,000

30,000

Figure 4.4: Distribution for age-0 walleye pollock in the EBS during summer 1987 taken from the Russian

survey aboard the MV Darwin.

+ + +

+ + +

+

+ +

+

+ + +

+ +

+

+ +

+

+

+

+

+

+

+ 200 m

100 m

50 m

Pribilof Is.

St. Matthew I.

Nunivak I.

Alaska Russia

USA

61°N

59°

57°

55°

179° 175° 171° 167° 163°W

0.0 1000 m-3

0.5 1000 m-3

2.0 1000 m-3

3.5 1000 m-3

5.0 1000 m-3

6.5 1000 m-3

Figure 4.5: Distribution of age-0 gadids observed from the 1999 summer (June–July) EIT survey (from

Honkalehto et al., 2002).
138 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Figure 4.6: The average locations (centroids), ages 1–8 for pollock year classes that remain concentrated

in the NW area of the EBS shelf and those that shift southeastward as they age. Triangles represent

the centers of survey operations in each year. The average is computed from the period 1975–2001 with

1975–1978, 1989, and 1990 representing the “southeast” year-classes and all other (since 1975) representing

“northwest” year-classes. 60 59 58 57 56 55 54

170 168 166 162 160 164

longitude (W)

latitude (N)

Figure 4.7: Acoustic survey areas used in comparing the Pribilof Island with other shelf areas in the Bering

Sea. Surveys were conducted from 1997 to 1999. Red lines indicate transects.
The Southeast Bering Sea Ecosystem—Part 4 139

and 1999 survey years, when data were available in both areas. Acoustic

data were collected across the Inner Front at four locations: Slime Bank

near Unimak Pass, Port Moller along the Aleutian inner front, and Nunivak

and Newenham transects off the Yukon-Kuskokwim river delta (Fig. 4.7).

Net sample data collected by Coyle and Pinchuk (2002a) suggested that the

abundance of age-0 pollock and zooplankton was about the same order of

magnitude as in the Pribilof region. This was corroborated by the acoustic data comparison. In each region, data were subdivided into nearshore,

offshore stratified, and transition regions.

For the Pribilof region, we selected transect A, as it was found to be representative of conditions. For transects A, B, C, and D (Fig. 4.1), age-0 pollock

mortality estimates between August and September were calculated from the

average differences in fish/m3 derived from acoustic biomasses recorded during the (August) EIT and (September) FOCI survey cruises in the vicinity

of the Pribilof Islands. Mortality estimates were made separately for each

transect line, but not for regions within transects. The identical transect

lines were not run during the EIT surveys, and therefore nearest-matching

segments of the EIT survey tracks were used instead to approximate lines

A, B, C, and D. Only daytime transects were used in all surveys. Acoustic

backscatter was quantified as age-0 pollock according to the algorithms of

Swartzman et al. (2002). Length-frequency distributions of age-0 pollock in

each survey were obtained from Methot trawl samples collected during the

cruises. Length averages were converted to measures of individual target

strength (TS) using an extrapolated Kirchhoff ray-mode model as explained

in Section 4.3, and numbers of fish per m3 on each transect were calculated

as the ratio of pollock acoustic backscatter energy/TS applicable to that

transect.

Each transect area received 1–2 daytime passes in the August surveys

and 2–3 daytime passes in the September surveys. Fish densities and time

stamps (normalized date + hour) of the ∼1–3 passes per area per survey

were averaged. Mortality rates (z; day−1) were calculated as

z = ln(fish m−3August/fish m−3September)

norm.daySeptember − norm.dayAugust

(7)

Results are summarized in Table 4.2.

Given the representativeness of transect A for the Pribilof region, comparison was made with data collected along transects of the four Inner Front

locations. Winter and Coyle found that the nearshore domains in all transects and years generally had the lowest abundance of both fish and zooplankton, while the stratified region generally had the highest abundance

for both groups (Fig. 4.8; details in Section 4.3). Also, there was general

agreement in year-to-year changes in age-0 pollock abundance between all

Inner Front and Pribilof transects, with 1997 having the lowest and 1999

generally having the highest abundance. A comparison of the depth distribution of young-of-the-year pollock and zooplankton showed no apparent

differences between regions and no consistent patterns (Section 4.3). It is

likely that the daytime depth distribution of fish and zooplankton depends
140 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Table 4.2: Comparison of age-0 pollock density and mortality for transect lines A, B, C, and D from

August and September surveys during 1996 and 1997 (“norm.day”: cumulative days where 1 = August 1,

0000 hours).

August September

Year Transect Norm. Day Density Norm. Day Density Mortality

(fish/m3) (fish/m3) (day−1)

1996 A 2.96 0.400 43.14 0.122 0.030

B 3.12 0.336 41.26 0.083 0.037

C 2.15 0.223 41.18 0.116 0.017

D 1.75 0.139 37.46 0.038 0.036

1997 A 2.76 0.173 47.15 0.005 0.081

B 2.67 0.074 44.83 0.005 0.065

C 1.63 0.142 42.90 0.002 0.106

D 1.98 0.045 41.39 0.0006 0.110

on the size and abundance of the fish as well as the depth and strength of

the thermocline (Swartzman et al., 2002).

Comparison of the abundance of zooplankton and fish around the Pribilof

Islands with other front areas (work by Winter and Coyle reported here)

suggest that abundance in the Pribilof region is no higher than in other

eastern Bering Sea frontal regions. While standing stocks are higher in the

front region than nearshore, they appear to be generally higher in stratified

waters offshore of the coastal front (Fig. 4.8). However, T/S Oshoro Maru

data (Fig. 4.2) suggest that abundance of age-0 pollock around the Pribilof

Islands is consistently higher than in adjacent shelf and offshore stations.

Thus, we hypothesize that abundance of age-0 pollock and macrozooplankton around the Pribilof Islands and in other frontal regions in the

eastern Bering Sea is generally elevated compared with shelf and basin abundance. That the Pribilof area supports elevated densities of seabirds and

marine mammals compared with other Bering Sea frontal areas without increased age-0 pollock abundance may be due to its isolation and the closeness

of the front regions to land more than its elevated production. We hypothesize that, although front regions may have elevated primary production relative to adjacent stratified areas, the abundance of macro-zooplankton and

juvenile fish may be consistently higher in the stratified than in front areas.

The greater depths of the stratified regions may provide some protection

from predation for zooplankton and age-0 pollock through diel migration

(Swartzman et al., 2002). Finally, stratified areas adjacent to frontal regions

may have enhanced production due to transport from the front regions. The

stratified regions at the extremities of transect A (away from the islands)

did not show any reduction in age-0 pollock abundance (Swartzman et al.,

2002), while the Oshoro Maru data did show reduced age-0 pollock abundance in shelf regions more remote from the Pribilof Islands. This suggests

that a longer transect than transect A might have been necessary to detect

a lessening in pollock abundance away from the Pribilof Islands.

Given data, as discussed above, on age-0 pollock abundance near the
The Southeast Bering Sea Ecosystem—Part 4 141

Pribilof line A Nunivak Island Cape Newenham Port Moller Slime Bank

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.00.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.00.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.00.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.00.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

near front off near front off near front off near front off near front off

Figure 4.8: Comparison of age-0 pollock (upper panels) and zooplankton (lower panels) abundance per

250-m horizontal bin in the nearshore tidally mixed, transition front, and offshore stratified zones in four

surveyed areas along the Bering Sea shelf in 1999.

Pribilof Islands, near other Inner Front regions, along the MACE survey

route and in shelf areas farther from the Pribilof Islands (Oshoro Maru data),

it is possible to compute the fraction of the entire eastern Bering Sea age-0

pollock abundance near the Pribilof region by computing the total area in

five habitats: nearshore, front and shelf adjacent to fronts, other shelf and

basin, and then multiplying these areas by the density of pollock per unit

area. However, this was not done. It was difficult to relate the abundance

from data taken at different times of the summer (late June, late July, and

early September), because (1) these distributions appear to change over time,

and (2) substantial mortality accrues between the early and later periods.

Thus, we provide only the rough abundance ratios in Table 4.1.

The dominance of particular year classes and differences in their spatial

distribution may explain why in some years early July surveys showed high

abundance of age-0 pollock around the Pribilofs (e.g., 1984 and 1985), while

in others (e.g., 1999) their abundance was lower than in other survey areas.

From 1994–2000, the abundance of age-0 pollock, sampled in late July by

the T/S Oshoro Maru, was consistently higher near the Pribilof Islands than

in surrounding areas. Perhaps as the age-0 pollock become larger and better

able to maintain position, they tend to congregate near the Pribilof Islands,
142 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

whereas earlier in the year, happenstance of birth location determines how

abundant they are near the Pribilof Islands.

4.2.4 How does the abundance of age-0 pollock near the Pribilof

Islands relate to diet, survival, and energetics of higher

predators?

Bird survival and energetics (George Hunt, Beth Sinclair, and

Lucy Vlietstra). It has been difficult to establish a relationship between

pollock abundance near the Pribilof Islands and bird populations or their

energetics parameters. Fledging success on St. George and St. Paul islands

were highly correlated for black and red-legged kittiwakes and common murres, but not for thick-billed murres (Fig. 4.9). This would suggest that some

environmental conditions around the islands are affecting all the breeding

colonies on the islands in the same way, except not thick-billed murres.

