NOAA Technical Memorandum NMFS-AFSC-56

An Evaluation of North Pacific

Groundfish Observer Program.

Methods of Haul Weight Estimation

by

M. W. Dorn, S. M. Fitzgerald, M. A. Guttormsen, and M. R. Loefflad

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Alaska Fisheries Science Center

August 1995
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:

Dorn, M.W., S. M. Fitzgerald, M. A. Guttormsen, and M. R. Loefflad.

1995. An evaluation of North Pacific groundfish observer program

methods of haul weight estimation. U.S. Dep. Commer., NOAA Tech.

Memo. NMFS-AFSC-56,31 p.

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

the National Marine Fisheries Service, NOAA.
NOAA Technical Memorandum NMFS-AFSC-56

An Evaluation of North Pacific

Groundfish Observer Program

Methods of Haul Weight Estimation

by

M. W. Dorn, S. M. Fitzgerald, M. A. Guttormsen, and M. R. Loefflad

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

D. James Baker, Under Secretary and Administrator

National Marine Fisheries Service

Rolland A. Schinitten, Assistant Administrator for Fisheries

August 1995
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INTRODUCTION

The National Marine Fisheries Service (NMFS) North Pacific

Groundfish Observer Program places observers on commercial

fishing vessels to collect data essential to the management and

assessment of the North Pacific groundfish fisheries. Key

objectives of their work are to estimate haul weight and catch

composition on each vessel that they monitor. These data are

incorporated into NMFS estimates of the total annual removals of

fish, shellfish, and other species, and into weekly harvest

estimates generated for in-season fishery management

NMFS estimates of total annual removals

fishery harvests for offshore-processors are

observer data and industry production data.

and in-season weekly

based on a blend of

Each of the two data

sources provides a weekly estimate of catch by species for each

processor: observers provide data from which overall total catch

and speciescomposition estimates are derived; processors provide

data on the production of fish products which are converted to

round weight by NMFS, with processor-estimated discards added.

Each week, the data from all offshore-processors are blended in a

manner which selects between the observer data and the production

data on a processor-by-processor basis. The selected data source

is used by NMFS to compile weekly catch estimates. Compiled over

the year, these blend estimates become the annual record of

catch. The blend model is weighted toward selection of the

observer data when it is available. Thus, weekly and annual

catch estimates are dependent on observer data and the derivation

of that data warrants examination.
2

Haul weight estimates obtained by observers are essential to

the overall estimation of catch by species on trawl vessels.

Catch composition sampling provides estimates of the proportion

of each species in a haul. The proportion by species is

multiplied by the haul weight to estimate catch (i.e., weight) by

species. Bias in haul weight estimation will also bias the

estimates of catch by species. For example, if the haul weight

estimate is l0% too high, then the estimate of catch by species

might also be 10% too high.

Estimates of haul weight by observers currently depend upon

two components: independent observer catch estimates for observed

hauls and vessel catch estimates for unobserved hauls. Observers

on catcher-processor trawl vessels typically estimate the weight

of between 50% and 70% of the hauls made while aboard vessels.

For unobserved hauls, the observer is dependent on vessel

personnel to provide estimates of catch weights. Observer and

vessel catch weight estimates can be biased. Both methods

deserve examination to determine if resultant estimates are

unbiased.

The accuracy of vessel personnel's haul weight estimates is 

difficult to evaluate. Prior to 1995, vessel-dependent data were

recorded such that they can not be readily segregated from the

independent observer estimates. This issue will best be

addressed when data from the 1995 season becomes available. At

that time it will be possible to directly compare ship's haul

weight estimates with observer's estimates.
3

The independent observer estimates are more readily examined

because observer data are collected systematically using

prescribed methods. In training, observers are taught two

methods of estimating haul weight on trawlers--codend and bin

volumetric estimates. Each method estimates the volume of fish

and applies a density factor to that volume to calculate the

weight.

Using the codend method, observers measure the dimensions of

a codend after it is brought onboard to determine the volume of

fish in the net. Typically, this is done using pre-determined

marks on the trawl deck, a measuring stick, and a tape measure.

