NOAA Technical Memorandum NMFS-AFSC-364

J. Sullivan and C. Faunce

Alternative Sampling Designs for the 

2018 Annual Deployment Plan of the 

North Pacific Observer Program

U.S. DEPARTMENT OF COMMERCE

National Oceanic and Atmospheric Administration

National Marine Fisheries Service

Alaska Fisheries Science Center

January 2018

doi:10.7289/V5/TM-AFSC-364
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 NMFS-NWFSC series is currently used by 

the Northwest Fisheries Science Center.

This document should be cited as follows:

Sullivan, J., and C. Faunce. 2018. Alternative sampling designs for the 

2018 Annual Deployment Plan of the North Pacific Observer Program. 

U.S. Dep. Commer., NOAA Tech. Memo. NMFS-AFSC-364, 30 p.

Document available: http://www.afsc.noaa.gov/Publications/AFSC-TM/NOAA-TM-AFSC-364.pdf

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

the National Marine Fisheries Service, NOAA.
January 2018

NOAA Technical Memorandum NMFS-AFSC-364

by

J. Sullivan1† and C. Faunce2‡

1

Alaska Sea Grant Fellow

Sustainable Fisheries Division

Alaska Regional Office

National Marine Fisheries Service

National Oceanic and Atmospheric Administration

709 West 9th Street

Juneau, AK 99801

†

Current Address: Alaska Department of Fish & Game 1255 W. 8th Street, Juneau, AK 99802

2

Fisheries Monitoring and Analysis Division

Alaska Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration

7600 Sand Point Way NE

Seattle, WA 98115

‡ contact author

U.S. DEPARTMENT OF COMMERCE

Wilbur L. Ross Jr., Secretary

National Oceanic and Atmospheric Administration

 RDML Timothy Gallaudet (ret.), Acting Under Secretary and Administrator

National Marine Fisheries Service

 Chris Oliver, Assistant Administrator for Fisheries 

doi:10.7289/V5/TM-AFSC-364

Alternative Sampling Designs for the 

2018 Annual Deployment Plan of the 

North Pacific Observer Program
This document is available to the public through: 

National Technical Information Service 

U.S. Department of Commerce 

5285 Port Royal Road 

Springfield, VA 22161 

www.ntis.gov 

iii

ABSTRACT

Changes in regulation enacted in 2013 have enabled the Alaska Fisheries Science 

Center’s Fishery Monitoring and Analysis Division (FMA) and the Alaska Regional Office’s 

Sustainable Fisheries Division to work collaboratively on an Annual Deployment Plan (ADP). 

Each ADP documents how the National Marine Fisheries Service (NMFS) plans to deploy 

observers into fishing activities for the coming year under the limits of available funding. Draft 

ADPs are presented to the North Pacific Fishery Management Council (Council) during 

September - October and are finalized in December. 

The sampling design for observer deployment has two elements: how the population is 

subdivided (i.e., stratification schemes) and how available samples are allocated (i.e., allocation 

strategies). Here the relative performance of 10 alternative sampling designs (at the primary 

sampling unit- the trip) are compared in support of the draft 2018 ADP. These alternative 

sampling designs consisted of the combination of two stratification schemes (gear-type only or 

gear-type × tendering activity), two metrics upon which to base optimizations [one consisting of 

discard of groundfish with Prohibited Species Catch (PSC) of Pacific halibut and the other 

consisting of the prior and PSC of Chinook salmon], and three allocation strategies (no 

optimization, a “hurdle” approach to optimization, and a optimization only). All optimization 

allocations incorporate three variables measured over the past 3 years: variance in the metric, the 

average cost of observing a trip, and the number of trips. Total afforded sample size is

determined by the available budget and the average cost of observing each trip. Resulting

selection rates derive from sample size, allocation weightings and the anticipated fishing effort 

which was defined as the most recent complete year of data. 

The total number of observer days that can be afforded is 4,062 which represents a 33% 

increase from 2017. Gap analyses that examine the chance of at least one or three observed trips 

in a NMFS Area × gear type combination (cell) were used as a performance metric. Gap 

analyses illustrated that stratifications based on gear type (3 strata) were outperformed by 

stratifications based on gear type × tendering activity (6 strata). Potential gaps in observer 

coverage appear to be mostly concentrated in areas with low fishing effort with fewer than 12 

trips in a cell. Simulations were performed to measure the potential impact of unknown vessel 

participation in electronic monitoring (EM). The variability in gap analyses from randomized 

differences in EM participant vessels was relatively minor (less than 10% probability of 

observation shifts across deployment designs). 