However, no relationship to age-0 pollock abundance and fledging success

of any of these species was found. The only significant relationship was

with black-legged kittiwake fledging success and age 3+ pollock abundance,

and it was a negative relationship (Fig. 4.10). This may possibly indicate

competition for age-0 pollock food resources. Current efforts on diets of

these bird species may shed some light on the importance of age-0 pollock to

these birds. Hunt and Vlietstra did find that diets of black-legged kittiwakes

in the 1970s changed through the summer, from primarily age-1 pollock in

July–August to primarily age-0 pollock in September. Since these birds are

near-surface feeders, this suggests that the age-1 pollock became deeper and

unavailable sometime in later summer, while age-0 pollock became large

enough to provide some energetic resources for the kittiwakes (Fig. 4.11).

Studies of pollock otoliths in fur seal scat collected on the

Pribilof Islands 1994–1999 (Rolf Ream). Scat samples from female

foraging fur seals have been collected for many years on the Pribilof Islands.

It is possible that the abundance of age-0 pollock otoliths in the scat can be

used as a surrogate for pollock abundance to extend the data base to previous years and perhaps use scat for prediction of age-0 pollock abundance.

Although they feed over a wide area, it has been suggested that female fur

seals resident for the summer on the Pribilof Islands may top up their stomachs with age-0 pollock just before returning to land. This is supported by

scat data, which show a consistently high presence (and percentage) of age-0

pollock in the scats (Fig. 4.12) and diving behavior from tagged seals (data

from Mike Goebel, University of California, Santa Cruz). As such, it was

felt that age-0 pollock otoliths in fur seal scat might be indicative of their

near-Pribilof abundance.

Trajectories of tagged, feeding fur seals (Robson, 2002; Jeremy Sterling,

NOAA/NMML, personal communication) suggest that the feeding areas of

fur seals depend on the location of their rookery. We examined the percent

occurrence of age-0 pollock otoliths in fur seal scat collected on rookeries

adjacent to the four Pribilof Island acoustic transects (Fig. 4.1) for 1994–1999

with an eye to the relationship of this percentage to the acoustic abundance

of age-0 pollock on each of these transects and years.
The Southeast Bering Sea Ecosystem—Part 4 143

0

0.2

0.4

0.6

Productivity

0

0.2

0.4

0.6

Productivity

0

0.2

0.4

0.6

Productivity

0

0.2

0.4

0.6

Productivity

0

0.2

0.4

0.6

Productivity 0.8

0

0.2

0.4

0.6

Productivity 0.8

0

0.2

0.4

0.6

Productivity 0.8

Reproductive Success

Black-legged Kittiwake, SP

Reproductive Success

Black-legged Kittiwake, SG

Reproductive Success

Red-legged Kittiwake, SP

Reproductive Success

Red-legged Kittiwake, SG

Reproductive Success

Common Murre, SP

Reproductive Success

Common Murre, SG

Reproductive Success

Thick-billed Murre, SP

Reproductive Success

Thick-billed Murre, SG

1975 1980 1985 1990 1995 2000 1980 1985 1990 1995 2000

1975 1980 1985 1990 1995 2000 1975 1980 1985 1990 1995 2000

Black-legged Kittiwake

Reproduction between islands:

r = 0.87, P < 0.0001

Red-legged Kittiwake

Reproduction between islands:

r = 0.84, P < 0.0001

Common Murre

Reproduction between islands:

r = 0.84, P < 0.0006

Thick-billed Murre

Reproduction between islands:

r = 0.06, P < 0.827

1975 1980 1985 1990 1995 2000

1975 1980 1985 1990 1995 2000

1975 1980 1985 1990 1995 2000

1975 1980 1985 1990 1995 2000

0

0.2

0.4

0.6

Productivity

Figure 4.9: Reproductive success in murres and kittiwakes on the Pribilof Islands, AK. Horizontal bars

show range of mean (±SD). Solid bars indicate years in which reproductive success exceeds or falls below

this range. (Figure courtesy of Vlietstra and Hunt.)
144 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

12,000

10,000

8,000

6,000

4,000

2,000

0

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

Pollock biomass - bird productivity

1976197819801982

19841986

198819901992199419961998

age3plkB (1000 t) BL kittiwake

Year

BL Kittiwake (chicks

fledged/nest)

Adult pollock biomass

(1000t)

Figure 4.10: Relationship between black-legged kittiwakes on the Pribilof Islands,

AK and shelf-wide, age-3 pollock abundance.

28

26

24

22

20

18

16

14

12

10

8

6

<5

10 20 30 40 50 10 20 30 40 50 10 20 30 40 50

July August September

From adult stomachs

From chick

regurgitations

Percent of pollock found in sample

Fish length (cm)

Figure 4.11: Seasonal length distribution of walleye pollock taken by black-legged

kittiwakes on the Pribilof Islands, AK.

Scats were collected from rookeries at the end of August. Adult, postparturient females are the main group of feeding northern fur seals present

and the rookery and thus the scat data primarily represent their diet. Based

on preliminary telemetry data (Jeremy Sterling, NOAA/NMML, personal

communication), females forage closer to the island than juvenile males, so

their scats may better represent the food base, and availability of pollock,

closer to the Pribilofs. The data may be biased toward the last meal on the

return leg of the foraging trip, which is desirable for this study because this

percentage is more indicative of pollock abundance on the study transects

rather than farther afield where fur seals usually feed before returning to the

Islands.

Otoliths were used to identify and age pollock. Percent occurrence (PO
The Southeast Bering Sea Ecosystem—Part 4 145

1994 1995 1996

1997 1998 1999

n = 77 n = 32 n = 58

n = 14 n = 16 n = 1

100

75

50

25

0

100

75

50

25

0

100

75

50

25

0

100

75

50

25

0

100

75

50

25

0

100

75

50

25

0

Pollock age group Pollock age group

Pollock age group Pollock age group

Percent occurrence

0 1 2 3 4 5+ 0 1 2 3 4 5+ 0 1 2 3 4 5+

0 1 2 3 4 5+ 0 1 2 3 4 5+ 0 1 2 3 4 5+

Figure 4.12: Percent occurrence of different age classes of pollock in fur seal scat

collected on St. Paul Island rookeries adjacent to transect A.

or FO) was calculated as the number of samples with a given age class of

pollock, divided by the number of samples with pollock otoliths that could

be aged.

Data were examined first for all rookeries combined on St. Paul in 1994–

1999 (1994, 1996 highest for age 0 pollock, 1998 lowest). The lowest age0 pollock abundance occurred in 1997. There appears to be an increased

percent occurrence in scat of older rather than age-0 pollock in 1997.

Data from individual rookeries on St. Paul were then assigned to acoustic

transects A, B, and C (G. Swartzman, UW/APL, personal communication)

based on the overlap of the female foraging locations and the location of

the transects. Note that some rookeries were assigned to two transects.

For transect A (NE St. Paul) the percent occurrence of age-0 pollock was

highest in 1994 and lowest in 1998 and 1995). Transect B (SW St. Paul)

had a similar percent occurrence of age-0 pollock for most years, with the

exception of 1998 when it was much lower. On transect C (SE St. Paul) the

percent occurrence of age-0 pollock was highest in 1994 and 1996, lowest in

1998 (Fig. 4.12). Data from 1999 is included only for completeness because

the sample size is too small.

Finally, data for all rookeries on St. George were combined to correspond

to transect D. The percent occurrence of age-0 pollock was highest in 1995

and 1996, lowest in 1998 and 1997. In general, it appears that the percent

occurrence of older pollock is greater on St. George (Rolf Ream, unpublished

data), though 1996 had very few older pollock.

These results (Fig. 4.12) suggest that frequency of occurrence of age0 pollock in fur seal scat cannot be used to distinguish large year classes

because the percentages of age-0 pollock in the scats are so high in most cases.

It would be preferable to explore the relationship between age-0 pollock
146 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

otoliths in scat and pollock abundance using the percentage of otoliths in

the scat rather than percent occurrence (percent of the samples that have any

age-0 pollock otoliths). Nonetheless, the data suggest that low abundance

year classes may be distinguishable using percent occurrence of age-0 pollock

otoliths in fur seal scat.

4.2.5 How important is predation to pollock abundance near the

Pribilof Islands?

Several approaches were adopted to look at the importance of predation to

the abundance and distribution of age-0 pollock near the Pribilof Islands:

1. Comparison of pollock abundance and distribution along transect A

(Fig. 4.1) to the abundance of groundfish predators (see Lang et al.,

2000).

2. Comparison of the relative abundance of age 1+ pollock and other

age-0 pollock fish predators by year in the Pribilof Island region to

their abundance in other shelf regions including the Inner Front region

and to the abundance of age-0 pollock (from acoustic surveys) in the

Pribilof Island region. This comparison will be based on data collected

in the annual groundfish survey in July–August (1978–2001).

3. Review of the Pribilof Island acoustic surveys and the Inner Front

surveys (by Andreas Winter) to assess where fish schools having large

target strength (for individual targets) were encountered along the

transects and to relate this to the distribution of age-0 pollock (i.e.,

are they proximate or divergent from the predator locations?).

4. Use of the ECOPATH model (Ciannelli) for the Pribilof Island region

to estimate the amount of consumption during September of each study

year (1994–1999) of age-0 pollock by fur seals, fish predators, jellyfish,

and birds, and compare this to standing stock and production. Diet

information will be updated with input from Troy Buckley (groundfish), Rolf Ream and Beth Sinclair (fur seals), and George Hunt and

Lucy Vlietstra (birds).