Once measurements are obtained, the observer applies a formula

for the volumetric solid which best approximates the codend

shape. Formulas are available for rectangular, ellipsoidal,

semi-ellipsoidal, and cylindrical solids. Depending upon the

fullness of the codend, position of straps and bands, packing of

the fish, and the shape of fish at the fore end, observers must

make some judgments regarding the average height, width, and

length of the portion of the net containing fish. Large codends

will often be greater than 2 m in height or width, creating some

difficulties in measuring these parameters. On many vessels the

codends are immediately dumped into fish bins, and the time

available to measure a codend may be insufficient.

On some vessels, observers can use fish bins (bin volume

method) to estimate catch weights. These bins hold fish after

they are dumped from the codend, but before processing occurs.

Typically, observers measure the bins prior to the start of
4 .

fishing and use these measurements throughout the cruise.

Vessels which participate in the Community Development Quota

Program have markings certified by engineers on their bins. Bin

heights may be marked on the sides where an observer can view

inside to determine the level of fish, or observers use reference

points such as bin boards. Despite these markings, the observer

may need to judge the average height of fish, as the catch

typically is at an uneven level in the bins. The observer

estimates the average level and uses calculated bin volume or the

volume from bins which have been certified to determine the

volume of the catch.

The fish density applied to volumetric estimates of catch is

usually estimated in situ by weighing a number of sampling

baskets, or other containers, of unsorted catch and using the

volume of the measuring container to calculate the density. The

volume of a full sampling basket is relatively small at 0.055 m3.

In contrast, the volume of a bin or codend is far greater and can

exceed 100 m3. Densities are calculated regularly and applied to

sampled hauls with a similar species mix. Currently, a NMFSprescribed density of 0.93 t/m3

 (t = metric ton) is applied to

hauls that are greater than 95% walleye pollock (Theragra

chalcouramma). This prescribed density was determined using

historical Observer Program data and represents an average of

many in situ density measurements calculated by observers using

standard observer sampling fish baskets.

This paper describes fieldwork conducted aboard the

FT Alaska Ocean designed to evaluate volumetric haul estimation
5

procedures used by observers. The Alaska Ocean was suited to

this type of work as it is outfitted with a Marel flow scale

system. Using this scale, observer estimates using the two

volumetric methods could be compared against a scale-derived

value for the haul weight. The objective was to evaluate the

bias and precision of the volumetric methods of haul weight

estimation. Following sections of this paper describe the

sampling procedures used, present and discuss the results, and

provide recommendations for additional fieldwork and potential

changes in methods that would improve observer haul weight

estimates.

METHODS

The project was carried out on the FT Alaska Ocean, a 115 m

(376 ft) factory trawler owned and operated by Alaska Ocean

Seafoods, Incorporated. The Alaska Ocean has three 95 metric ton

(t) capacity fish bins and uses a net with a codend capacity of

approximately 200 t. The vessel was chosen as a platform for

this work because a motion-compensating in-line flow scale (Marel

Flow Scale type M2000-B0l, P1900/450) is used to weigh the

retained catch as fish are moved from the fish bins to the

processing area via conveyer belts.

Normal operating procedures on the Alaska Ocean made it

possible to routinely obtain codend estimates- for the entire haul

and bin volume estimates and scale weights for individual bins.

A large codend (>l00 t) would be dumped into two bins, but fish
6

from separate hauls were not mixed in the bins. Fish were always

processed one bin at a time, so Marel flow scale readings could

be obtained for individual bins, and bin totals could be summed

to represent a particular haul.

In the aft section of the Alaska Ocean, adjustable openings

at the base of the bins allow fish to flow onto conveyer belts

which carry the fish from starboard to port. The fish next

travel up an incline, forward 2 m, and then back across the

vessel from the port to starboard sides. There are three discard

stations along this port to starboard portion where crew members

remove bycatch (all non-target species and undersized or damaged

fish of the target species). At each discard station, bycatch is

dropped onto a discard belt that runs below the primary conveyer

to a discard chute. The retained catch (i.e., catch that is not

discarded) is carried via conveyer across the Marel flow scale

and on into the processing factory.

