NOAA Technical Memorandum NMFS-AFSC-19

Species-specific Stratification and

the Estimate of Groundfish Biomass

in the Eastern Bering Sea

by

Stephen E. Syrjala

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Alaska Fisheries Science Center

August 1993
NOAA Technical Memorandum NMFS

The National Marine Fisheries Service’s Alaska Fisheries Science Center uses

the NOAA Technical Memorandum series to issue informal scientific and

technical publications when complete formal review and editorial processing are

not appropriate or feasible. Documents within this series reflect sound

professional work and may be referenced in the formal scientific and technical

literature.

The NMFS-AFSC Technical Memorandum series of the Alaska Fisheries Science

Center continues the NMFS-F/NWC series established in 1970 by the Northwest

Fisheries Center. The new NMFS-NWFSC series will be used by the Northwest

Fisheries Science Center.

This document should be cited as follows:

Syrjala, S. E. 1993. Species-specific stratification and the estimate of

groundfish biomass in the Eastern Bering Sea. U.S. Dep. Commer.,

NOAA Tech. Memo. NMFS-AFSC-19,20 p.

Reference in this document to trade names does not imply endorsement by the

National Marine Fisheries Service; NOAA.
NOAA Technical Memorandum NMFS-AFSC-19

Species-specific Stratification and

the Estimate of Groundfish Biomass

in the Eastern Bering Sea

by

Stephen E. Syrjala

Resource Assessment and Conservation Engineering Division

Alaska Fisheries Science Center

7600 Sand Point Way N.E., BIN C-15700

Seattle, WA 98115-0070

U.S. DEPARTMENT OF COMMERCE

Ronald H. Brown, Secretary

National Oceanic and Atmospheric Administration

Diana Josephson, Acting Administrator

August 1993
This document is available to the public through:

National Technical Information Service

U.S. Department of Commerce

5285 Port Royal Road

Springfield, VA 22161
Notice to Users of this Document 

This document is being made available in .PDF format for the convenience of users; however, the 

accuracy and correctness of the document can only be certified as was presented in the original hard 

copy format. 

111

ABSTRACT

Species biomass estimates for eastern Bering Sea groundfish are calculated

using data collected in the Resource Assessment and Conservation Engineering

Division’s annual demersal trawl survey. These data are analyzed with a stratified

statistical sampling model. The survey is divided into 10 strata; a common

stratification scheme is used for all species. In this study, the use of speciesspecific stratification Schemes was examined to study how great a reduction in the

variance of the biomass estimator might be achievable. New stratification schemes

were developed by visually examining the distribution of catch per unit effort

(CPUE) data averaged over several years. Geographically contiguous strata were

drawn by grouping sampling stations with similar mean CPUEs. Reductions in the

variances were achieved, but their magnitude was not large enough to yield

biologically meaningful reductions in the sizes of the estimated confidence intervals

of the species biomass. The use of non-geographically contiguous strata was also

examined. Using this type of a stratification- scheme resulted in even smaller

reductions in the variance estimates.
INTRODUCTION

The Resource Assessment and Conservation Engineering (RACE) Division of

the Alaska Fisheries Science Center, National Marine Fisheries Service, annually

conducts a demersal groundfish trawl survey in the eastern Bering Sea. Data

collect&l in the survey are analyzed to estimate the total species-specific biomass in

the survey area, the variances of the biomass estimates, and confidence intervals for

the biomass estimates. A sampling model, with the same stratification scheme for

all species, is used to analyze the data (Armistead and Nichol, 1993). The

objective of this study was to see if a species-specific stratification, rather than a

single stratification scheme, could reduce the estimate of the variance of the

biomass estimate. 

The annual eastern Bering Sea groundfish survey consists of bottom trawl

samples collected at 329 stations located on a regular grid with 20 nautical mile

(nmi) intervals, both north-south and east-west (Armistead and Nichol, 1993). The

grid extends from Bristol Bay north to just north of Nunivak and St. Matthew

Islands and west to the 200 m depth contour. Twenty-six additional trawl stations

are located in an area around the Pribilof Islands and in a second area south of St.