Predation by fish on age-0 pollock near the Pribilof Islands

compared with other areas in the eastern Bering Sea (Troy Buckley). Predation by fishes on age-0 walleye pollock has been described as

part of a larger, ongoing study examining the trophic relationships in the

eastern Bering Sea (Livingston, 1991; Livingston et al., 1993; Livingston

and deReynier, 1996; Lang et al., 2003). In these documents the length

composition, the relationship to predator size, and the geographic distribution of predation of walleye pollock are described. From these documents,

it appears that signals from strong year classes can be seen in the preylength composition of various fish predators. Currently these data are being

compiled and analyzed with regard to the spatial distribution of the prey,

including age-0 walleye pollock. The diet of predatory fish in the Pribilof region will be examined to see whether these diets include more age-0 walleye
The Southeast Bering Sea Ecosystem—Part 4 147

pollock than the diet in other areas and whether this changes from year to

year. If the diets of Pribilof Island region groundfish are higher in age-0 pollock, this could suggest higher densities of age-0 pollock and/or that there is

higher vulnerability to predation in that area. A majority of these data are

collected during the spring and summer when piscivory tends to be lower

because of the abundance of zooplankton. Lang et al. (2000) conducted a

study directed toward examining predation on age-0 walleye pollock in the

vicinity of the Pribilof Islands in late summer and found very high rates

of predation. Ciannelli (2002) reviewed data from Lang et al., (2000) and

suggested that predation pressure may vary considerably from year to year.

Information on the distribution of walleye pollock year classes ages 1

through 8 suggested that some year classes predominated in the northwestern part of the eastern Bering Sea, while others appeared to predominate in the southeastern part of the EBS (Fig. 4.6; Buckley et al., 2001;

http://www.afsc.noaa.gov/refm/reem/Default.htm). Northwest year

classes, which appear to remain from year to year in the northwestern region

during the summer, may not be contributing to the Bogoslof and Unimak

spawning stocks. Spawning has been observed northwest of the Pribilof Islands from June through August (Hinckley, 1987).

Results of ECOPATH analysis for 1996–1997 (Lorenzo Ciannelli). The ECOPATH model (Polovina, 1984), which was discussed earlier on the basis of an average year, was run for the Pribilof Island region for

1996 and 1997 separately. These years were chosen because the age-0 pollock abundance was extreme for the study period, being highest in 1996 and

lowest in 1997. Figure 4.13 compares the percentage of total consumption of

several groups, including juvenile gadids (over 90% age-0 pollock) for 1996

with that in 1997. While the model runs in Fig. 4.13 include differences

in age-0 pollock abundance for the two years, they assume the same diet

selectivity for all their predators does not account for possible differences in

abundance between these predators.

As can be seen (Fig. 4.13), there is little to choose from between the

two years. In both years, flatfish and large jellyfish dominate predation on

juvenile gadids, with adult pollock also contributing a significant amount of

mortality through cannibalism. The results are almost identical despite the

large difference in abundance. These results, which are admittedly speculative, reflect the linear nature of the model as well as the model parameters

chosen. There was not enough time to analyze the groundfish stomach contents in those two years to distinguish any major change in diet composition.

As such, only the biomass estimate of age-0 pollock changed, which apparently did not affect mortality sources as much as the diet compositions did.

Future work, with diet information, as outlined the previous section of this

report, may remedy this situation. The extremely high predation by jellyfish

on juvenile gadids is surprising. Although large jellyfish and age-0 pollock

are seen in close association, the two being forced together in the cod end of

nets may bias the consumption of pollock by jellyfish. On the other hand,

jellyfish showed extremely large increases in abundance near the Pribilof Islands during the 1990s and in theory could have eaten many age-0 pollock

in 1996 and 1997.
148 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

1996

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Juvenile

gadids

Forage Mesopelagic Squids

Prey

Predation mortality (%)

Marine mammals

seabirds

Other groundfish

Flatfish

Pollock

Grenadier

Big Jellies

Squids

Predator

1997

0%

20%

40%

60%

80%

100%

Juvenile

gadids

Forage Mesopelagic Squids

Prey

Predation mortality (%)

Marine mammals

seabirds

Other groundfish

Flatfish

Pollock

Grenadier

Big Jellies

Squids

Predator

Figure 4.13: Partition of predation mortality of key forage species, 1996 and 1997.

Although we have not attempted the calculation because of our concerns

about model parameter estimates, it is possible to compute, for each year, the

total consumption of age-0 pollock by various predators and its percentage of

the standing stock. To do so, however, considerable effort will be needed to

improve our estimates of year-to-year changes in predator abundance as well

as diets in the neighborhood of the Pribilof Islands—work that is proceeding

apace, but not yet finished. Given the predicted large impact of jellyfish

predation on age-0 pollock, further effort is needed on substantiating the

consumption rates, abundance, and diet composition of jellyfish—a project

not yet attempted.
The Southeast Bering Sea Ecosystem—Part 4 149

4.3 Comparison of abundance of juvenile pollock and zooplankton between the Pribilof Islands and other shelf areas in the eastern Bering Sea based on acoustic surveys

in 1997, 1998, and 1999 (Andreas Winter and Kenneth

Coyle)

The waters around the Pribilof Islands have been identified as an important

nursery area for juvenile walleye pollock (Swartzman et al., 2002). Hydrographic fronts surrounding these islands (Stabeno et al., 1999a) are believed

to play an important role in structuring the habitat and concentrating food

resources for the juvenile fish (Ciannelli et al., 2002), as likewise do fronts

occurring in other regions of the eastern Bering Sea (Coyle and Pinchuk,

2002a).

It has been suggested that conditions for juvenile pollock in the Pribilof

Islands area may be representative of rearing conditions throughout the

Bering Sea (Brodeur and Wilson, 1999; Swartzman et al., 2002) and serve as

an indicator for Bering Sea pollock production. Thus, a question of current

interest is how the Pribilof Islands area compares to other frontal regions

with respect to the population of juvenile pollock it supports from year to

year. In this section, we address the question by examining echo-integration

data from three recent years (1997, 1998, 1999) in which acoustic fishery surveys were conducted, at approximately the same times, around the Pribilof

Islands and four other locations of the eastern Bering Sea.

4.3.1 Methods

The Pribilof surveys were carried out in September of each year under the

Bering Sea FOCI (Macklin, 1999a) and SEBSCC programs (Macklin, 1999b),

and occupied four transects radiating north and south from St. Paul and St.

George Islands (Fig. 4.1). Acoustic data on these surveys were collected

using a hull-mounted SIMRAD EK-500 split-beam echo-sounder system operating at 38 and 120 kHz, and integrated to horizontal × vertical resolutions

of ∼9 m × 0.5 m in 1997 and 1999, and ∼9 m × 1 m in 1998.

Nunivak Island, Cape Newenham, Port Moller, and Slime Bank were

surveyed between late July and early September as part of the Inner Fronts

project (Coyle and Pinchuk, 2002a). Each sampling location comprised up to

three parallel transect lines running orthogonal to the bathymetry (Fig. 4.6).

Acoustic data were collected with an HTI model split-beam system operating

at 43 and 120 kHz. Sampling was conducted during the day only. The

transducers were towed beside the vessel about 4 m from the hull and 2 m

below the surface. Echo data were integrated to (horizontal × vertical)

∼180 m × 2 m in 1997, and ∼45 m × 1 m in 1998 and 1999.

The acoustic data were processed to delineate shoals of fish and patches

of zooplankton. In order to derive comparable biomass indices, a common

algorithm was applied to the data of both sets of surveys. This algorithm

is described in Swartzman et al. (1999, 2002). Briefly, the spatial echointegration units (i.e., pixels) are thresholded to the ranges of backscatter

expected for fish or zooplankton aggregations at the appropriate frequency,
150 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

and morphologically filtered to retain only contiguous areas (patches) greater

than a given minimum size. Fish shoals only are expected to have backscatter

within threshold range at the low frequency, and having higher backscatter

(above a minimum value) at the high frequency than at the low frequency

identifies zooplankton patches.

Because this algorithm was designed for the Pribilof Islands surveys (38

and 120 kHz; ∼9 m horizontal resolution), adjustments had to be taken into

consideration for using it on the Inner Fronts surveys (43 and 120 kHz; ∼45–

180 m horizontal resolution). The difference between 38 and 43 kHz as the

low frequency turned out to be negligible for juvenile pollock, as evidenced by

target strength vs. length plots calculated from the Kirchhoff ray mode model

(Hazen and Horne, University of Washington, personal communication).

However, the large discrepancies in echo-integration resolution (9 m vs.

45–180 m horizontally) required calibration of the backscatter-difference

value used to identify zooplankton patches. Since each pixel averages the

echo return over its area, a larger pixel size (lower resolution) requires a

lower backscatter-difference value to represent the same level of discrimination between the two frequencies. Through empirical testing, we found

an appropriate calibration factor to be given by dividing the backscatterdifference value by the square root of the ratio of the spatial resolutions

that need to be normalized with each other. For example, 5 dB is used as

the standard backscatter-difference value for spatial resolutions averaging

9 m × 0.5 m (Swartzman et al., 1999). A spatial resolution of 45 m × 1 m

would thus be set to a backscatter difference of:

5 dB/



45 m × 1 m

9 m × 0.5 m

= 1.58 dB (8)

After processing of the echo-integration data, acoustic biomass indices

for fish and zooplankton were calculated as the sums of backscatter (sA) per

pixel × pixel area. Acoustic biomass totals were calculated separately for

sampling locations and for habitat regions at each location; i.e., nearshore

(mixed), front, and offshore (stratified) habitats. Boundaries between habitat regions follow the definition of Stabeno et al. (1999a), and are described

in Ciannelli et al. (2002), Kachel et al. (2002), and Swartzman et al. (2002).

Acoustic biomass totals of pollock and zooplankton per area and habitat

are summarized as box plots in Figs. 4.14–4.16 for each year of the study.