Because sorting takes place before fish cross the Marel flow

scale, discards for each sampled codend or bin were weighed by an

observer. For this purpose, Alaska Fisheries Science Center

(AFSC) staff acting as second observers were deployed on the

vessel, for the 1994 "B" season pollock fishery (16 August to 21

September) and during the October 1994 Pacific whiting

(Merluccius productus) fishery (1 October to 5 October). The

primary observer followed the normal observer sampling regime,

while the second observer was dedicated to this special project

and collected and weighed all the discards for the sampled hauls

using a 100 kg platform scale. The discard weights (typically
7

less than 2% of the total catch) obtained using the platform

scale were added to the Marel flow scale weights to obtain a

total weight estimate for a particular bin, henceforth referred

to as the scale weight. The scale weight from each bin could

then be compared with a bin volume density-based weight estimate.

The comparison of codend volume estimates to scale-derived

weights for larger hauls (greater than one bin) required weighing

discards from more than one bin. This type of comparison was

difficult to achieve regularly. Some hauls selected for this

project were abandoned because the bycatch rate was too high for

the second observer to weigh all the discards.

Scale Weights

The Marel scale is a motion-compensating type in-line scale.

The unit integrates a weighing assembly (load cells and weighing

plate) with measurement of the belt travel speed to record a

cumulative weight of product. The weighing operation was

designed to compensate for vessel motion using two load cells:

the first is mounted beneath the weighing plate and senses the

weight of material passing across it on the belt while the second

load cell is fitted with a known weight. The M2000 Indicator

component of the flow scale reads the signal from each load cell

and uses the difference between apparent weight and known weight

on the second cell to calculate a correct weight of material for

the first cell.

The Marel scale was calibrated regularly with a 10 kg test

weight and was further checked using known weights of fish, as
determined on the platform scale. This test usually consisted of

weighing a basket of approximately 50 kg of fish, and placing

them on or sending them across the Marel flow scale. Platform

and flow scale readings differed by less than 5% and were usually

within 2%. Early in the study a larger quantity (560 kg) of fish

were weighed using the platform scale and then sent across the

flow scale. Scale readings were within 5 kg (0.89%). Thus, no

substantial differences were found between the Marel scale weight

and those obtained using the platform scale.

Determining the accuracy and precision of the Marel flow

scale was beyond the scope of this project. Comprehensive work

on scale performance under actual commercial (at-sea) conditions

has not been fully explored, and the Marel motion-compensating

flow scale has not been subjected to scale certification tests or

procedures. However, for the purpose of this study, we treat the

scale-derived weights as a measure of actual catch weights so

that we can contrast the two volumetric-based methods.

Volumetric Estimates

We wanted to compare the two volumetric methods against a

"known" weight to 1) delineate the accuracy and precision of each

method, and 2) examine each method for indications of potential

bias. The methods employed to determine volumetric estimates of

catch for this study followed those of the Observer Program, as

described earlier and in a Observer Program field manual (Teig et

al. 1994). With both methods, observers first determine the
9

volume of the catch, and then apply a density factor to this

volume.

The fish bins on the Alaska Ocean were suitable for

volumetric catch estimates. The bins were roughly rectangular,

with some floor irregularity, and heights were marked clearly

along the sides 'and back. The height of fish could be seen

easily through either a viewing portal or by raising bin boards.

The first AFSC staff member on the vessel measured the bin

dimensions and produced a table that converted the depth of fish

in the bin to a volume estimate. This table was used by all

subsequent observers. Bin measurements were checked against ship

drawings, measurements made by earlier observers, and

measurements of the bins made when the vessel returned to port.

Observers could feasibly weigh only a limited amount. of

discard (bycatch) using the platform scale. Therefore, only

hauls of greater than 95% pollock were sampled for this project,

to which the NMFS-prescribed 0.93 t/m3 density was applied.