The NMFS recommended an observer deployment design for the draft 2018 ADP that 

has gear type × tendering stratification and uses a “hurdle” approach to sample allocation 

wherein 15% base coverage is obtained first across all strata and the remainder is optimized 

according to the variance in the metric of discarded groundfish catch combined with PSC Pacific 

halibut and Chinook salmon. At their October 2017 meeting the Council did not support the 

NMFS recommendation and instead proposed a five strata design with optimal sample 

allocations based on discarded groundfish catch and PSC of Pacific halibut only. Comparisons 

between the NMFS and Council recommended designs were included in the final 2018 ADP.

v

CONTENTS

Abstract..........................................................................................................................................iii

Contents.......................................................................................................................................... v

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

Methods........................................................................................................................................... 2

Data Preparation: Defining the Partial Coverage Fleet............................................................... 2

Budget Forecasting ..................................................................................................................... 3

Deployment Design .................................................................................................................... 4

Stratification Schemes ............................................................................................................ 5

Sample Allocation................................................................................................................... 6

Blended allocations................................................................................................................. 8

Evaluation of Alternative Designs.............................................................................................. 9

Gap Analyses.............................................................................................................................. 9

Uncertainty Due to Electronic Monitoring ............................................................................... 10

Results and Discussion ................................................................................................................. 11

Citations........................................................................................................................................ 15

INTRODUCTION

The North Pacific Observer Program uses a hierarchical sampling design with 

randomization at all levels to achieve unbiased data from fishing operations in the region. The 

Annual Deployment Plan (ADP) documents how NMFS plans to deploy observers in the partial 

coverage category onto fishing trips in the upcoming year under the limits of available funding.

The ADP provides an annual process for NMFS and the Council to evaluate the sampling 

design used to deploy observers and EM. In the Draft 2017 ADP, NMFS presented six 

alternative stratification designs for deployment of observers (NMFS 2016a). The adopted design 

in the Final 2017 ADP allocates observed trips among six strata defined by gear and tendering 

activity according to an optimized allocation resulting from the interactions of stratum size and 

variance in total discarded catch with Pacific halibut Prohibited Species Catch (PSC, NMFS 

2016b). Following the most recent Annual Report (NMFS 2017a) and subsequent Council

motion this analysis builds upon the 2017 ADP design by evaluating whether to continue the 

tender strata definition and compares the following alternative allocation designs: 1) equal 

coverage rates that can be afforded across all strata with available funding; 2) 15% coverage 

rates across all strata with optimization on anything above 15%; and 3) optimization of all 

partial-coverage trips. All allocation strategies evaluated include those based on discarded 

groundfish and halibut, Chinook salmon PSC and a blended combination of the two. In addition, 

this ADP accounts for the uncertainty introduced by Electronic Monitoring (EM) pool by 

simulating the full range of potential partial coverage populations in the hook-and-line and pot 

gear strata.
This analysis provides a comparison of the relative performance of alternative strata 

definitions, stratification schemes, and allocation designs for the deployment of observers into 

the partial coverage fleet for consideration in 2018.

METHODS

Data Preparation: Defining the Partial Coverage Fleet

The partial coverage fleet in general consists of the catcher vessel fleet when not 

participating in a catch sharing or cooperative style management program. Changes to this 

general design have resulted from NMFS policy, Council Action, and regulations. Activities 

expected to occur in 2018 that have been excluded from observer coverage in the past include 1) 

catcher vessels while fishing in state-managed fisheries, 2) catcher vessels fishing with jig gear, 

3) catcher vessels fishing that are sized < 40 feet in length overall (LOA), and 4) vessels that 

volunteer for EM. It was assumed that AFA-endorsed trawl catcher vessels that volunteered to 

carry full observer coverage when fishing in the Bering Sea and Aleutian Islands in 2017 will 

continue to do so in 2018. 

A database containing 2014, 2015, and 2016 species-specific catch amounts, dates, 

locations, and disposition, and observation status was first enhanced with additional information 

from the Alaska Regional Office and FMA, and then parsed to reflect the partial coverage fleet 

subject to observer coverage in 2017, and finally re-labelled according to the alternative 

deployment designs for 2018.
3

Budget Forecasting

The available budget for observer days in 2018 was estimated from carryover funds from 

the previous fiscal year, stable fee revenues between 2018 and 2019, and a $1M increase in 

Federal funding in fiscal year 2018. Sea-day expenditures were set so that the total number of 

observer days would remain stable between 2018 and 2019 and there would be no carryover 

funds after December of 2019. Budget forecasting is necessary to determine not only the number 

of sea-days expected for the upcoming calendar year, but also to determine how much money 

should be allocated for each contract year, which runs from 17 June of one year to 16 June of the 

next. For this reason, calendar years were divided into two seasons(s): a first half (FH) period 

from 1 January 2017 to 16 June 2017 and a second half (SH) from 17 June 2017 to 31 December

2018. 

The exercise of budget forecasting starts in the SH of the calendar year (y) prior to the 

ADP (y - 1; here y is = 2018 and y - 1 = the SH of 2017). The forecasting process requires an 

estimation of available funds (F). The available sea day budget for the current fiscal year (Bo, 

comprised of SH 2017 and FH 2018) was determined by subtracting the expected travel for the 

current fiscal year from F. Expected travel (Texp) was estimated from the division of the total 

observer day funds expended for each season of the previous fiscal year (Eo) into the total travel 

funds (T) expended in the previous fiscal year and multiplying this ratio (R) by the available 

funds for the current fiscal year, or

TTeeeeee = ∑ EEoo, yy−1

SS

ss=1

∑ TTyy−1

SS

ss=1

FF = RR . Eq. (1)