Following the convention used by Swartzman et al. (1999), the data were

binned by 250 m of horizontal distance. The acoustic biomass totals per bin

were then modeled as a function of distance using generalized additive models (GAM; Hastie and Tibshirani, 1990). In order to standardize transects

and highlight the effect of fronts, distances were scaled to an index such that

the front region of each transect goes from 0 (nearshore boundary) to 1 (offshore boundary). These GAMs are plotted in Figs. 4.17–4.19. The relative

distributions by depth of the acoustic biomass are plotted in Fig. 4.20 for

each study area and year. Only transect lines “A” of the Pribilof Islands

surveys have been included in the analysis.
The Southeast Bering Sea Ecosystem—Part 4 151

Pribilof line A Nunivak Island Slime Bank

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

nearshore front offshore nearshore front offshore nearshore front offshore

Figure 4.14: Box plots of acoustic biomass per 250-m bin, by habitat region, in each survey area, 1997.

Top panels (blue): pollock. Bottom panels (green): zooplankton.

Pribilof line A Nunivak Island Cape Newenham Slime Bank

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

nearshore front offshore nearshore front offshore nearshore front offshore nearshore front offshore

Figure 4.15: Box plots of acoustic biomass per 250-m bin, by habitat region, in each survey area, 1998.

Top panels (blue): pollock. Bottom panels (green): zooplankton.
152 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Pribilof line A Nunivak Island Cape Newenham Port Moller Slime Bank

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.00.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.00.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

0.20 0.15 0.10 0.05 0.0 0.20 0.15 0.10 0.05 0.0

near front off near front off near front off near front off near front off

Figure 4.16: Box plots of acoustic biomass per 250-m bin, by habitat region, in each survey area, 1999.

Top panels (blue): pollock. Bottom panels (green): zooplankton.

Pribilof A 97 - fish Pribilof A 97 - plankton

Nunivak Island 97 - fish

Slime Bank 97 - fish

Nunivak Island 97 - plankton

Slime Bank 97 - plankton

–0.005 0.0 0.005 –0.0004 0.0 0.0004 –0.002 0.0 –0.004

–0.005 0.0 –0.015 –0.0002 0.0002 –0.0005 0.0005 –0.0015

–1 0 1 2 3 –1 0 1 2 3

–1 0 1 2 –1 0 1 2

0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0

distance index distance index

s (distindex) s (distindex) s (distindex)

s (distindex) s (distindex) s (distindex)

Figure 4.17: GAM smoothed plots of acoustic density as a function of distance index per survey area in

1997. Vertical lines on the plots mark the inner and outer boundaries of the hydrographic fronts.
The Southeast Bering Sea Ecosystem—Part 4 153

Pribilof A 98 - fish

Nunivak Island 98 - fish

Slime Bank 98 - fish

Pribilof A 98 - plankton

Nunivak Island 98 - plankton

Slime Bank 98 - plankton

Cape Newenham 98 - fish Cape Newenham 98 - plankton

–0.5 0.0 0.5 1.0 1.5 –0.5 0.0 0.5 1.0 1.5

–0.5 2.0 0.0 0.5 1.0 1.5 0.0 0.5 1.0 1.5 –0.5 2.0

–2 –1 0 1 2 –2 –1 0 1 2

0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0

–0.04 0.0 0.02 –0.0002 0.0002 0.0 –0.002 0.002 0.0 0.004 –0.004 0.004 0.0

–0.010 0.0 0.005 –0.0010 0.0 –0.002 0.002 0.006 –0.004 0.0 0.004

distance index distance index

s (distindex) s (distindex) s (distindex) s (distindex)s (distindex) s (distindex) s (distindex) s (distindex)

Figure 4.18: GAM smoothed plots of acoustic density as a function of distance index per survey area in

1998. Vertical lines on the plots mark the inner and outer boundaries of the hydrographic fronts.

Pribilof A 99 - fish

Nunivak Island 99 - fish

Slime Bank 99 - fish

Pribilof A 99 - plankton

Nunivak Island 99 - plankton

Slime Bank 99 - plankton

Cape Newenham 99 - fish Cape Newenham 99 - plankton

Port Moller 99 - fish Port Moller 99 - plankton

–0.5 0.0 0.5 1.0 1.5 –0.5 0.0 0.5 1.0 1.5

–0.5 0.0 0.5 1.0 1.5 2.0 2.5

0.0 0.5 1.0 1.5 2.0 2.5 3.0 0.0 0.5 1.0 1.5 2.0 2.5 3.0

–2 0 4 2 –4 –2 0 4 2

0 3 1 2 0 3 1 2

0 2 1

–0.04 0.0 0.02 –0.02 –0.001 0.001 0.003 –0.005 0.0 0.005 0.010 –0.004 0.0 0.004 –0.010 –0.004 0.0 0.004

–0.004 0.0 0.004 –0.008 –0.0010 0.0 0.0010 –0.001 0.001 0.0 –0.002 0.001 0.0 –0.002 0.0 0.002 –0.004

distance index distance index

s (distindex) s (distindex) s (distindex) s (distindex) s (distindex)s (distindex) s (distindex) s (distindex) s (distindex) s (distindex)

Figure 4.19: GAM smoothed plots of acoustic density as a function of distance index per survey area in

1999. Vertical lines on the plots mark the inner and outer boundaries of the hydrographic fronts.
154 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

1997 1998 1999 0 –20 –40 –60 0 –20 –40 –60 0 –20 –40 –60 0 –20 –40 –60 0 –20 –40 –60 –80

depth (m) depth (m) depth (m) depth (m) depth (m)

Pribilof A

Nunivak Island

Cape Newenham

Port Moller

Slime Bank

Figure 4.20: Bar plots of the relative depth distributions in 1 m decrements of acoustic pollock (blue) and

zooplankton (green) biomass in each survey area by year and habitat region. Each panel represents from

left to right the nearshore, front, and offshore regions.

4.3.2 Results

Three of the five study areas were surveyed in all three years. Pribilof line

A had its lowest average pollock biomass density in 1997, and the acoustic

pollock sign tended to be clustered near the inner boundary of the front.

In 1998 and 1999, the pollock sign was denser and more evenly distributed

among habitats, with maximum concentration near the outer boundary of

the front. Both pollock and zooplankton average acoustic biomass were

concentrated closest to the bottom in 1997 and highest in the water in 1999.
The Southeast Bering Sea Ecosystem—Part 4 155

Among individual transects within each year the depth distribution was

variable.

In the Nunivak Island area, average pollock biomass was lowest in 1997

over the nearshore and front habitats and lowest offshore in 1998. One acoustic transect was recorded in 1997, and showed pollock sign to be maximal

near the outer boundary of the front, with vertical distribution throughout the water column. Offshore, the heaviest pollock sign occurred high in

the water, with a second mode at intermediate depth, and near shore small

schools were present mid-water and near bottom. In 1998, density increased

from nearshore outwards, leveled through the front, and increased again offshore. The nearshore pollock sign occurred on the bottom, and from the

front toward offshore formed a narrow mid-water band. In 1999, very little

pollock sign was detected nearshore; the acoustic density increased through

the front, leveled off around the outer boundary of the front, and then increased again offshore.

The Slime Bank area also had its overall lowest pollock densities in 1997

(although the near-shore was not surveyed that year). Pollock density decreased beyond the outer boundary of the front and predominantly moved

to deeper water. In 1998, average pollock densities increased from shore to

the front, leveled off across the front, then increased again offshore. In 1999,

densities increased through the outer boundary of the front, then decreased.

In both 1998 and 1999, acoustic pollock sign occurred mostly near-bottom

in the shallows and slope of the transects, and throughout the water column

in the deep part. In 1998, the offshore depth distribution spread toward

distinct modes in deep- and mid-water. In 1999, a dense, narrow band of

pollock sign was also detected just below the surface.

Cape Newenham was surveyed in 1998 and 1999. In 1998, pollock density

increased from the outer part of the front toward offshore. Separate aggregations high and low in the water column were evident across the length of

the transects. The smaller, upper bands of acoustic sign appeared distinctly

restricted by the thermocline below. In 1999, pollock density increased nearmonotonically from nearshore to offshore, with a slight plateau at the outer

boundary of the front. No pollock sign at all was detected in the nearshore

region. Horizontally stratified biomass distributions, similar to 1998, occurred throughout the front and offshore.

Port Moller was surveyed only in 1999, from the front to offshore. Average pollock density increased in the offshore direction until just beyond

the outer boundary of the front, and then leveled off. The average vertical

distributions were the most uniform of any of the surveys, although some

evidence of stratification did appear on the transects.

4.3.3 Discussion

The 2001 eastern Bering Sea pollock stock assessment (Ianelli et al., 2001)

estimated 1-year-old stock sizes of 16482, 16233, and 14994 millions from

the 1997, 1998, and 1999 year classes, respectively. The relative sizes of

those stock assessments are not supported by the acoustic survey data presented here. Rather, 1997 appeared to be substantially the weakest year-class
156 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

throughout the eastern Bering Sea. The temporal variability of the Inner

Fronts surveys prohibits definite conclusions about the absolute quantities of

biomass, but the data are sufficiently consistent to indicate larger numbers

of age-0 fish in 1998 and 1999 than in 1997. The average offshore densities

were highest in 1999 in all areas surveyed (non-significantly different from

1998 in the Pribilof area), and lowest in 1997 in all areas except Nunivak

Island. The three easternmost areas (Cape Newenham, Port Moller, and

Slime Bank) had very consistent average offshore densities in 1999, the year

that all three were surveyed. Cape Newenham and Slime Bank were similar

also in 1998, suggesting that the juvenile pollock populations in those areas

might be closely related. Pribilof and Nunivak Islands varied independently

of the other three areas, but their acoustic biomass were always within the

same order of magnitude.