Observers-did not calculate in situ densities during the pollock

fishery. No in situ pollock density estimates based on basket

weight were calculated during this project. During the whiting

fishery, density was calculated in situ using fish in the

standard 0.055 m3 sampling baskets.
10

Analytical Approach

Volumetric-derived catch weight estimates depend upon two

components: calculated volume and fish density. Therefore, in

analyzing these data, questions about the bias of the volumetric

component can not be addressed separately from questions about

the appropriate density for fish in the bins or in a codend. For

this reason, our analysis examined the relationship between the

volume estimate (in m3) obtained with standard observer-methods

and the scale weights (Marel flow scale + discard). A linear

regression model was used to explore the nature of that

relationship. For each type of estimate (pollock bin, pollock

codend, whiting bin, and whiting codend), we tested whether the

relationship between volume and weight had a zero y-intercept,

and whether there was any curvature in the relationship. The

presence of either a non-zero y-intercept or curvature in the

relationship between volume and weight would indicate that the

density of fish changes with the weight of fish in the codend or

bin. This would also suggest that the use of a single density

value to convert volume to weight across a range of volumes is

inappropriate.
11

An estimate of the appropriate codend or bin in situ density

(t/m3) is the slope of a zero-intercept linear regression (Neter

et al. 1985),

RESULTS

During the 1994 walleye pollock "B" season, observers

collected 13 codend and 35 bin estimates that could be compared

with scale weights (Marel scale weights + weighed discard) (Table

1) In the Pacific whiting fishery, 6 codend and 12 bin

estimates were made (-Table 2). Codend volumes used in this study

ranged between 22.4 and 218.3 m3

, single bin volumes ranged

between 17.6 and 106.0 m3

. Deviations of the volumetric

estimated pollock catch weights (using the prescribed density)

from the scale weights ranged from 39.59 t to -22.78 t for

codends and from 10.32 t to -10.64 t for bins, with an apparent

bias among bin volumetric estimates (Fig. 1).

The pollock volume and weight data were collected by two

different AFSC staff members acting as secondary observers. The
12

first observer was on the Alaska Ocean from 16 August to 3

September, the second observer from 6 to 23 September. The data

was first tested for between-observer differences. Bin weights

were regressed on bin volumes with the observer as covariate with

two factor levels. A test for differences in y-intercepts

between observers was not significant (P = 0.40). A test for

differences in slopes was also not significant (P = 0.43).

Codend weights were regressed on the codend volumes and the same

statistical tests were conducted. Again, neither the test for

differences in y-intercepts between observers (P = 0.17) nor the

tests for differences in slopes (P = 0.15) were significant.

Based on these results, we combined the data for the two

observers in all subsequent analyses. Since both observers used

the same bin and codend measurements to determine volume, the

high level of agreement is not surprising. A potentially

important source of between-observer differences is error in the

measurement of bin or codend dimensions. This would bias all of

the observer's haul weight estimates. However, these results

suggest that observer volume estimates are consistent; different,

observers would independently produce. similar weight estimates.

A second series of statistical tests was used to examine

whether the relationship between volume and weight is linear and

if it passes through the origin. A single density value might

not be appropriate to convert a volumetric estimate to a haul.

weight over a wide range of volumes. For example, a large haul

in a codend or a bin might compress the fish, increasing the in

situ density. Two tests were performed for each data set: a

test for a zero y-intercept and a test for a quadratic term in
13

the regression. The purpose of the first test was to determine

whether the weight estimate would go to zero as the volume goes

to zero. The purpose of the second test was to determine whether

there is any curvature in the relationship between volume and

weight. For the pollock bin volume data set, the test for a nonzero y-intercept was significant (P = 0.04), but an additional

test for a quadratic term was not significant (P = 0.21). For

the pollock codend data set, the test for a non-zero y-intercept

again was significant (P = 0.01) and the test for an additional

quadratic term was not (P = 0.15). These results indicate that

the apparent density of pollock declines linearly with increasing

haul size (Fig. 2). This result is opposite to the deviation

from a constant density relationship that would occur if fish

were being compressed in a large haul, where the apparent density

would increase with haul size. The possible explanations for

this are 1) the observers tended to underestimate the volume of

small hauls relative to large hauls or 2) changes in density

between large and small hauls. The estimated y-intercept was

larger for the codend volume estimates: therefore, this tendency

was greater for codends than it was for bins (Fig. 3). However,

the overall effect of this tendency was small.