Matthew Island. The effect of the additional trawls is to double the sampling

intensity in those areas.
2

Currently, the eastern Bering Sea survey area is divided into three areas

containing 10 strata (Fig. 1). The three areas are based on depth: roughly the

shoreline to 50 m, 50 - 100 m, and 100 - 200 m. These areas were selected based

on the cluster analyses of trawl data done by Walters and McPhail (1982) and

Walters (1983). Bakkala (1993) found that the eastern Bering Sea continental shelf

is divided into three distinct environmental domains and that the boundaries

between these domains are approximately at the 50 m and 100 m isobaths, lending

further support to the appropriateness of divisions.

Figure 1. Map of the survey area, the target locations of the trawl stations,

and the currently used stratification scheme. The heavy, solid lines show the

primary strata, the dashed lines mark the high-intensity sampling areas.
3

The three areas are subdivided into six primary strata by a line that runs

roughly southwest to northeast through the middle of the sampling area. This line

was originally established to divide the areas inhabited by separate stocks of fish

into different strata (Kashkina, 1965; Maida, 1972). It was believed (at present,

the assumption is debatable) that there were two separate stocks of walleye pollock

(Theragra chulcogramma) and two separate stocks of yellowfin sole (Pleuronectes

asper) in the eastern Bering Sea. The six primary strata were first used in the 1986

eastern Bering Sea survey (Halliday and Sassano, 1988); prior years’ data were

reanalyzed using this stratification scheme.

Overlaying the six primary strata is the two double-intensity sampling areas,

each of which is divided into two strata by the aforementioned stratum divisions.

The result is six low-intensity sampling strata (approximately one trawl station per

400 nmi2

 ) and four high-intensity sampling strata (approximately one trawl station

per 200 nmi2).

The biomass of each species is estimated by first calculating a mean catch

per unit effort (CPUE) for each stratum. The catch is measured in kilograms of

biomass; the effort is measured as the area trawled. The area trawled for a specific

trawl is measured as the wingtip to wingtip spread of the net times the length of the

trawl. Thus, multiplying the mean CPUE by the area of the stratum produces an
4

estimate of total species biomass for the stratum. A primary assumption of the

model is that 100% of the fish in the areas trawled are caught and that no fish from

outside the trawled area are caught.

Stratification is used in statistical sampling as a method to reduce the

variance of the statistical estimator and hence the size of confidence intervals.

Strata are defined so that observations within a stratum are similar and so that the

stratum means differ. In estimating biomass in the eastern Bering Sea, the ideal

strata are such that the CPUE is relatively constant across each stratum and so that

the mean CPUE varies from stratum to stratum. The objective is maintain an

unbiased estimate of the species biomass while reducing the variance of that

estimate.

Historically, the same stratification scheme has been used to estimate the

biomass of each species in the survey (Wilson and Armistead, 1991; Bakkala et al.,

1992; Armistead and Nichol, 1993). The species distributions across the eastern

Bering Sea, however, vary greatly from species to species. Thus, the optimal

stratification scheme will usually vary from species to species. The objective of the

current study was to examine the effect of species-specific stratification on the size

of the estimated variance. No change in the distribution of sampling effort has

been considered, only a change in the stratified model used to analyze the data.
5

METHODS

The data used in this analysis were taken from the eastern Bering Sea

demersal trawl surveys from 1982 to 1991. Survey data from earlier years were

available, but changes in the survey protocol indicated that those data should not be

included. The distributions of three commercially important species were

examined: walleye pollock, yellowfin sole, and rock sole (Pleuronectes bilineatus).