In order to calculate the funds remaining at the end of the current fiscal year, the total 

number of sea days and their cost is needed. While the number of observer days (ddoo) in FH 2017 
4

are known, those of SH 2017 were assumed using the number of expected days from the 2017 

ADP (NMFS 2016b). The expected expenditures of funds for observer days (EEoo) for each season 

is the product of ddoo and the cost of a sea-day from the contract between NMFS and its observer 

provider. Subtraction of EEoo from BBoo in SH 2017 yielded the value for FF in the FH of 2018. Since 

FH 2018 is in the SH of the fiscal year, travel funds were already accounted for, so the only 

expenditure to account for in this time period is the expenditure of observer days. By setting (1) 

TTeeeeee to equal a constant RR with an updated FF each fiscal year, (2) ddoo FH yy + 1. . nn equal to ddoo in 

FH 2018, and (3) ddoo SH yy + 1. . nn equal to the ratio of ddoo SH : ddoo FH, the value for ddOO FH 2018 

could be used as the main input into initial cost forecasts. The value for ddoo FH 2018 was 

adjusted until the EEoo 2019 = BBoo 2019, and ddoo 2018 = ddoo 2019.

The values for FF2018 and the average cost of an observer day (from FF2018 divided by ddoo

2018) from above were then passed as inputs into the analyses described in the Deployment 

Design section below. From 1,000 iterations of simulated sampling, the value for the ratio of ddoo 

SH 2018 : ddoo FH 2018 was determined and passed back into the budget forecasts. The values for 

ddoo FH 2018 was then adjusted in an iterative process until EEoo 2019 = BBoo 2019, and ddoo 2018 = ddoo

2019. At this point the budgetary forecast was considered complete, and the values for FF2018 and 

the average cost of an observer day in 2018 were passed a final time into the analyses described 

in the sections.

Deployment Design

The sampling design for observer deployment (hereafter 'deployment design') involves 

two elements; how the population of partial coverage trips is subdivided (stratification), and 
5

what proportion of the total observer deployments are to occur within these subdivisions 

(allocation).

Stratification Schemes

Stratification is the partitioning of units in the population into independent groups (or 

sub-populations). These groupings are individually called stratum (strata if plural). Stratified 

random sampling is the act of obtaining independently random samples from within each 

stratum. For this reason, strata need to be defined based on criteria known prior to the draw of 

the sample. This means that elements of fishing trips known prior to departure are valuable in 

defining deployment strata, whereas catch is not.

There are numerous reasons for creating strata. These include the following: when a 

separate estimate for a sub-population is desired, when administrative convenience (field 

logistics) requires it, and to increase the precision of sample-based estimates of the total. 

Increased precision is accomplished through the division of a heterogeneous population into 

homogeneous sub-populations, and the resulting variance of the population total being calculated 

from the variance of the individual stratum (Cochran 1977). The collection of strata that together 

subdivide the population of trips in partial coverage constitutes a stratification scheme. In this 

study two stratification schemes were considered. These stratification schemes (with the number 

of the individual strata in parentheses) are as follows:

1. Gear × Tender (6 strata)

This status quo stratification divides the partial coverage trips into six strata based on 

gear and tendering status.

• Hook and Line ≥ 40' LOA (HAL).

• Tender Hook and Line ≥ 40' LOA (Tender HAL).
6

• Pot ≥ 40' LOA (POT).

• Tender Pot ≥ 40' LOA (Tender POT).

• Trawl (TRW).

• Tender Trawl (Tender TRW).

2. Gear (3 strata)

This stratification was used in 2016 and is comprised of HAL, POT, and TRW vessels.

Sample Allocation

Sample allocation refers to the allotment of trips in a stratum. Three allocation strategies

were compared for 2018 observer deployment (the full workflow for the methods used in these 

designs is found in Fig. 1):

1. Equal Allocation

This allocation design estimates the equal coverage rate (trips sampled/total trips) across 

strata that can be afforded with available funding. Unlike previous years when optimal allocation 

was used, this design allocates samples proportional to fishing effort in a stratum. Similar to past 

years, the number of fishing trips (NN) that occur within HH strata was assumed to be equal to the 

most recent years' fishing activity. The cost of an observed trip in each stratum (cch) is estimated 

as the product of the mean trip duration in a stratum and the cost of an observer day. The equal 

coverage rate afforded (rr) across all strata was then calculated as

rrh = FF2018

∑ cch

HH

h=1 NNh

, Eq. (2)

where FF2018 is the estimated funds from the budget forecasting.

2. 15% + Optimized
7

Unlike equal rates afforded, this sample allocation adopts a "hurdle" approach to 

optimization. Optimization aims to maximize precision for the chosen metrics for the least cost. 

In this allocation strategy, observer sea days are first allocated equally up to a 15% coverage rate. 