The volume and weight data collected during the whiting

fishery showed a similar pattern to the pollock data. For the

whiting bin weight-volume regression, the y-intercept was

significant (P = 0.011, but an additional quadratic term was not

significant (P = 0.34) (Fig. 4). For the whiting codend data

set, neither the y-intercept nor quadratic coefficient in the

regression of haul weight on volume were significant (y-intercept
14

P = 0.30, quadratic P = 0.23) . The whiting codend sample size (n

= 6) was too small to adequately test for a significant departure

from a zero y-intercept linear relationship. The direction of

departure was the same as with the pollock codend data sets,

where smaller hauls had a higher apparent density than the larger

hauls (Fig. 5).

The zero y-intercept linear regression estimates of fish

density (Table 3) show some differences from the nominal values

of fish density obtained from basket sampling. For pollock, a ttest (Weisberg 1985) indicated that the in situ bin density

estimate of 1.02 t/m3 was significantly larger than the NMFSprescribed value of 0.93 t/m3

 (P < 0.001). However, the codend

in situ density estimate of 0.93 t/m3 was not significantly

different than the nominal value (P = 0.945). The lower

estimated in situ density for codends probably does not indicate

that fish are packed less tightly in codends than in fish bins,

though this is one possible interpretation. -A more likely

explanation is that codend volumes are consistently

overestimated, producing a lower apparent density.

For Pacific whiting, a standard NMFS fish density was not

used, and observers estimated fish density daily with basket

samples. An average of fish densities from four vessels fishing

in the same area as the Alaska Ocean was 0.90 t/m3. The in situ

bin density estimate of 0.89 t/m3 was not significantly different

than the basket sample estimate (P = 0.801). The in situ codend

density of 0.91 t/m3 also was not significantly different than

the basket sample estimate (P = 0.727).
Although the data showed statistically significant

departures from a constant density, the percent difference is

fairly small over the range of haul weights typically observed

(Fig. 2). For the bin volume data, the difference was 4% for bin

weights between 40 and 60 t, and less than 1% for bin weights in

the 80-110 t range. For the pollock codend data the departure

from a constant density was larger. The difference was 22% for

haul weights between 40 and 60 t, and 2% for haul weights between

175 and 200 t (Fig. 3). For the whiting bin volume data, the

difference is 16% for bin weights in the 20-30 t range and less

than 1% for bin weights in the 40-60 t range (Fig. 4). There

were too few whiting codend estimates (n = 6) to make a

meaningful comparison between the constant density estimates and

the linear regression predictions (Fig. 5).

An additional aspect to be considered when comparing the bin

volume estimates with the codend estimates is their relative

precision. The estimated weights for the bin volume method

tended to have smaller deviations than the codend method (Figs.

2-5). An F-test (Rosner 1982) for the ratio of mean square

errors (MSES) for the bin volume and codend constant density

regressions was highly significant for both the pollock and the

whiting data (pollock: P < 0.001, whiting: P= 0.025). This is

a strong indication that the bin volume method is more precise

than the codend method.

Using the constant density estimate for pollock and a

pollock bin volume of 50 m3 the 80% confidence interval for a

new haul weight is plus or minus 12% of the predicted haul
16

weight. For a codend volume of 50 m3, the 80% confidence

interval is plus or minus 42% of the predicted value. For a

pollock bin volume of 100 m3, an 80% confidence interval to

predict a new haul weight is plus or minus 6%, while for a codend

volume of 100 m3, the 80% confidence interval is plus or minus

21%. With these 80% confidence intervals, we would be 80% sure

that the true haul weight is between 96 and 109 t for a bin

volume of 100 m3, while for a codend volume of 100 m3

, we would

be 80% sure that true haul weight is between 73 and 113 t. An

80% confidence interval is 'reported for haul weight because in

our judgment this is an appropriate level of accuracy to compare

different methods of monitoring catches on fishing vessels at

sea. The 95% confidence interval used in scientific work is too

high a standard of accuracy.

DISCUSSION

Potential biases of varying magnitude exist for both the bin

and codend volumetric methods used by observers for haul weight

estimation. Two significant problems are apparent. First, both

methods showed a decrease in apparent fish density as the haul

size increased. This phenomenon would occur if the volumes of

small hauls were consistently underestimated, or if the volumes

of larger hauls were consistently overestimated. It is not

possible from this study to determine which is occurring. While

this phenomenon is slight, it is consistent for both pollock and

whiting, and for both estimation methods. However, the bin
17

volume method had smaller deviations from a constant density than

the codend method.