The standard method for analyzing the eastern Bering Sea demersal trawl

data uses the BIOMASS program at the Alaska Fisheries Science Center (AFSC) (see

Armistead and Nichol, 1993). For this analysis, a Microsoft QuickBASIC program

was written to emulate that portion of the BIOMASS program that calculates estimates

of the species biomass and the population numbers plus the associated variances and

confidence intervals. The program uses the standard haul file data, the standard

catch file data (with catch data from only one species per program run), and a

modified stratum/area file.

The variable of primary interest in this analysis was species biomass. It was

estimated by first calculating a mean CPUE for each stratum with effort measured

in units of area trawled. The biomass was estimated as the product of the mean
6

CPUE times the total area of the stratum. The stratum biomass estimates were then

summed, as were the variance estimates, to yield an estimate of the eastern Bering

Sea biomass of the species of interest and an estimate of the variance of the

biomass estimate.

-Five different geographically contiguous stratification schemes were

examined for the three species: 1) unstratified; 2) 2 strata, in which the survey

area was divided into areas of high- and low-intensity sampling; 3) 6 strata, using

the 6 primary strata shown in Figure 1; 4) all 10 strata shown in Figure 1; and

5) species-specific schemes (6 to 8 strata) based on an examination of patterns in

the mean CPUE. Three geographically non-contiguous stratification schemes were

also examined.

All 355 trawl stations of the annual eastern Bering Sea survey were included

in the analysis. Of these, 329 were located on the regular 20 nmi by 20 nmi grid.

The remaining 26 stations were located near the Pribilof Islands and south of St.

Matthew Island in two areas of higher intensity sampling (see Fig. 1). To maintain

a uniform sampling intensity across each stratum, the 26 high-intensity stations

were omitted from the analyses based on the first and third stratification schemes.
7

The species-specific stratification in the analysis using the fifth scheme was

based on historical CPUE data. The mean CPUE for each species at each trawl

station was calculated and then maps were drawn (one for each species) to

summarize the distribution of mean CPUEs. Mean CPUEs were calculated using

only the even years (1982, 1984, 1986, 1988, and 1990). This permitted analyses

of the odd-year sampling data that were independent of the data used to construct

the stratification schemes.

Strata were defined subjectively, based on an inspection of each map,

looking for contiguous regions with similar mean CPUE values. When appropriate,

the high- and low-intensity sampling areas were retained as separate strata. Thus,

data from all 355 stations could be used in the species-specific analyses. Maps of

the means of the even-year CPUEs and the selected stratifications by species are

shown in Figure 2.

To examine whether the species-specific stratification yields a significant

reduction in the estimated variance of the biomass estimate, the estimated variance

was calculated for the five stratification schemes (developed with data from the

even years) for each of the five odd years (1983, 1985, 1987, 1989, and 1991).

The effect of the species-specific stratification was analyzed with a two-way

analysis of variance model (with stratification scheme and year as the main effects).
8

Figure 2. Species-specific stratification scheme; the numbers on the map are the

means of the even-year CPUEs at the stations.
9

As there was no replication possible, a model without an interaction term was used.

Fisher’s least-significant difference test was used to specifically test for a

statistically significant difference between the various species-specific schemes and

the conventional lo-stratum approach.

In some ways, using post-stratification would seem to be the best way to

develop an optimal stratification scheme. But in the context of estimating the

biomass of a species in the eastern Bering Sea survey area, using the observed

CPUE figures for a particular year to define the strata used to analyze the data for

that year does not fit within the framework of the usual post-stratification model

(Kish, 1965). To post-stratify in this situation is likely to introduce a negative bias

in the estimate of the variance of the biomass estimate (i.e., the variance will likely

be underestimated).