Once 15% has been met, an optimal allocation algorithm (described below) is used to allocate 

remaining monitored trips among strata. If available funding does not permit equal allocation up 

to 15%, the total amount of additional funds needed to meet 15% is estimated. The minimum 

15% coverage rate was recommended by the Observer Science Committee because it has been 

shown to eliminate or minimize severe gaps in observer data (Faunce et al. 2017, NMFS 2017, 

NMFS 2015c, NMFS 2015d, p. 98). This allocation first estimates the number of trips left over 

in each stratum after 15% coverage has been met

NNh+ = NNh − (0.15 × NNh) Eq. (3)

and then calculates the new budget (FF+) available for optimized allocation among strata

FF2018+ = �cch

HH

h=1

NNh+ . Eq. (4)

The FF2018+ and NNh+ are then used in the optimization algorithm, where FF2018+ and NNh+

are substituted for FF2018 and NNh, respectively, in the following equations.

3. Optimized

This design was used in the 2016 and 2017 ADP and has no minimum sample size 

requirement. If nn is the number of observed trips afforded for the year among all partial coverage 

fishing trips in each strata (NNh), and the estimate of the chosen metric of interest has SS2 variance, 

the number of samples that is considered optimum for each stratum (nnh) is denoted by the 

product of the total sample size and the optimal weighting (WWhoo oo),
8

nnh ∗ WWhoo oo, where WWhoo oo =

NNhSSh

�cch

∑ �

NNhSSh

�cch

� HH

h=1

Cochran (1977). Eq. (5)

While equation 1 gives the allocation of observed trips among strata, it does not give the 

total sample size. To obtain this we can rearrange the previous equation as

nn =

FF2018 ∑ �

NNhSSh

�cch

� HH

h=1

∑ ( HH

h=1 NNhSSh�cch)

Cochran (1977). Eq. (6)

The value for nn is used to solve for the sample size in each stratum using the stratum 

weightings described previously. The resulting coverage rate in each stratum is obtained from the 

division of nnh by NNh. 

Blended allocations

Optimized sample allocations were generated using the variance of discarded catch with 

Pacific halibut PSC included. However, optimizations may be conducted on more than one target 

metric. Following the 9 June 2017 Council Motion that emphasized PSC-limited fisheries, we 

included an additional variable of Chinook PSC counts into the optimization. Cochran (1977) 

shows that the blended optimal allocation (mmh) is derived from the average number of optimal 

sample sizes measured across LL metrics,

mmh = nn × nnh, wwhee ee nnh = ∑ nnll,h

LL

ll=1

LL . Eq. (7)

It is worth noting that unless nnh among all metrics are positively correlated, the resulting 

compromise allocations may be substantially different from nnh for any individual target metric.
9

Evaluation of Alternative Designs

Data from 2014, 2015, and 2016 were combined and treated as a single meta-year for the 

calculation of optimal allocation weightings (WWhoo oo) in each strata. Distributions of the trip 

duration, discarded catch with halibut PSC, and Chinook PSC counts for each stratification 

scheme were plotted since these form the raw ingredients for the sample size allocation formulae 

(Fig. 2).

Gap Analyses

Observers provide an invaluable service to the generation of total catch estimates; if there 

are no observer data in a given domain of interest, then data must be borrowed from similar or 

adjacent sampling units, resulting in poor inference about the total catch. An insufficient level of 

observer coverage can have implications for in-season quota management, catch estimation, 

stock assessment, and management of protected resources. The evaluation of alternative designs 

was determined using gap analysis following previous evaluations of observer program 

deployments (NMFS 2015a, NMFS 2016a). Gap analysis estimates the probability of observing 

a trip in a given domain of interest; the fewer the gaps, the better the design.

The gap analyses and all subsequent analyses were performed using 2016 data under the 

assumption that immediate past fishing activity is a good predictor of future fishing activity 

(Fig. 1). Similar to the 2017 ADP, the number of partial coverage trips corresponding to each 

stratification scheme was summed into domains defined by gear and NMFS reporting area 

(NMFS 2016a, NMFS 2016b).

The hypergeometric distribution was used to calculate the probability of observing at 

least one and three trips within a domain for each stratification and allocation design. These 
10

probabilities were made binary (0 and 1) based on whether or not they exceeded 50%. This value 

was chosen as the minimum acceptable value since it represents equal chance of meeting the 

needs of variance calculation within a domain. The proportion of domains that passed the three 

or more criteria was calculated for comparison and represented as a G score (GG) for each 

allocation strategy. This G score was divided by the maximum G score within a given 

stratification scheme to provide a relative metric. This relative G score ranges from 0.00 to 1.00, 

where 1.00 is best.

Uncertainty Due to Electronic Monitoring

The EM pool will remain a voluntary stratum in the partial coverage category in 2018. 

Methods used to estimate costs and allocate funds for maintenance and growth of the EM 

program in 2018 are described in Appendix B of NMFS 2017b. This analysis estimates that there 

will be sufficient funding available to wire 35 new vessels for monitoring in 2018 for a total of 

110 EM vessels. Enrollment into the EM stratum is open until 1 November 2017, after the 

publication date and presentation of the Draft ADP to the NPFMC. The Final Rule for EM states 

that hook-and-line and pot gear vessels in the partial coverage category are eligible to volunteer 

for the EM stratum. Because the open enrollment period extends beyond the date of completion 

of the ADP, the 2018 population of vessels in the HAL and POT strata were unknown. To 

account for this uncertainty, 10,000 simulations of random draws of 35 new vessels from the 

HAL and POT strata were performed and coverage rate and gap analyses on the vessels 

remaining in the partial coverage category were evaluated from each simulation. The differences 

in the outcomes from each simulation were depicted as error bars around the resulting coverage 

rates and a subset of random draws from the outcomes of the gap analyses. All resulting tables 
11

show the mean estimates of coverage rates and probabilities from the gap analyses from the 

simulation outcomes.