A second potential problem is that the NMFS-prescribed

pollock density values applied to volume estimates may be

inappropriate and are causing a bias in the haul weights. This

problem appears most severe for the pollock bin volume estimates.

While there is apparent agreement between the codend density

estimate from the Alaska Ocean sampling and nominal pollock

density, this may be- due to opposing biases that cancel. The bin

density estimate may bias the catch low while the codend

estimates may bias the catch high. For Pacific whiting, both the'

codend and bin density estimates corresponded well with the mean

of the in situ basket sample estimates of fish density. The

conclusion that the NMFS-prescribed pollock density value may be

inappropriate is contingent on the accuracy of the Marel scale

weights; a bias in scale weights would explain all or part of

this apparent density bias.

Our results suggest that the current prescribed 0.93 t/m3

density may not be a good approximation of the density of pollock

in bins or codends under all conditions. The prescribed density

value is based on historical data derived by the basket method

and represents an average of many densities obtained by

observers. Either the average gained from all in situ densities

(across fishery seasons and catch proportions) should not be

applied to specific cases; that is, hauls composed of greater

than 95% pollock during the B season, or the in situ densities

making up this average are themselves in error. The density
18 I

measurements may bias the catch estimates such that the actual

catch weight is underestimated. The data from the Alaska Ocean

indicate that the estimates of pollock catch-weight using bin

volumes and a density of 0.93 t/m3 are 9% too low. A possible

explanation for this density bias is that fish are not packed as

tightly in a basket as they are in bins. In addition, the surface

area to volume ratio decreases with size of the container used to

estimate density. Thus, the effect of container size is much

more important in a small basket compared with a bin, which tends

to further reduce the estimated density.

Regardless of these biases, the codend method of volumetricbased catch estimation is significantly less precise than the bin

volume method and shows a greater departure from a constant

density across a wide range, of haul volumes.

RECOMMENDATIONS

Several recommendations follow as a result of this study

which could reduce bias and increase precision in current

observer haul weight estimation procedures.

1. Conduct fieldwork to evaluate Marel flow scale performance

under field conditions. This would encompass work where a

large quantity of fish can be weighed independently and then

weighed by a flow scale.
19

Rationale--The accuracy and precision of motion-compensating

flow scales has not been addressed in an experimental

setting. Results of such work will provide guidance on the

best way of improving precision in haul weight estimates.

Further work may also identify the source of the discrepancy

between the Marel scale weights and the volumetric estimates

based on the NMFS-prescribed pollock density.

2. Conduct fieldwork to further evaluate volumetric-based haul

weight estimation methods.

Rationale--the existing methods to-determine codend or bin

volume warrant review to identify potential improvements.

Additional work would explore the relationship between

density and volume (codend or bin size) by increasing the

number of samples at various codend or bin volumes. Current

procedures assume a constant density of fish regardless of

volume. Results reported in this paper suggested that the

assumption of constant density is questionable,

3. Prioritize fieldwork to further research the density

variable used in volumetric catch estimation, expanding it

beyond the pollock and whiting fisheries. Such fieldwork

should address broader issues such as determining the most

appropriate density sampling unit (size, shape, volume), use

of an accurate weight scale to experimentally calculate

codend or bin in situ densities, determining prescribed

densities for specific fisheries, and developing a density

table for use by observers which takes into account species
20

composition and other factors or developing an appropriate

device for in situ density measurement by observers.

Rationale--The current method likely has a bias in the

pollock or whiting fishery which may be present in other

fisheries. Investigating options to resolution will help to

determine what is needed to obtain in situ density

measurements which are not biased. Current methods appear

inadequate.

4. Direct observers to use codend estimates only when bin

volume measurements are not possible.

Rationale--This work demonstrates that codend estimates are

less precise than bin volume estimates.

5. Pursue regulatory measures to either weigh catch at sea or

provide marked, measured, lighted, and accessible bins for

use by observers.