While using the current year’s data to define strata is inappropriate, there is

nothing wrong with using the previous year’s data to define strata. Thus, another

approach to defining non-geographically contiguous strata that was examined was to

sort the trawl stations by the previous year’s CPUE data and then assign the stations

to strata based on the sorted order. Three-year and five-year moving averages of

the station CPUE data were also used to sort the stations and then to define strata.
10

For example, to construct strata to analyze the 1991 data, the stations were

ranked by the 1990 CPUE data. Regardless of their geographic locations, stations

with the largest 1990 CPUE values were defined to be in the first stratum. Stations

with somewhat smaller 1990 CPUE values were defined to be in the second

stratum, etc. Stations with the smallest 1990 CPUE values were defined to be in

the last stratum. No fixed rule was used to define the CPUE values used to

separate one stratum from the next. The objective was not to develop an optimal

stratification rule. Rather it was to simply look at the general effect on variance

estimates of using such a stratification scheme. Somewhat arbitrary stratum

separation values were selected in an attempt to keep the strata small when there

was large within-stratum CPUE variability while allowing for larger strata when

there was very little CPUE variability.

Additional analyses of the 1991 data were conducted by calculating the

average CPUE for 1988 to 1990 and the average CPUE for 1986 to 1990 and then

stratifying the stations by using the ranked average CPUEs. The 3-year and 5-year

averages were selected in an attempt to reduce the effect of annual within-station

CPUE variability on the process of defining homogeneous (with respect to CPUE)

strata.
11

RESULTS

The geographic pattern of the means of the even-year CPUEs in the eastern

Bering Sea varied by species. The number of strata into which the survey area was

divided also varied from species to species. For walleye pollock, the area was

divided into eight strata; for yellowfin sole, the area was divided into seven strata;

and for rock sole, the area was divided into six strata.

For all three species, the estimated variance of the biomass estimate

decreased as the number of strata into which the survey area was divided increased.

However, the statistical significance of the differences in estimated variance among

the stratification schemes varied from species to species. Of particular interest was

a test of the null hypothesis H0

: the variance of the biomass estimate using speciesspecific stratification is equal to the variance obtained with the presently used

I0-stratum scheme against the one-sided alternative hypothesis Ha: the variance of

the biomass estimate using species-specific stratification is less than the variance

obtained with the presently used I0-stratum scheme.

For walleye pollock, the observed decrease in the variance of the biomass

estimate (when going from the currently used lo-stratum stratification to the
12

species-specific stratification) was very highly significant (P = 0.001). In contrast,

for the other two species, the differences are not statistically significant (for

yellowfin sole, P = 0.3 and for rock sole, P = 0.17).

For walleye pollock, the two-stratum and six-stratum models both resulted in

a 4% reduction in the predicted standard deviation when compared to the standard

deviation from the unstratified model. The l0-stratum model resulted in a 7%

reduction in the standard deviation and the contiguous, species-specific stratification

in

a 13% reduction - 7% less than the predicted l0-stratum model standard

deviation (Fig. 3). Overall, the species-specific stratification resulted in a predicted

variance that was significantly smaller (P = 0.05) than the variance predicted using

the other stratifications. Variance estimates from the unstratified model were

significantly larger than the variance estimates from any other stratified model.

The differences among the variances from the 2-, 6-, and lo-stratum schemes-and

the species-specific stratification were not statistically significant.

For yellowfin sole, the same reductions in the predicted standard deviations

relative to those from the unstratified model were 3 %, 22%) 24 % , and 27 % - with

a 4% reduction in the species-specific model standard deviation when compared to

the lo-stratum results (Fig. 3). The difference between the variance of the
13

Stratification Scheme

Figure 3. Mean predicted standard

deviation (in metric tons) as a function of

the stratification scheme.

estimated biomass from the

unstratified model and the twostratum model was not significant.

Likewise, the differences among the

variances from the 6-stratum, the

l0-stratum, and the species-specific

stratification models were not

significant. But the difference in

variances between the unstratified or

2-stratum model and any one of the

6-stratum, the l0-stratum, or the

species-specific stratification models

was, however, statistically

significant.