RESULTS AND DISCUSSION

The total number of observer days available for deployment in the Observer Program is 

4,062. Depending on the deployment design chosen, the ratio between the SH and FH days is 

0.876 - 0.879 to 1.

The optimization algorithm puts more samples where 1) strata are larger, 2) variance of a 

chosen metric is larger, and 3) costs are lower (Cochran 1977). This accommodates differential 

trip duration and differential costs between observation types (e.g., human vs. cameras) that may 

be needed in future ADPs. Moreover, the comparison of coverage rates using equal allocation, 

15% plus optimization, and full optimization elucidates the tradeoff between minimizing gaps in 

coverage and emphasizing the importance of certain metrics such as groundfish discards and 

PSC.

Whether resulting rates of observer coverage differ between deployment designs depends 

upon how the rates are compared (Fig. 3, Tables 1, 2). Coverage rates differ substantially 

between allocation designs, in particular between designs that use equal or 15% + optimized 

allocation and designs that rely solely on optimization based on chosen metrics. Within a given 

allocation design, coverage rates vary minimally within a stratum between stratification schemes. 

For example, within the 15% + optimized on discarded groundfish catch including Pacific 

halibut PSC allocation, the rates for TRW, HAL, and POT vary minimally between stratification 

schemes (TRW = 18.08%, 18.16%; HAL = 17.33%, 17.32%; POT = 15.59%, 15.56% for Gear 

and Gear × Tender, respectively; Tables 1, 2). The lack of difference in coverage rates within a 
12

stratum and allocation design is due to the fact that tendering strata are relatively small in terms 

of total trips compared to gear-based strata (Nh2018 in Tables 1, 2). The Optimized allocation 

based on blended discarded groundfish catch with halibut and Chinook PSC results in the lowest 

rates for HAL and POT and the highest rates in TRW (HAL = 10.76%, 10.86%; POT = 2.20%, 

2.04%; TRW = 31.15%, 34.42% for Gear and Gear × Tender, respectively).

Results from gap analyses indicate that allocation based solely on optimization results in 

the most gaps in observer coverage (Fig. 4, where the curves that reach the top the fastest, or the 

farthest to the left, represent designs that result in the fewest gaps in coverage). The optimized 

allocation based on blended discarded groundfish catch with halibut and Chinook PSC has the 

most gaps, whereas designs that use equal or 15% + optimized allocations result in far fewer 

gaps in coverage (indicated by high relative G scores in Table 3). The best performing designs 

result in a predicted 76% and 62% of cells with at least a 50% probability of having three or 

more observed trips in the Gear and Gear × Tender stratification, respectively. A closer 

examination of resulting gaps by NMFS Reporting Area and stratum combination in the Bering 

Sea and Aleutian Islands (Tables 4, 5) and the Gulf of Alaska (Tables 6, 7) suggests that 

potential gaps (shown in bold) in designs that use equal or 15% + optimized allocations only 

occur when there is low fishing effort (fewer than 12 fishing trips) in that cell.

Results from the simulation analysis, which was conducted to account for uncertainty 

introduced by open-enrollment of HAL and POT boats into EM, suggests that its impact on ADP 

results is relatively minor. The “error” bars shown in Figure 3 are undetectable except in the 

HAL Tender stratum. The variability in HAL Tender can be accounted for by the small number 

of predicted trips in this stratum (7 trips, Table 2). The variability seen in the gap analyses is also 
13

relatively minor, with the probability of observing at least three trips shifting less than 10% 

across deployment designs (Fig. 4).

The 15% + optimized allocation is a balance between the prioritization of PSC-limited 

fisheries in optimization weighting schemes and the need to reduce gaps in observer coverage in 

the partial coverage category. Within this allocation design, the gear-based stratification scheme 

that optimizes on discarded groundfish catch and halibut PSC performs the best in the gap 

analysis 4). However, there are numerous reasons to select the Gear × Tender stratification. 

First, the Gear × Tender stratification scheme, which was first implemented in 2017, has not 

been fully evaluated in the Annual Report process. Maintaining this stratification scheme for 

another year, while improving the allocation design, would allow the Observer Program to fully 

analyze the effects and performance of these designs. Further, as discussed in the 2016 Observer 

Program Annual Report, tendering activity in pollock trawl fisheries continues to represent a 

sampling challenge for the Observer Program (NMFS 2017). Although it has yet to be evaluated 

whether the addition of a Tender TRW stratum fully alleviates this problem, it does ensure an 

expectation for a certain level of coverage for that operation type. 