Average acoustic plankton biomass was lowest in all habitat regions in

1997 for Nunivak Island and Slime Bank areas, and lowest on Pribilof line A

everywhere except the front region. All areas except the Nunivak Island front

region had higher average plankton biomass in 1998 than in 1999, although

the differences were generally not very great. By contrast, Coyle and Pinchuk

(2002a) found no evidence of significant interannual differences in euphausiid

biomass. The relative density distributions of acoustic plankton sign, as a

function of distance index, closely matched the distributions of fish sign in

most areas and years (Figs. 4.17–4.19). Two visible exceptions are Pribilof

line A in 1997, where plankton densities remained near maximum in the

offshore, while fish densities strongly decreased; and Slime Bank in 1997

where fish densities were high throughout the front, while plankton densities

increased from the inner to the outer boundary of the front.

Frontal structures had an evident impact on the horizontal distributions

of fish biomass. In five of the twelve survey areas, the maximum of the

smoothed GAM density occurred within 30% of the frontal width from the

outer boundary (i.e., distance index 0.97–1.30). In three further surveys,

either the absolute or a local maximum density occurred inside the front.

Overall, maximum fish density, and maximum plankton density, was offshore

of the fronts in all surveys except Pribilof line A during 1997 and 1998.

The relative depth distributions of pollock and zooplankton substantially

correlated over most of the surveys. Exceptions occurred primarily in areas

of high pollock density, e.g., Slime Bank and Port Moller offshore in 1999;

and Pribilof line A front and offshore in 1998 (Fig. 4.20). Those observations

may indicate consumption or avoidance, but are possibly due to masking of

the acoustic zooplankton sign by pollock. As the transects in this study were

recorded by day, the patterns cannot be directly interpreted as feeding interactions, which are known to take place primarily at dusk (Schabetsberger

et al., 2000). A common overall trend among the surveys appears to be that

pollock were deepest in the water in 1997, intermediate in 1998, and nearest

to the surface in 1999. Further data analyses will be needed to examine possible causes for this outcome, e.g., predation pressure from above or below,

or the water temperature gradients.
The Southeast Bering Sea Ecosystem—Part 4 157

4.4 Summary and Conclusions

In this section, we addressed the importance of age-0 pollock in the Pribilof

Island region as possible indicators of eastern Bering Sea pollock recruitment,

and we discussed relationships of age-0 pollock to their prey and predators.

To this end, we defined how large an area to include as the Pribilof region,

and how age-0 pollock abundance in the Pribilof Island region relates to their

abundance in other parts of the eastern Bering Sea.

We defined the Pribilof Island region as an area extending along the

shelf and shelf-break 100 nm from the center of the islands, based on runs of

the energetic-trophic web-based ECOPATH model that suggested that this

region had the highest ecological efficiency—that is, the largest percentage

of the energetic demand of region biota was met by food sources within the

region.

Of all the drogued buoys released near Unimak Pass during the pollock

spawning season, a significant percentage ended up near the Pribilof Islands,

suggesting that the pollock found near the Pribilofs during summer are likely

coming from these spawning areas. There were year-to-year changes in the

percentage of released buoys reaching the Pribilof Islands. Based on the

timing of these results, pollock found near the Pribilof Islands during June

and July, on which some of our comparisons are based, could not have come

from the Bogoslof and Unimak spawning areas and were spawned closer to

the islands.

There may be an interaction among the spatial distribution of adults

(that appears to have two modes over the years: a northwest and southeast

mode), the relative importance of different pollock spawning areas and the

summer wind patterns that can influence changes in the distribution of age-0

pollock over the summer. To whit, the abundance of age-0 pollock near the

Pribilof Islands may change drastically throughout the summer.

We examined the abundance density (abundance per unit area) of age-0

pollock near the Pribilof Islands relative to abundance in surrounding shelf

areas and areas along the Inner Front. Findings show that, while pollock

abundance was generally higher than in the surrounding area, it was about

the same as abundance in the Inner Front region.

It is difficult to establish a relationship between bird and mammal diets

and abundance of age-0 pollock near the Pribilof Islands. No such relationships were found for murres or kittiwakes. Percent occurrence of age-0

pollock otoliths in fur seal scat was high throughout most summers, which

suggests that large year classes cannot be distinguished from moderate sized

year class by scat samples. However, small pollock year classes may be

distinguishable using scat data.

When autumn age-0 pollock abundance near the Pribilof Islands, assessed

using acoustic surveys from 1994–1999, was compared to pollock recruitment

from the AFSC pollock model, the root mean square error was about the

same as similar comparisons of the pollock model to age-1 estimates from

the annual bottom trawl survey and age-3 estimates from the EIS survey.

This suggests that age-0 pollock may provide an acceptable early predictor

for pollock year-class strength (Section 3.3.6).

The Southeast Bering Sea Ecosystem—Part 5 159

Part 5—SEBSCC Administration and

Management

From the outset, the University of Alaska Fairbanks, the NOAA Alaska Fisheries Science Center, and the NOAA Pacific Marine Environmental Laboratory managed SEBSCC jointly. NOAA Coastal Ocean Program was the

funding agency.

The project’s management goals were to

• Conduct a first-quality scientific program that supported a specific

goal to provide critical knowledge needed for formulating policy and

management of resources of the southeastern Bering Sea ecosystem.

• Build partnerships and encourage multidisciplinary cooperative efforts

among research scientists within the academic community, NOAA, and

other agencies interested in the southeastern Bering Sea.

• Provide an open process in establishing research objectives and proposal selection to ensure quality and diversity.

The project management structure consisted of a Project Management

Team, a Technical Advisory Committee, a Research Council, and Office of

Coordination. This structure built on successful structures of the Coastal

Ocean Program, i.e., NECOP, SABRE, and Bering Sea FOCI. In addition,

it provided a coordinated forum for marine ecological research in the southeastern Bering Sea.

5.1 Project Management Team

The Project Management Team (PMT) actively led the scientific conduct

of SEBSCC, maintained financial and project accountability, and directed

project administration. A primary function of the PMT was to assemble a

multidisciplinary research team for multi-year investigations of the southeastern Bering Sea ecosystem during each of the three research phases of

SEBSCC. The PMT conducted a workshop early in 1996 to define specific

2-year and 5-year research objectives for investigations into biophysical domains, juvenile pollock productivity, and modeling. The SEBSCC workshop

attracted a substantial group of potential PIs. A recently completed National Research Council review of the Bering Sea ecosystem and a PICES

Workshop on the Bering Sea helped establish a foundation for discussion.

Shortly after the workshop, and at two other intervals in the life of the

project, the PMT conducted a competitive, peer-reviewed proposal process.

SEBSCC’s Technical Advisory Committee (section 5.2) ranked the proposals

based on peer-review scores and scientific merit, making a recommendation

for subproject funding to the Coastal Ocean Program.

During all research phases of SEBSCC, the PMT assured that balance

and integration was maintained among subprojects, and that academic,

NOAA, and resource manager viewpoints were included. The PMT, with

guidance from the TAC and RC, prioritized research, and adjusted the mix
160 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

of investigators during the progress of the study to reflect the evolving needs

for observation, modeling, and synthesis. The PMT was responsible for ensuring that integrated results were passed to management organizations such

as the North Pacific Fishery Management Council.

The primary way that the PMT achieved the SEBSCC research and

management goals was through clear guidelines of accountability. The PMT

acted as COTRs (monitors) on the accepted proposals. The expertise within

the PMT included integrative approaches to modeling fisheries stock structure; lower-trophic, process-oriented research; and a regional oceanographic

approach. The PMT also balanced a research orientation with a NOAA perspective of providing scientific products to the North Pacific Fishery Management Council. The PMT members did not compete for funds from the

program. The composition of the team was as follows:

Vera Alexander, University of Alaska Fairbanks: Dr. Alexander is Dean

of the School of Fisheries and Ocean Sciences. She has a long career of

studying the Bering Sea, specializing in physical influences on lower trophic

level processes. She is one of two U.S. delegates to PICES and serves on

numerous research and review boards. The North Pacific Marine Science

Organization (PICES) was established to promote scientific coordination

among Pacific Rim nations. Dr. Alexander is a Fellow of the American

Association for the Advancement of Science, the Arctic Institute of North

America, and the Explorers Club.

Anne Hollowed, Alaska Fisheries Science Center: Dr. Hollowed provides

scientific advice to the North Pacific Fishery Management Council for the

Gulf of Alaska fisheries. She works with population models and publishes

on the connectivity between strong year classes throughout the North Pacific. She serves on the steering committee for U.S. GLOBEC and is leading

PICES-GLOBEC planning to address the issue of carrying capacity and climate change in the North Pacific and Bering Sea.

Jim Overland, Pacific Marine Environmental Laboratory: Dr. Overland

has published research on the Bering Sea for 20 years. He was co-director

of the Bering Sea FOCI project for the previous 5 years and conducted two

proposal solicitations. He served a 5-year term as an editor for the Journal

of Geophysical Research-Oceans and served on the National Research Council Committee for the Coastal Ocean and the PICES Bering Sea Working

Group. Dr. Overland provided the primary contact with the Coastal Ocean

Program office.

All members of the PMT agreed to undertake the following:

• Actively manage the scientific conduct of this research.

• Participate in meetings for planning and coordination of the program.

• Evaluate and report on interim progress and steps required to meet

the project objectives.

• Prepare annual implementation plans.

• Ensure that quality-controlled data are made available to other investigators in a timely manner.
The Southeast Bering Sea Ecosystem—Part 5 161

• Participate in synthesis and interpretation of research results and the

development of products of value to environmental and scientific communities.