For rock sole the 2-, 6-, and

l0-stratum models and the speciesspecific stratification model resulted

in reductions of 6%, 14%, 24%, and

29%) respectively, in the standard

deviations, relative to the predicted
14

standard deviation in the unstratified model (Fig. 3). The species-specific

stratification standard deviation was 5% less than that from the l0-stratum

stratification. The differences in the variances between the unstratified and the

2-stratum models, between the 2- and 6-stratum models, between the 6- and

l0-stratum models, and between the N-stratum and the species-specific

stratification models were not statistically significant but the differences in variances

between any other pair of models were significant.

While the decreases in the estimated variances of the biomass estimates were

as one would expect and some of the observed reductions were statistically

significant, the magnitude of the reductions was not large enough to be biologically

meaningful. For all three species, the reduction in the standard deviation when

going from the in-common lo-stratum stratification (the stratification scheme

presently used to analyze the eastern Bering Sea demersal trawl data for all species)

to the species-specific stratification scheme was between only 4 and 7 % .

For the non-geographically contiguous stratification schemes, only the

walleye pollock data were analyzed.

When just the previous year’s CPUE data were used to stratify the survey

area, the estimated variance of the biomass estimate was smaller than that from an
15

unstratified analysis but larger than that from the presently used l0-stratum

analysis. If the previous 3 years’ CPUE data were used to stratify, the estimated

variance was reduced to less than that from the l0-stratum analysis and the variance

estimate was even further reduced if the previous 5 years of CPUE data were used

to stratify the sampling stations.

The smallest estimated variance, however, was obtained by using the means

of the even-year CPUE data to stratify the survey area into geographically

contiguous strata. Statistically, the variance based on the stratification using the

CPUE means for the even-year data is significantly smaller than all of the variances

based on the non-contiguous stratification schemes (except for the difference

between the variances from the previous 5-year means and the even-year means

models where the difference is nearly, but not quite; significant at the 0.05 level).

None of those latter variances was significantly different from the variance based on

the presently used lo-stratum scheme and all were significantly smaller than the

variance obtained from an unstratified analysis.
16

DISCUSSION

Eastern Bering Sea demersal trawl data from 1982, 1984, 1986, 1988, and

1990 were used to develop species-specific stratification schemes for use with

sampling theory models to estimate the species biomass. The expectation was that

new stratification schemes would reduce the estimates of the variances for the

biomass estimates. After the strata were defined, data from 1983, 1985, 1987,

1989, and 1991 were analyzed to produce estimates of the biomass and the variance

of the biomass estimate.

As anticipated, the species-specific stratification models yielded smaller

estimated variances than did the more general lo-stratum sampling scheme

currently in use. But the magnitude of the reductions in the estimated variance was

so small - a 4 to 7% reduction in the standard deviation - that it does not seem to

be worth the effort to modify the current stratification scheme to achieve the

potential improvement.

In an attempt to assess the adequacy of the stratification based on the means

of the even-year CPUE data, two tasks were carried out. First, maps were drawn

using the means of the odd-year CPUE data. Those maps and the stratification that
17

they suggest were not substantially different from the maps drawn using the means

of the even-year CPUE data.

Second, using the stratification schemes based on the means of the even-year

CPUE data, the estimated variances of the biomass estimates for the even years

were calculated and analyzed. Since the stratification was based on the data being

analyzed, the strata should be in some sense optimal. Qualitatively, the results

were very similar to the results obtained by analyzing the odd-year data using the

same stratification schemes but, surprisingly, the standard deviations calculated in

the even-year data analyses were larger than the standard deviations achieved in the

odd-year analyses. No plausible explanation for this result was apparent other than

simple random variation. It may be that simply by chance there was greater CPUE

homogeneity, within the strata, in the odd years.