This analysis relies on several key assumptions and has limitations. For example, it is 

assumed that discarded catch on each sampled trip is known without variance, and a simple 

single-stage estimator of trip variances are used in optimization algorithms. The variances used 

in this analysis are not the same that will arise from the five-stage sampling design of the 

observer program (Cahalan et al. 2014). Previous studies have demonstrated that although the 

vessel was a significant factor in estimating total discards, the first stage of nested sampling 

designs (vessel or trip) is often the stage with the least amount of variance (Allen et al. 2002, 
14

Borges et al. 2004). Multi-stage based estimates of variance for each stratum and metric will be 

used in subsequent analyses when they become available.

The resulting coverage rates for observer deployment depend upon the amount of fishing 

effort and the available number of observer days. Since this analysis is focused on the relative 

performance of alternative stratification schemes, it uses a simplified assumption of future 

fishing effort-- namely that fishing in 2016 will be identical to that in 2018. This assumption is 

made in anticipation that for the Final 2018 ADP, when a stratification scheme is selected, a 

more careful estimate of anticipated fishing effort would be made at that time, and resulting rates 

adjusted to reflect this new prediction. This approach was adopted for the Final 2017 ADP 

(NMFS 2016b).

Finally, the resulting coverage rates presented in this study should only be considered 

preliminary estimates and may differ from rates determined in the Final ADP or realized in 2018. 

Once a stratification design for the Final ADP is established and EM participants known, more 

focused simulated sampling procedures will be used to estimate expected coverage rates 

following the methods described in the Final 2016 and 2017 ADPs (NMFS 2015b, NMFS 

2016b).
15

CITATIONS

Alaska Fisheries Science Center and Alaska Regional Office. 2017. North Pacific Observer 

Program 2016 Annual Report. AFSC Processed Rep. 2017-07, 143 p. Alaska Fish. Sci. 

Cent., NOAA, Natl. Mar. Fish. Serv., 7600 Sand Point Way NE, Seattle WA 98115. 

Available online at: https://www.afsc.noaa.gov/Publications/ProcRpt/PR2017-07.pdf.

Allen, M., D. Kilpatrick, M. Armstrong, R. Briggs, G. Course, and N. Perez. 2002. Multistage 

cluster sampling design and optimal sample sizes for estimation of fish discards from 

commercial trawlers. Fish. Res. 55: 11-24.

Borges, L., A. F. Zuur, E. Rogan, and R. Officer. 2004. Optimum sampling levels in discard 

sampling programs. Can. J. Fish. Aquat. Sci., 61: 1918-1928.

Cahalan, J., Mondragon, J., and J. Gasper. 2014. Catch sampling and estimation in the federal 

groundfish fisheries off Alaska, 2015 edition. U.S. Dep. Commer., NOAA Tech. Memo. 

NMFSAFSC-286, 46 p.

Cochran, W. G. 1977. Sampling Techniques (Third Edition), New York, NY: John Wiley & 

Sons.

Faunce, C., J. Sullivan, S. Barbeaux, J. Cahalan, J. Gasper, S. Lowe, and R. Webster. 2017. 

Deployment performance review of the 2016 North Pacific Groundfish and Halibut 

Observer Program. U.S. Dep. Commer., NOAA Tech. Memo. NMFS-AFSC-358, 75 p. 

Document available: https://www.afsc.noaa.gov/Publications/AFSC-TM/NOAA-TMAFSC-358.pdf.
16

NMFS. 2017a. North Pacific Observer Program 2016 Annual Report. AFSC Processed Report 

2017-07, 143 p. Available online at: 

https://www.afsc.noaa.gov/Publications/ProcRpt/PR2017-07.pdf.

NMFS (National Marine Fisheries Service). 2017b. Draft 2018 Annual Deployment Plan for 

Observers in the Groundfish and Halibut Fisheries off Alaska. National Oceanic and 

Atmospheric Administration, 709 West 9th Street. Juneau, Alaska 99802. Available 

online at: https://alaskafisheries.noaa.gov/sites/default/files/draft_2018_adp.pdf.

NMFS (National Marine Fisheries Service). 2016a. Draft 2017 Annual Deployment Plan for 

Observers in the Groundfish and Halibut Fisheries off Alaska. National Oceanic and 

Atmospheric Administration, 709 West 9th Street. Juneau, Alaska 99802. Available 

online at: https://alaskafisheries.noaa.gov/sites/default/files/draft_2017adp.pdf.

NMFS. 2016b. 2017 Annual Deployment Plan for Observers in the Groundfish and Halibut 

Fisheries off Alaska. National Oceanic and Atmospheric Administration, 709 West 9th 

Street. Juneau, Alaska 99802. Available online at: 

https://alaskafisheries.noaa.gov/sites/default/files/2017finaladp.pdf.

NMFS. 2015a. Draft 2016 Annual Deployment Plan for observers in the groundfish and halibut 

fisheries off Alaska. Available online at: 

http://alaskafisheries.noaa.gov/sustainablefisheries/observers/draft2016adp.pdf.

NMFS. 2015b. 2016 Annual Deployment Plan for observers in the groundfish and halibut 

fisheries off Alaska. Available online at: 

https://alaskafisheries.noaa.gov/sites/default/files/final2016adp.pdf.
17

NMFS. 2015c. North Pacific Groundfish and Halibut Observer Program 2015 Annual Report. 