• Participate in selected fora to encourage communication between the

resource management and scientific communities.

• Encourage the publication of research results in the peer-reviewed literature for the benefit of the marine scientific community.

• Prepare a data management plan and schedule.

5.2 Technical Advisory Committee

The Technical Advisory Committee (TAC) provided independent oversight

to the PMT. Members reviewed the science implementation plan and suggested how to better coordinate the program to meet its goal and objectives.

They provided peer-review of proposals and recommended subprojects for

funding. TAC members participated in annual meetings, helping to guide the

project to meet changing research challenges during the life of the project.

The following members served:

Dr. Michael J. Dagg is Professor, Louisiana Universities Marine Consortium, Chauvin, Louisiana. He was Interim Director during 1990–1991. Dr.

Dagg was a participant in PROBES and NECOP. His specialty is secondary

production.

Mr. D. Bart Eaton is Vice President of Alaska Operations with Trident Seafoods Corporation. He has been active in the commercial fishing industry for 30 years and is currently partner in two state-of-the-art

catcher/processors operating in the Bering Sea and Gulf of Alaska. Mr.

Eaton is a past member of the North Pacific Fishery Management Council

and was a Technical Advisor to Bering Sea FOCI. Mr. Eaton resigned from

the TAC during phase II.

Dr. Eileen E. Hofmann is Associate Professor at the Center for Coastal

Physical Oceanography, Old Dominion University, in Norfolk, Virginia. Her

primary interest is marine ecosystem models.

Dr. Thomas C. Royer was Professor of Marine Science at the University

of Alaska Fairbanks before moving to Old Dominion University. His specialties are coastal boundary currents and mesoscale ocean circulation with

emphasis on the sub-polar gyre. He is a member of the National Academy of

Science Ocean Studies Board and Committee on the Bering Sea Ecosystem.

Dr. Royer has served as an Associate Editor of the Journal of Geophysical

Research.

Dr. Albert V. Tyler, now retired, was Associate Dean and Professor

of Fisheries at the School of Fisheries and Ocean Sciences, University of

Alaska Fairbanks. Dr. Tyler has served as a Research Scientist with the

Fisheries Research Board of Canada, Professor at Oregon State University,

and Research Scientist with the Canada Department of Fisheries and Oceans.

Dr. Tyler is active in developing models of stock assessment in multi-species
162 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

fisheries and at-sea surveys of groundfish assemblages. Dr. Tyler was chair

of the PICES Bering Sea Working Group.

Dr. Warren S. Wooster is Professor Emeritus of the School of Marine

Affairs, University of Washington, Seattle, Washington. He has held positions as Research Oceanographer and Professor at the Scripps Institution of

Oceanography, Director of UNESCO’s Office of Oceanography, and Dean of

the Rosenstiel School of Marine and Atmospheric Sciences at the University

of Miami. A Technical Advisor to the Bering Sea FOCI, Dr. Wooster also

was Chairman of PICES and a member of the National Research Council

Committee on the Bering Sea Ecosystem. He is a Fellow of the American

Geophysical Union and a Fellow of the American Meteorological Society. His

main area of scientific interest is the relationship between climate and large

marine ecosystems.

Dr. Jerald S. Ault is Associate Professor of Marine Biology and Fisheries at the Rosenstiel School of Marine and Atmospheric Science, University of Miami. He replaced Dr. Wooster on the TAC during phase II. Dr.

Ault’s expertise is in ecosystem dynamics synthesis, specifically theoretical

and applied population and community dynamics for fishery assessment and

management in marine ecosystems.

5.3 Research Council

The Research Council was a loosely knit, informal collective of projectfunded principal investigators and associate PIs from other projects, notably

the NSF Inner Front Study and the NMFS resource survey team. The Council provided a forum for exchange of information on the multidisciplinary

aspects of the southeastern Bering Sea. Several smaller interdisciplinary scientific working groups evolved from the Research Council, as needed. The

continuity of the Council promoted extensive cross-disciplinary cooperation.

5.4 Project Coordination and Communication

SEBSCC supported a small office to coordinate communication among project investigators, other agencies, and researchers studying the SE Bering

Sea ecosystem, and NOAA’s Coastal Ocean Program and National Marine

Fisheries Service. The Project Coordinator was Mr. Allen Macklin, Pacific

Marine Environmental Laboratory, Seattle, Washington. Products provided

were data management, personnel directories, seminar series and announcements, publication and presentation lists, reports and documents, minutes

of meetings, production of conferences and workshops, a graphic archive,

and cruise plans and schedules. The primary method was by a World Wide

Web SEBSCC home page at http://www.pmel.noaa.gov/sebscc. This office maintained a catalog of investigators and encouraged interdisciplinary

contact among investigators.

The SEBSCC Office of Coordination also managed a Bering Sea and

North Pacific Ocean theme page on the World Wide Web (http://www.pmel.

noaa.gov/bering). This site exploits existing home page developments to

create a single, hyper-linked resource that enables any user to browse the
The Southeast Bering Sea Ecosystem—Part 5 163

most recent observational data, scientific analyses, model animations, management and proposal information, and historical perspectives. The site

continues to provide a virtual network for the SE Bering Sea, with participation based on common interest and easy access to information.

5.5 Data Management

SEBSCC followed the lead of the U.S. GLOBEC data policy (U.S. Global

Ocean Ecosystems Dynamics Report Number 10, February 1994). SEBSCC

maintained that the intellectual investment and time committed to the collection and processing of a data set entitles an investigator to the fundamental benefits of the data set. Initial publication of descriptive or interpretive

results derived immediately and directly from the data is the privilege and

responsibility of the investigators responsible for each collection. Accordingly, SEBSCC generally allowed exclusive use of data for 1 year from the

completion of data processing. Data were released for collaboration among

scientists to promote interdisciplinary and comparative interpretation, development of collaborative approaches, and development and testing of new

theories. Any scientist making substantial use of a data set was obligated to

communicate with the investigators who acquired the data and anticipated

that these scientists would be co-authors of published results.

In its request for proposals, SEBSCC’s PMT required a data management plan from each investigator. This plan included documentation of

adequate methods and equipment to meet the quality standards established

for SEBSCC. As part of an interdisciplinary study, each plan was required to

show coordination with other elements. Investigators submitted a schedule

for collecting, processing, analyzing, archiving, and sharing data with other

investigators consistent with SEBSCC standards. The investigators were responsible for archiving data with appropriate agencies and maintaining data

for project sharing, preferably by on-line electronic means; SEBSCC facilitated data sharing. Applicants for research funds agreed to the following

conditions: At least 3 months prior to execution of field sampling or scientific

cruises, investigators will submit a plan to the SEBSCC data management

office which documents the procedure to be used to collect, process, and

analyze samples and data. SEBSCC will then derive a single plan for each

cruise. From the collection of cruise summaries, SEBSCC will publish an

annual data report describing its field operations.

The project developed data quality and timeliness standards following

the GLOBEC model. SEBSCC’s data management plan ensured that all

data were processed, validated, and made available to other investigators.

Metadata (descriptions of how data were collected, processed and analyzed)

are part of the record. Retrospective data sets, numerical experiments, and

field data are included in the database. Data information may be located

through the SEBSCC web site and in the North Pacific Ecosystem Metadatabase (http://www.pmel.noaa.gov/bering/mdb).
164 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

5.6 Research Structure and Methodology

SEBSCC was designed as a three-phase research project. The emphasis of

Phase I was initialization of ecosystem monitoring and modeling, retrospective analysis, and beginning process studies. Phase II continued the monitoring and modeling effort. It also promoted more process studies to address

specific objectives and began the important synthesis stage. Phase III was

devoted to synthesis of results with limited monitoring to support synthesis

activity. Research requirements were based on the following assertions:

• The southeastern Bering Sea shelf is a major region for groundfish,

other commercial species, and marine mammals, containing as much

as 50–85% of the pollock biomass in the Bering Sea.

• Walleye pollock is a nodal species in the Bering Sea ecosystem, i.e., it

dominates the pelagic guild, and, in its juvenile stage, it serves as a

major forage fish. Adult pollock are a major commercial asset for the

United States.

• Because population dynamics processes that determine abundance appear to be chaotic, they are especially sensitive to initial conditions.

The success of age-structured models for pollock in the Bering Sea

shows that much variability in year class abundance is established by

age two. There appears to be top-down predation control of pollock

recruitment, spatial and temporal variability in food supply, and variability in transport processes affecting larval and juvenile pollock. The

sequence of survival processes is non-linear.

• SEBSCC will test the hypothesis that interannual ocean variability

influences the availability of prey, growth rate, predation, and distribution of juvenile pollock and higher trophic level species. Although

we already know that ocean variability can influence fisheries, what

is not known is how these factors specifically co-occur in the Bering

Sea. We use the phrase “carrying capacity” in a general context as to

what limits the potential size of the pelagic guild. From the results of

testing these hypotheses, we will develop annual recruitment indices

for pre-recruit pollock.

The scientific approach that SEBSCC pursued was interdisciplinary research balancing time-series measurements, process studies, models, and

syntheses that were phased over the life of the project. Basic time series

comprised a spring larval survey and autumn juvenile survey over the shelf

and shelf break, moored biophysical platforms in several habitats over a 6-

year period, and periodic hydrographic and water property surveys. The

time series measurements tracked important climate changes, the ecosystem response to these changes, and provided data for model studies. Process and retrospective studies examined horizontal and vertical physical

dynamics influencing the separation and overlap of aggregations of predators and prey, the feeding and switching behavior of juvenile pollock and

their predators, nutrient transport onto the shelf, and the establishment
The Southeast Bering Sea Ecosystem—Part 5 165

Table 5.1: Annual funds received by SEBSCC from NOAA Coastal Ocean Program.