These two analyses suggest that the stratification schemes based on the evenyear data are reasonable for the distribution of CPUE data in the odd years. While

there was a change in the distribution of catch from year to year, as well as a

change in the total catch, there does not appear to have been a substantial change in

the overall relative distribution of the three species of fish examined. The relatively

small reductions in the estimated standard deviations seem to be a result of a

somewhat patchy distribution of the fish. Patterns emerge when one looks at the
18

distribution of mean CPUE, but within any contiguous area there remains

substantial variation in the density of the fish stocks so that the variance in the

estimates of biomass remains large.

The currently used lo-stratum scheme does relatively well, possibly because

while it is largely based on changes in sea depth rather than fish distribution, the

distributions of the species examined are also affected to a large extent by depth.

Thus, there are some distinct similarities between the existing stratification and the

species-specific stratifications.

The improved performance obtained using the geographically contiguous

stratification schemes and the lack of improvement using the non-geographically

contiguous schemes based on data from the catch in the previous year (or years)

indicate that there is some year-to-year continuity in the areas where fish densities

are high. The localized concentrations of fish are not, however, stable at any

specific trawl station. This observation suggests that if further studies are

undertaken to reduce the variance in the biomass estimate through the use of

improved stratification, then those studies’ should focus on geographically

contiguous strata.
19

CITATIONS

Armistead, C. E. and D. G. Nichol. 1993. 1990 Bottom trawl survey of the

eastern Bering Sea continental shelf. U.S. Dep. Commer., NOAA Tech.

Memo. NMFS-AFSC-7, 190 p.

Bakkala,. R. G. 1993. Structure and historical changes in the groundfish complex

of the eastern Bering Sea. U.S. Dep. Commer., NOAA Tech. Rpt.

NMFS 114, 91 p.

Bakkala, R. G., W. A. Karp, G. F. Walters, T. Sasaki, M. T. Wilson, T. M.

Sample, A. M. Shimada, D. Adams, and C. E. Armistead. 1992.

Distribution, abundance, and biological characteristics of groundfish in the

eastern Bering Sea based on results of U.S.-Japan bottom trawl and midwater

surveys during June-September 1988. U.S. Dep. Commer., NOAA Tech.

Memo. NMFS F/NWC-213, 362 p.

Halliday, K. L. and J. A. Sassano. 1988. Data report: 1986 Bottom trawl survey

of the eastern Bering Sea continental shelf. U.S. Dep. Commer., NOAA

Tech. Memo. NMFS F/NWC-147, 159 p.

Kashkina, A. A. 1965. Reproduction of yellowfin sole (Limanda aspera (Pallas))

and changes in its spawning stocks in the eastern Bering Sea. Tr. Vses.

Nauchno-issled. Inst. Morsk. Rybn. Khoz. Okeanogr. 58 (Izv. Tikhookean.

Nauchno-issled. Inst. Rbn. Khoz. Okeanogr. 53):191-199. In Russian.

(Transl. by-Isr. Program Sci. Transl., 1968, p. 182-190, in P.A. Moiseev

(editor), Soviet fisheries investigations in the northeastern Pacific, Part IV,

avail. U.S. Dep. Commer., Natl. Tesh. Inf. Serv., Springfield, VA, as

TT67-51206.)

Kish, L. 1965. Survey Sampling. John Wiley & Sons, New York.

Maida, T. 1972. Fishing grounds of the Alaska pollock. (In Japanese, English

summary.) Bull. Jap. Soc. Sci. Fish. 43:39-45.
20

Walters, G. E. 1983. An atlas of demersal fish and invertebrate community

structure in the eastern Bering Sea: Part 2, 1971-77. U.S. Dep. Commer.,

NOAA Tech. Memo. NMFS F/NWC-40, 152 p.

Walters, G. E. and M. J. McPhail. 1982. An atlas of demersal fish and

invertebrate community structure in the eastern Bering Sea: Part 1, 1978-81.

U.S. Dep. Commer., NOAA Tech. Memo. NMFS F/NWC-35, 122 p.