Published May 2016. 104 p. Available online at: 

https://alaskafisheries.noaa.gov/sites/default/files/annualrpt2014.pdf.

NMFS. 2015d. Supplement to the Environmental Assessment for Restructuring the Program for 

Observer Procurement and Deployment in the North Pacific. NMFS, Alaska Regional 

Office, Juneau. May 2015. Available online at 

https://alaskafisheries.noaa.gov/sites/default/files/analyses/finalea_restructuring0915.pdf
18

Table 1. -- Comparison of the number of trips in a stratum (Nh2018), the optimal sample weighting 

(Whopt), preliminary predicted observed trips (nh), days (dh), and coverage rates (rh) 

resulting from the Gear-based stratification scheme under three allocation designs: 1) 

Equal allocation, 2) 15% + Optimized, and 3) Optimized. Metrics used for 

optimization included: 1) discarded groundfish catch with Pacific halibut prohibited 

species catch (PSC) and 2) a blended optimization of 1) and Chinook PSC (in numbers 

of fish).

Stratum (h) Metric Nh2018 Whopt nh dh rh (%)

Equal Allocation

HAL None 2,237 388 1,896 17.36

POT None 963 167 599 17.36

TRW None 2,686 466 1,567 17.36

15% + Optimized

HAL Discards w/ halibut PSC (Status quo) 2,237 0.37 388 1,892 17.33

POT Discards w/ halibut PSC (Status quo) 963 0.04 150 538 15.59

TRW Discards w/ halibut PSC (Status quo) 2,686 0.59 486 1,632 18.08

HAL Blended: Discards w/ halibut PSC + Chinook PSC 2,237 0.21 365 1,784 16.33

POT Blended: Discards w/ halibut PSC + Chinook PSC 963 0.02 147 528 15.29

TRW Blended: Discards w/ halibut PSC + Chinook PSC 2,686 0.77 521 1,751 19.40

Optimized

HAL Discards w/ halibut PSC (Status quo) 2,237 0.41 417 2,035 18.63

POT Discards w/ halibut PSC (Status quo) 963 0.04 42 151 4.38

TRW Discards w/ halibut PSC (Status quo) 2,686 0.55 558 1,877 20.79

HAL Blended: Discards w/ halibut PSC + Chinook PSC 2,237 0.23 241 1,175 10.76

POT Blended: Discards w/ halibut PSC + Chinook PSC 963 0.02 21 76 2.20

TRW Blended: Discards w/ halibut PSC + Chinook PSC 2,686 0.75 836 2,811 31.15
19

Table 2.-- Comparison of the number of trips in a stratum (Nh2018), the optimal sample weighting 

(Whopt), preliminary predicted observed trips (nh), days (dh), and coverage rates (rh) 

resulting from the Gear × Tender stratification scheme under the allocation designs 

described in Table 1. Bold values denote NMFS recommendations for the 2018 ADP.

Bold values denote NMFS recommendations for the 2018 ADP.

Stratum (h) Metric Nh2018 Whopt nh dh rh (%)

Equal Allocation

TRW None 2,427 421 1,377 17.34

HAL None 2,231 387 1,890 17.34

POT None 858 149 517 17.34

Tender TRW None 259 45 196 17.34

Tender HAL None 7 1 4 15.46

Tender POT None 105 18 78 17.31

15% + Optimized

TRW Discards w/ halibut PSC (Status quo) 2,427 0.55 441 1,442 18.16

HAL Discards w/ halibut PSC (Status quo) 2,231 0.37 386 1,888 17.32

POT Discards w/ halibut PSC (Status quo) 858 0.03 133 464 15.56

Tender TRW Discards w/ halibut PSC (Status quo) 259 0.04 44 194 17.13

Tender HAL Discards w/ halibut PSC (Status quo) 7 0.00 1 4 15.42

Tender POT Discards w/ halibut PSC (Status quo) 105 0.01 17 72 16.02

TRW Blended: Discards w/ halibut PSC + Chinook PSC 2,427 0.75 480 1,571 19.78

HAL Blended: Discards w/ halibut PSC + Chinook PSC 2,231 0.21 364 1,781 16.34

POT Blended: Discards w/ halibut PSC + Chinook PSC 858 0.02 131 456 15.28

Tender TRW Blended: Discards w/ halibut PSC + Chinook PSC 259 0.02 42 182 16.06

Tender HAL Blended: Discards w/ halibut PSC + Chinook PSC 7 0.00 1 4 15.42

Tender POT Blended: Discards w/ halibut PSC + Chinook PSC 105 0.00 16 70 15.46
20

Table 2. -- Continued.