1996 1997 1998 1999 2000 2001 2002

$500,000 $1,070,500 $941,800 $946,700 $964,700 $672,700 $361,800

of pollock as the dominant species. Model-based research implemented a

three-dimensional physical model, an individual-based model (IBM) for larval/juvenile processes, a trophic dynamics model, and a spatially dependent

model of pollock, their predators, and alternate food sources. Synthesis efforts documented a contemporary understanding of the southeastern Bering

Sea ecosystem and produced indices for predicting the abundance of age-1

pollock.

5.7 Resources

The project was awarded $5,458,200 over its lifetime. Annual increments

(Table 5.1) indicate the ramp-up, level-funding, and ramp-down character

of the funding. Annual resources also included ∼30 days aboard NOAA Ship

Miller Freeman, ∼30 days aboard a Class I research vessel, and a cooperative

summer cruise aboard Oshoro Maru.

5.8 Leverage and Collaboration

Southeast Bering Sea Carrying Capacity was a highly leveraged program.

It worked collaboratively with ongoing research by other National Marine

Fisheries Service (NMFS) programs examining pollock resources and ecology of the Bering Sea (fishery acoustics group, stock assessment group, and

Marine Mammal Protection Act studies), programs at the University and

State of Alaska, Environmental Protection Agency, Shelikof Strait FOCI,

Japan Far Seas Fisheries Laboratory, Ocean Research Institute of Tokyo

University, Faculty of Fisheries of Hokkaido University, the Japanese Marine Science and Technology Center, Tokai University in Sapporo, Tohoku

National Fisheries Institute, Korean Ocean Research and Development Institute and the Institute of Marine Biology, Far East Branch of the Russian

Academy of Sciences. SEBSCC also coordinated with the inhabitants of St.

Paul Island. The project promoted collaborative research with the Office of

Naval Research, the National Science Foundation, and the National Aeronautics and Space Administration. Southeast Bering Sea Carrying Capacity

was considered a component in the PICES-GLOBEC Climate Change and

Carrying Capacity (CCCC) Program.

5.9 Research Components and Principal Investigators

SEBSCC advertised for research applicants to address the following scientific

issues:
166 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

• How does climate variability influence the Bering Sea ecosystem? Is

there historical evidence for a biophysical regime shift on the Bering

Sea shelf? How is this reflected in ecological relationships and species

mix? Are there “top-down” ecosystem effects associated with climate

variations as well as “bottom-up” effects?

• What limits population growth on the Bering Sea shelf? Is there evidence of a single species carrying capacity, e.g., for pollock, or a more

complex structure? What is the ecological role of pollock on the Bering

Sea shelf, i.e., how are pollock, forage fish, and apex species linked

through energetics and life history? How important is cannibalism?

• How do oceanographic conditions on the shelf influence biological distributions? How do the separate mixing domains, sea ice, and the cold

pool influence the overlap or separation between predators and prey?

• What influences primary and secondary production regimes? What are

the sources of nutrients to the southeastern Bering Sea shelf, and what

processes affect their availability? Is the variability in sea ice extent

and timing the primary factor influencing productivity? What determines the relative allocation of organic carbon going to benthos versus

that remaining in the pelagic system? What are the lower trophic level

structure and energetics on the shelf in summer and winter, especially

regarding euphausiids? What is the role of gelatinous organisms?

Applicants were selected for the three research phases by the competitive process described in section 5.2 and according to the specific phase’s

research requirements (section 5.6). Funded subprojects are listed by title,

investigator, and institution for phases I, II, and III in Tables 5.2, 5.3, and

5.4, respectively.
The Southeast Bering Sea Ecosystem—Part 5 167

Table 5.2: Research components and principal investigators for Phase I, 1996–1998.

Research Title Investigators Institution

Individual-based modeling of walleye pollock in the southeast

Bering Sea

Hinckley, Sarah

Megrey, Bern

Hermann, Al

NOAA/NMFS/AFSC

NOAA/NMFS/AFSC

U. Washington/JISAO

A spatial model of upper-trophic level interactions in the eastern Bering SeaLivingston, Pat

Hinckley, Sarah

NOAA/NMFS/AFSC

NOAA/NMFS/AFSC

Circulation modeling for the S.E. Bering Sea Hermann, Al

Stabeno, Phyllis

Haidvogel, Dale

Musgrave, David

U. of Washington/JISAO

NOAA/OAR/PMEL

Rutgers University

U. Alaska Fairbanks

Monitoring and development of biophysical indices of the southeastern Bering SeaSchumacher, Jim

Stabeno, Phyllis

Brodeur, Ric

Napp, Jeff

Hunt, George

NOAA/OAR/PMEL

NOAA/OAR/PMEL

NOAA/NMFS/AFSC

NOAA/NMFS/AFSC

U. California Irvine

An altimetric census of mesoscale eddy-like features in the

Bering Sea

Okkonen, Steve U. Alaska Fairbanks

Investigation of the origin and dynamics of nutrients on the

southeast Bering Sea shelf in relation to dominant physical and

biological processes

Whitledge, Terry U. Texas at Austin

Isotopic and biomarker composition of sinking organic matter

in the southeast Bering Sea: indicators of food web structure

Henrichs, Susan U. Alaska Fairbanks

Using optical measurements to explore the influence of

mesoscale eddies on the interaction of lower and higher trophic

levels in the southeast Bering Sea

Stabeno, Phyllis NOAA/OAR/PMEL

High-resolution acoustic and juvenile pollock retrospective data

analysis

Swartzman, Gordie

Brodeur, Ric

U. Washington

NOAA/NMFS/AFSC

Habitat differences in frontal regions around the Pribilof Islands

and their importance to juvenile pollock growth and survival in

the Bering Sea

Brodeur, Ric

Napp, Jeff

Doyle, Miriam

NOAA/NMFS/AFSC

NOAA/NMFS/AFSC

U. Washington/JISAO

Low-temperature incubation of walleye pollock eggs (Theragra

chalcogramma) from the southeast Bering Sea region

Blood, Debbie NOAA/NMFS/AFSC

Natural scales of variability in coastal marine ecosystems of the

eastern Bering Sea

Francis, Bob U. Washington

Historical trends in the number of foraging trips made by lactating northern fur sealsLoughlin, Tom NOAA/NMFS/AFSC

The role of atmospheric forcing on the cold pool and ecosystem

dynamics of the Bering Sea shelf: A retrospective study

Niebauer, Joe

Wyllie-Echeverria, Tina

Bond, Nick

Schumacher, Jim

Stabeno, Phyllis

U. Alaska Fairbanks

U. Washington/JISAO

U. Washington/JISAO

NOAA/OAR/PMEL

NOAA/OAR/PMEL

A retrospective investigation into relationships between southeast Bering Sea pollock recruitment and biophysical correlatesMegrey, Bern

Wespestad, Vidar

NOAA/NMFS/AFSC

NOAA/NMFS/AFSC
168 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Table 5.3: Research components and principal investigators for Phase II, 1999–2000.

Research Title Investigators Institution

Circulation modeling for the S.E. Bering Sea Hermann, Al

Haidvogel, Dale

Stabeno, Phyllis

Musgrave, David

U. Washington/JISAO

Rutgers University

NOAA/OAR/PMEL

U. Alaska Fairbanks

Measures of ecosystem trends: an approach for synthesis and

understanding

Schumacher, Jim Two Crow Environ. Consult.

The regime shift of the 1970s: air-sea interactions crucial to

walleye pollock

Bond, Nick

Kachel, Nancy

U. Washington/JISAO

U. Washington/JISAO

Environmental influences on the early life stages of walleye pollock, Theragra chalcogramma, in the southeastern Bering Sea

during the late 1970s climate regime shift

Kendall, Art NOAA/NMFS/AFSC

Monitoring and use of biophysical indices of the southeastern

Bering Sea: phase II

Schumacher, Jim

Stabeno, Phyllis

Brodeur, Ric

Napp, Jeff

Hunt, George

Two Crow Environ. Consult.

NOAA/OAR/PMEL

NOAA/NMFS/AFSC

NOAA/NMFS/AFSC

U. California Irvine

Currents and transfer processes between shelf and slope waters:

a Lagrangian perspective

Stabeno, Phyllis NOAA/OAR/PMEL

In situ monitoring of nitrate concentrations on the Southeast

Bering Sea shelf

Whitledge, Terry

Stabeno, Phyllis

U. Alaska Fairbanks

NOAA/OAR/PMEL

Sinking organic matter and pelagic food webs Henrichs, Susan U. Alaska Fairbanks

Investigation of origin of nutrients on the Southeast Bering Sea

shelf in relation to physical processes and biological uptake

Whitledge, Terry U. Alaska Fairbanks

Proximity of age-0 pollock, jellyfish, predators and prey Swartzman, Gordie

Coyle, Ken

Brodeur, Ric

Napp, Jeff

U. Washington

U. Alaska Fairbanks

NOAA/NMFS/AFSC

NOAA/NMFS/AFSC

Habitat differences in frontal regions around the Pribilof Islands

and their importance to juvenile pollock survival in the Bering

Sea: Phase II

Brodeur, Ric

Napp, Jeff

Paul, A.J.

NOAA/NMFS/AFSC

NOAA/NMFS/AFSC

U. Alaska Fairbanks
The Southeast Bering Sea Ecosystem—Part 5 169

Table 5.4: Research components and principal investigators for Phase III, 2001–2002.