Rationale--The proper approach would depend upon results of

the work suggested thus far. Scaleswould provide a weight

of catch. Alternatively, marked, measured, lighted, and

accessible bins would increase the precision of catch

estimates over codends, and provide for consistency in those

estimates between different observers. Increased precision

might also be gained through the use of bin indicators to

determine the height of fish in a bin.
21

When available, analyze the 1995 data to determine if there

are systematic differences between observer volumetric data

and vessel estimates of catch.

Rationale--The current total catch estimates in the Observer

Program database may contain a bias that affects current and

historical catch estimates..

7. Develop analytical procedures to eliminate dependence on

vessel estimates of catch.

Rationale--The current observer catch estimation relies on a

combination of observer estimates mixed with vessel

estimates. Expansion of catch data from observed to

unobserved hauls should represent a consistent scientific

approach to catch estimation rather than a mix of

methodologies.

In addition to the above recommendations, the level of catch

weight precision needed for management should be defined, and the

most reliable and cost-effective means (volumetric estimates or

direct weighing) of achieving this goal determined. Should the

currently used pollock density be in error, once this is

corrected the volumetric estimate method provides reasonably

accurate, precise, and dependable haul weight estimates. Whether

volumetric estimates will be outperformed by catch weight

determination using at-sea weighing has yet to be determined. A

critical factor in such a comparison is the overall reliability

 of each method. Reliability can be assessed through I) an
22

analysis of the at-sea dependability and 2) a review of the

amount of work needed to ensure either method's feasibility. The

feasibility measures include regulatory action, observer training

and workload, compliance monitoring, and enforcement.

I.

ACKNOWLEDGMENTS

This work would not have been possible without the

assistance and hospitality of Captain Jeff Hendricks and the crew

of the Alaska Ocean. Mike Atterberry of Alaska Ocean Seafoods

Inc. was also very helpful in making the arrangements to place

researchers on the Alaska Ocean to carry out this research. We

thank Greg Morgan and Allison Barnes who acted as the secondary

observers on board the vessel. We also thank Anne Hollowed,

Vidar Wespestad, and William Karp for reviewing and commenting on

earlier drafts.
23

CITATIONS

Neter, J., W. Wasserman, and M.H. Kutner. 1985. Applied linear

statistical analysis. Richard D. Irwin, Inc. Homewood,

Illinois, 1,127p.

Rosner, B. 1982. Fundamentals of biostatistics. Duxbury Press,

Boston, Massachusetts, 547p.

Teig, K.T., M.A. Brown, and S. Corey. 1994. Manual for

biologists aboard domestic groundfish vessels. 478 p.

Available from Observer Program; NMFS, Alaska Fisheries

Science Center, 7600 Sand Point Way, N.E., BIN C15700,

Seattle, WA 98115.

Weisberg, S. 1985. Applied linear regression. John Wiley and

Sons, New York, 324p.
2 4

Table 1. Pollock “B” season (16 Aug.-21Sept., 1994) Marel scale weights, discard,

codend and bin volume estimates using a density of 0.93.t/m3 . W= whole haul sample,

B = bin sample, Numbers 1, 2, 3 identify bins.
2 5

Table 2. Pacific whiting Oct. opening (1-5 Oct., 1,994) Marel scale weights, discard,

codend and bin volume estimates using a density of 0.90 t/m3. W= whole haul sample,

B= bin sample, Numbers 1, 2, 3 identify bins.
2 6

Table 3. Zero y-intercept linear regression estimates of fish density.

2 8

Figure 2. Haul weight in metric tons(t) (Marel scale weight +

discard) versus bin volume (m3

) for sampled pollock hauls during

the 1994 "B" season on the Alaska Ocean. Two regression lines

are shown: 1) a zero y-intercept regression line to estimate a

constant density and 2) a linear regression of weight on volume.