Wilson, M. T. and C. E. Armistead. 1991. 1989 Bottom trawl survey of the

eastern Bering Sea continental shelf. U.S. Dep. Commer., NOAA Tech.

Memo. NMFS F/NWC-212, 212 p.
RECENT TECHNICAL MEMORANDUMS

Copies of this and other NOAA Technical Memorandums are available from the

National Technical Information Service, 5285 Port Royal Road, Springfield, VA 22167.

Paper copies vary in price. Microfiche copies cost $3.50.

18

17

16

15

14

13

12

11

10

9

8

7

6

5

PELLA, J., M. HOFFMAN, S. HOFFMAN, M. MASUDA, S. NELSON, and L. TALLEY. 1993. Adult

sockeye and pink salmon tagging experiments for separating stocks in northern British Columbia

and southern Southeast Alaska, 1982-1985, 134 p. NTIS number pending.

SEASE, J. L., J. P. LEWIS, D. C. MCALLISTER, R. L. MERRICK, and S. M. MELLO. 1993. Aerial

and ship-based surveys of Steller sea lions (Eumetopias jubatus) in Southeast Alaska, the Gulf of

Alaska, and Aleutian Islands during June and July 1992, 57 p. NTIS number pending.

FISCUS, C. F. 1993. Catalogue of cephalopods at the National Marine Mammal Laboratory, 183

p. NTIS number pending.

KINOSHITA, R. K., A. GREIG, L. E. QUEIROLO, and J. M. TERRY. 1993. Economic status of

the groundfish fisheries off Alaska, 1991, 94 p. NTIS No. PB93-197861.

PEREZ, M. A., and W.B. MCALISTER. 1993. Estimates of food consumption by marine mammals

in the Eastern Bering Sea, 36 p. NTIS No. PB93-191195.

BERGER, J. D. 1993. Comparisons between observed and reported catches of retained and

discarded groundfish in the Bering Sea and the Gulf of Alaska, 89 p. NTIS No. PB93-184711.

HARRISON, R.C. 1993. Data report: 1991 bottom trawl survey of the Aleutian Islands area,144 p.

NTIS No. PB93-186237.

LIVINGSTON, P. A., A. WARD, G. M. LANG, and M-S. YANG. 1993. Groundfish food habits and

predation on commercially important prey species in the eastern Bering Sea from 1987 to 1989,

192 p. NTIS No. PB93-184703.

KINOSHITA, R. K., and J. M. TERRY. 1993. Oregon, Washington, and Alaska exports of edible

fishery products, 1991, 47 p. NTIS No. PB93-159101.

KARINEN, J. F., M. M. BABCOCK, D. W. BROWN, W. D. MACLEOD, JR., L. S. RAMOS, and J.W.

SHORT. 1993. Hydrocarbons in intertidal sediments and mussels from Prince William Sound,

Alaska, 1977-1980: Characterization and probable sources, 69 p. NTIS No. PB93-159093.

WING, B. L. 1993. Winter oceanographic conditions in the eastern Gulf of Alaska, JanuaryFebruary 1986, 53 p. NTIS No. PB93-158335.

ARMISTEAD, C. E., and D. G. NICHOL. 1993. 1990 bottom trawl survey of the eastern Bering

Sea and continental shelf, 190 p. NTIS No. PB93-156677.

WOLOTIRA, R. J., JR., T. M. SAMPLE, S. F. NOEL, and C. R. ITEN. 1993. Geographic and

bathymetric distributions for many commercially important fishes and shellfishes off the West Coast

of North America, based on research survey and commercial catch data, 1912-84, 184 p. NTIS

No. PB93-167682.

GUTTORMSEN, M., R. NARITA, J. GHARRETT, G. TROMBLE, and J. BERGER. 1992. Summary

of observer sampling of domestic groundfish fisheries in the northeast Pacific Ocean and Eastern

Bering Sea, 1990, 281 p. NTIS No. PB93-159085.