Research Title Investigators Institution

Synthesis of pinniped and seabird ecology Sinclair, Beth

Hunt, George

Byrd, Vernon

NOAA/NMFS/NMML

U. California Irvine

USFandWS

Ecosystem trends of the southeastern Bering Sea Schumacher, Jim

Springer, Alan

Ianelli, Jim

Napp, Jeff

Stabeno, Phyllis

Two Crow Environ. Consult.

U. Alaska Fairbanks

NOAA/NMFS/AFSC

NOAA/NMFS/AFSC

NOAA/OAR/PMEL

Synthesis of sediment trap, zooplankton, and biophysical

data

Henrichs, Susan U. Alaska Fairbanks

Habitat differences around the Pribilof Islands Brodeur, Ric

Napp, Jeff

Francis, Robert

NOAA/NMFS/NWFSC

NOAA/NMFS/AFSC

U. Washington

Synthesis of acoustic and net data Swartzman, Gordie

Napp, Jeff

Coyle, Ken

U. Washington

NOAA/NMFS/AFSC

U. Alaska Fairbanks

Biophysical monitoring of the southeastern Bering Sea Stabeno, Phyllis

Whitledge, Terry

NOAA/OAR/PMEL

U. Alaska Fairbanks
170 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

5.10 Field Operations: SEBSCC Cruise Schedule

52°

54°

56°

58°

60°

172° 168° 164° 160°

3500

1000

1000

2

4

6

3

Nunivak I.

St. Paul I.

St. George I.

C. Newenham

Port Moller

Slime Banks

Dutch Harbor

Unimak I. 200

Figure 5.1: Field operations area for SEBSCC. Yellow numbers indicate mooring sites. Yellow lines

connecting the moorings are transects for hydrographic, nutrient, phytoplankton, and zooplankton sampling.

The crosshatched circular region on the left indicates the Pribilof Islands study area. The juvenile pollock

sampling area of cooperative cruises aboard Oshoro Maru is shaded dark blue. Transects occupied by the

Inner Front program are shown as orange lines.
The Southeast Bering Sea Ecosystem—Part 5 171 Table 5.5: SEBSCC cruises. Cruise Departure and Departure and Sea Vessel Designator Return Dates Return Ports Days Activities 1996 Miller Freeman MF96-13 Sep 4 to Sep 16 Dutch Harbor to St. Paul 13 Age-0 monitoring, N. Pribilof mooring recovery Miller Freeman MF96-14a Sep 16 to Sep 30 St. Paul to Larsen Bay 15 Bering Sea shelf and Shelikof mooring recoveries 1997 Miller Freeman MF97-01 Feb 10 to Feb 18 to Feb 26 Seattle to Dutch Harbor to Dutch Harbor (8) 9 Test systems, deploy Bering Sea shelf subsurface moorings; NPZ samples, transect, Unimak stations Miller Freeman MF97-04 Mar 28 to Apr 13 Homer to Dutch Harbor 17 Drifters, line 8, egg collections, slope current Miller Freeman MF97-05A Apr 15 to May 1 Dutch Harbor to Dutch Harbor 17 Recover/deploy moorings, NPZ samples, transect, Unimak stations, egg incubation Miller Freeman MF97-05B May 3 to May 13 Dutch Harbor to Dutch Harbor 11 NPZ samples, larval feeding, egg incubation Miller Freeman MF97-06 May 14 to May 21 Dutch Harbor to Kodiak 8 Tsunami moorings, line 8, ADCP, drifters, egg incubation Miller Freeman MF97-07 May 23 to May 31 to Jun 6 Kodiak to Kodiak to Seattle 15 Larval index survey, drifters Alpha Helix HX196 May 29 to Jun 30 Seward to Seward 33 Inner front Wecoma W9706A Jun 4 to Jun 9 Dutch Harbor to Dutch Harbor 6 Deploy/recover moorings, test MPS gear Wecoma W9706B Jun 10 to Jun 27 Dutch Harbor to Dutch Harbor 18 Eddy studies, NPZ samples Wecoma W9706C Jun 30 to Jul 14 Dutch Harbor to Dutch Harbor 15 Habitat survey, ground truth, transect Oshoro Maru OM97 Jul 18 to Aug 2 Seward to Dutch Harbor 16 EBS shelf and St. Lawrence shelf survey: CTD, bottom trawl, NORPAC net, larval net, juvenile beam trawl Alpha Helix HX200 Aug 12 to Aug 28 Dutch Harbor to Dutch Harbor 17 Inner front Miller Freeman MF97-09A Sep 8 to Sep 18 Dutch Harbor to St. Paul 10 Age-0 survey Miller Freeman MF97-09B Sep 18 to Sep 27 St. Paul to Dutch Harbor 10 Mooring recovery, NPZ samples, transect 1998 Miller Freeman MF98-01 Feb 11 to Feb 16; Feb 17 to Feb 27 Seattle to Kodiak; Kodiak to Dutch Harbor (6) 11 Moorings; NPZ samples, transect, Unimak stations Miller Freeman MF98-05B Apr 14 to Apr 29 Dutch Harbor to Dutch Harbor 16 Moorings, NPZ samples, transect, Unimak stations Wecoma (W9804C) and W9804D Apr 24 to Apr 30 to May 5 Newport to Dutch Harbor to Dutch Harbor (6) 5 Mooring or CTD / Unimak / nutrients Wecoma W9805A May 6 to May 22 Dutch Harbor to Dutch Harbor 17 NPZ samples and transects Alpha Helix HX209 May 22 to Jun 24 Seward to Seward 34 Inner front Sir Wilfrid Laurier LA-98-00 Jul 10 to Jul 15 Kodiak to Nome 6 Mooring Deployment, CTD Oshoro Maru OM98 Jul 18 to Aug 1 Seattle to Dutch Harbor 15 Ecology, biophysical control Alpha Helix HX213 Aug 15 to Sep 12 Seward to Dutch Harbor 29 Inner front Professor Kaganovsky PK-98-01 Sep 6 to Sep 18 Dutch Harbor to Dutch Harbor 13 Habitat survey, indices Sir Wilfrid Laurier LA-98-02 Sep 28 to Oct 9 Nome to Kodiak 11 Moorings, transects 1999 Thomas G. Thompson TGT99-01 Feb 4 to Feb 13 Dutch Harbor to Dutch Harbor 10 Moorings; NPZ samples, transects Miller Freeman MF99-04 Apr 10 to Apr 19 Kodiak to Dutch Harbor 10 Pollock eggs Miller Freeman MF99-05 Apr 21 to May 1 Dutch Harbor to Dutch Harbor 11 Moorings, NPZ samples, transects Miller Freeman MF99-06 May 1 to May 10 Dutch Harbor to Dutch Harbor 9 Hydrographic and NPZ transects Miller Freeman MF99-07 May 12 to May 20 Dutch Harbor to Dutch Harbor 9 Greenbelt water properties, drifters, midwater trawls Alpha Helix HX220 May 17 to Jun 18 Seward to Seward 33 CTDs, water samples Alpha Helix HX222 Jul 18 to Aug 22 Seward to Seward 36 CTDs, water samples Oshoro Maru OM99 Jul 20 to Aug 1 Juneau to Dutch Harbor 13 Ecology, biophysical control Miller Freeman MF99-11 Sep 2 to Sep 19 Kodiak to Dutch Harbor 18 Habitat survey Miller Freeman MF99-12 Sep 19 to Sep 29 Dutch Harbor to Dutch Harbor 11 Moorings, transects 2000 Miller Freeman MF00-01 Leg 3 Feb 17 to Feb 25 Dutch Harbor to Dutch Harbor 9 Transit; moorings; NPZ samples, transects; drifters Miller Freeman MF00-05 Apr 18 to Apr 30 Kodiak to Dutch Harbor 13 Moorings, NPZ samples, transects, drifters Miller Freeman MF00-06 May 1 to May 12 Dutch Harbor to Dutch Harbor 12 Hydrographic and NPZ transects, drifters Oshoro Maru OM00 Jul 21 to Aug 3 Victoria to Dutch Harbor 14 Ecology, biophysical control Ron Brown RB00-07 Leg 2 Sep 16 to Sep 23 Dutch Harbor to Dutch Harbor 8 Moorings, transects 2001 Miller Freeman MF-01-01, Leg 2 Feb 5 to Feb 10 Dutch Harbor to Dutch Harbor 5 Bering Sea moorings Miller Freeman MF-01-06 May 12 to May 14 to May 22 Dutch Harbor to Dutch Harbor to Dutch Harbor 11 Moorings and ecosystem monitoring Ron Brown RB-01-03, Leg 3 May 25 to Jun 8 Kodiak to Dutch Harbor 15 Mesoscale eddies, shelf-slope exchange Oshoro Maru OM01 Jul 14 to Jul 28 Seattle to Dutch Harbor 15 Age-0 pollock survey Sir Wilfrid Laurier LA-01-01 Oct 5 to Oct 9 to Oct 14 Nome to Dutch Harbor to Dutch Harbor 10 Moorings
172 S.A. Macklin and G.L. Hunt, Jr. (Eds.)

Acknowledgments

The editors appreciate the artistic and word processing skills of Karen Birchfield, Ryan L. Whitney and Tracey Nakamura in producing this report.

James E. Overland and Beth Turner contributed valuable comments to an

early version of the manuscript. NOAA Coastal Ocean Program provided

additional funds for this publication. We extend special thanks to the scientists, engineers, technicians and other personnel of the Southeast Bering

Sea Carrying Capacity project and the NSF Inner Front study, and to the

men and women of the many research vessels that supported this research.
The Southeast Bering Sea Ecosystem—Part 6 173

Part 6—References and SEBSCC Publications∗

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∗SEBSCC publication
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