The linear regression had a significant y-intercept (P = 0.04).
29

Figure 3. Haul weight in metric tons(t) (Marel scale weight +

discard) versus codend volume (m3) for sampled pollock hauls

during the 1994 "B" season on the Alaska Ocean. Two regression

lines are shown: 1) a zero y-intercept regression line to

estimate a constant density and 2) a linear regression of weight

on volume. The linear regression had a significant y-intercept

(P = 0.01);
3 0

Figure 4. Haul weight in metric tons (t) (Marel scale weight +

discard) versus bin volume (m3

) for sampled whiting hauls during I

the 1994 October whiting opening on the Alaska Ocean. Two

regression lines are shown: 1) a zero y-intercept regression line

to estimate a constant density and 2) a linear regression of

weight on volume. The linear regression had a significant yintercept (P = 0.01).
Figure 5. Haul weight (t) (Marel scale weight + discard) versus

codend volume (m3) for sampled whiting hauls during the 1994

October whiting opening on the Alaska Ocean. Two regression

lines are shown: 1) a zero y-intercept regression line to

estimate a constant density and 2) a linear regression of weight

on volume. The y-intercept for the linear regression was not

significant (P = 0.30).
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.

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PELLA, J., R. RUMBAUGH, and M. DAHLBERG. 1995. Incidental catches of salmonids in the 1991 North

Pacific squid driftnet fisheries, 33 p. NTIS No. PB95-252722.

KINOSHITA, R. K., A. GRIEG, and J. M. TERRY. 1995. Economic status of the groundfish fisheries off

Alaska, 1993, 108 p. NTIS No. PB95-252714.

,

WING, B. L., and D. J. KAMIKAWA. 1995. Distribution of neustonic sablefish larvae and associated

ichthyoplankton in the eastern Gulf of Alaska, May 1990, 46 p. NTIS No. PB95-241519.

HONKALEHTO, T., and N. WILLIAMSON. 1995. Echo integration-trawl survey of walleye pollock

(Theragna) in the southeast Aleutian Basin during February and March, 1994, 39 p. NTIS

No. PB95-219424.

CLARY, J. C. (editor). 1995. Poster abstracts and manuscripts from the Third International Conference

on Marine Debris, May 8-13, 1994, Miami, Florida, 108 p. NTIS No. PB95-210837.

MUNRO, P. T., and R. Z. HOFF. 1995. Two demersal trawl surveys in the Gulf of Alaska: Implications

of survey design and methods, 139 p, NTIS No. PB95-195061.

STARK, J. W., and D. M. CLAUSEN. 1995. Data Report: 1990 Gulf of Alaska bottom trawl survey, 221

p. NTIS No. PB95-194825.

NARITA, R., M. GUTTORMSEN, J. GHARRETT, G. TROMBLE, and J. BERGER. 1994. Summary of

observer sampling of domestic groundfish fisheries in the northeast Pacific Ocean and eastern Bering Sea,

1991,540 p. NTIS No. PB91190963

DORN, M. W., E. P. NUNNALLEE, C. D. WILSON, and M. E. WILKINS. 1994. Status of the coastal

Pacific whiting resource in 1993, 101 p. NTIS No. PB95-176467.

SINCLAIR, E. H. (editor). 1994. Fur seal investigations, 1993, 93 p. NTIS No. PB95-178943.

SINCLAIR, E. H. (editor). 1994. Fur seal investigations, 1992, 190 p. NTIS No. PB95173472.

KINOSHITA, R. K., and J. M. TERRY. 1994. Oregon, Washington, and Alaska exports of edible

fishery products, 1993, 52 p. NTIS No. PB95-165924.

FERRERO, R. C., and L. W. FRITZ. 1994. Comparisons of walleye pollock, Theragra chalcogramma,

harvest to Steller sea lion, Eumetopias jubatus, abundance in the Bering Sea and Gulf of Alaska, 25 p.

NTIS No. PB95-155602.

ZIMMERMANN, M. , M. E. WILKINS, R. R. LAUTH, and K. L. WEINBERG. 1994. The 1992 Pacific

west coast bottom trawl survey of groundfish resources: Estimates of distribution, abundance, and length

composition, 110 p. plus Appendices. NTIS No. PB95-154159.

ZIMMERMANN, M., P. GODDARD, T. M. SAMPLE. 1994. Resullts of the 1991 U.S.-U.S.S.R. cooperative

bottom trawl survey of the eastern and western Bering Sea continental shelf, 178 p. NTIS No. PB95-

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SIGLER, M. F., and H. H. ZENGER, Jr. 1994. Relative abundance of Gulf of Alaska sablefish and

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