Stratum (h) Metric Nh2018 Whopt nh dh rh (%)

Optimized

TRW Discards w/ halibut PSC (Status quo) 2,427 0.52 530 1,736 21.86

HAL Discards w/ halibut PSC (Status quo) 2,231 0.41 416 2,035 18.67

POT Discards w/ halibut PSC (Status quo) 858 0.03 35 121 4.07

Tender TRW Discards w/ halibut PSC (Status quo) 259 0.03 31 136 11.99

Tender HAL Discards w/ halibut PSC (Status quo) 7 0.00 1 4 15.47

Tender POT Discards w/ halibut PSC (Status quo) 105 0.01 7 29 6.50

TRW Blended: Discards w/ halibut PSC + Chinook PSC 2,427 0.73 835 2,733 34.42

HAL Blended: Discards w/ halibut PSC + Chinook PSC 2,231 0.23 242 1,184 10.86

POT Blended: Discards w/ halibut PSC + Chinook PSC 858 0.02 18 61 2.04

Tender TRW Blended: Discards w/ halibut PSC + Chinook PSC 259 0.02 16 68 6.02

Tender HAL Blended: Discards w/ halibut PSC + Chinook PSC 7 0.00 0 0 0.01

Tender POT Blended: Discards w/ halibut PSC + Chinook PSC 105 0.00 4 16 3.62
21

Table 3. -- Results of gap analyses by deployment design. G scores are the proportion of cells 

with at least a 50% chance of observing three (G3) or one (G1) trips during the year. 

G Relative is the G score for each allocation design divided by the maximum, where 

G relative equal to 1.00 represent the designs with the fewest predicted gaps in 

coverage. Allocations are listed in descending order by G3.

Allocation design G3 G3

Relative

G1 G1 

Relative

Gear Stratification

Equal Allocation 0.76 1.00 0.91 1.00

15% + Optimized on Discards + Halibut + Chinook PSC 0.76 1.00 0.91 1.00

15% + Optimized on Discards + Halibut PSC 0.76 1.00 0.91 1.00

Optimized on Discards + Halibut PSC 0.73 0.96 0.91 1.00

Optimized on Discards + Halibut + Chinook PSC 0.64 0.84 0.79 0.87

Gear × Tender Stratification

Equal Allocation 0.62 1.00 0.84 1.00

15% + Optimized on Discards + Halibut + Chinook PSC 0.62 1.00 0.84 1.00

15% + Optimized on Discards + Halibut PSC 0.62 1.00 0.84 1.00

Optimized on Discards + Halibut PSC 0.58 0.93 0.80 0.95

Optimized on Discards + Halibut + Chinook PSC 0.47 0.75 0.64 0.76
22

Table 4. -- The number of trips and associated likelihood of observing at least three trips within 

each NMFS Reporting Area and stratum combination in the Bering Sea and Aleutian 

Islands for each allocation design under the Gear-based stratification scheme. If the 

likelihood of observing at least three trips is less than 0.50, the cell is bolded in order 

to identify potential gaps more easily. The number of trips in an Area Stratum 

combination are not whole numbers since fishing trips can span more than one NMFS 

Reporting Area.

BSAI Gear Stratification

NMFS 

Area_Stratum Trips

Equal 

Allocation

15% + 

Optimized 

on Discards 

+ Halibut 

PSC

15% +

Optimized on 

Discards + 

Halibut + 

Chinook PSC

Optimized 

on Discards 

+ Halibut 

PSC

Optimized on 

Discards + 

Halibut + 

Chinook PSC

509_POT 129.0 1.00 1.00 1.00 0.94 0.56

509_TRW 133.7 1.00 1.00 1.00 1.00 1.00

513_HAL 5.4 0.06 0.06 0.05 0.07 0.02

514_HAL 11.2 0.31 0.31 0.28 0.35 0.12

517_HAL 5.4 0.06 0.06 0.05 0.07 0.02

517_POT 96.4 1.00 1.00 1.00 0.81 0.36

517_TRW 104.7 1.00 1.00 1.00 1.00 1.00

518_HAL 32.7 0.94 0.94 0.92 0.96 0.70

518_POT 18.6 0.65 0.58 0.57 0.05 0.01

519_HAL 21.3 0.74 0.74 0.70 0.78 0.41

519_POT 195.5 1.00 1.00 1.00 0.99 0.83

519_TRW 39.7 0.98 0.98 0.99 0.99 1.00

521_HAL 30.8 0.92 0.92 0.89 0.94 0.66

521_POT 0.5 0.00 0.00 0.00 0.00 0.00

523_HAL 4.2 0.03 0.03 0.03 0.04 0.01

524_HAL 12.0 0.35 0.35 0.32 0.40 0.13

541_HAL 76.3 1.00 1.00 1.00 1.00 0.99

541_POT 1.0 0.00 0.00 0.00 0.00 0.00

542_HAL 25.2 0.83 0.83 0.80 0.87 0.52

543_HAL 2.2 0.00 0.00 0.00 0.00 0.00
23

Table 5. -- The number of trips and associated likelihood of observing at least three trips within 

each NMFS Reporting Area and stratum combination in the Gulf of Alaska for each 

allocation design under the Gear-based stratification scheme. If the likelihood of 

observing at least three trips is less than 0.50, the cell is bolded in order to identify 

potential gaps more easily. The number of trips in an Area Stratum combination are 

not whole numbers since fishing trips can span more than one NMFS Reporting Area.

GOA Gear Stratification

NMFS 

Area_Stratum Trips

Equal 

Allocation

15% + 

Optimized on 

Discards + 

Halibut PSC

15% + 

Optimized on 

Discards + 

Halibut + 

Chinook PSC

Optimized 

on Discards 

+ Halibut 

PSC

Optimized on 

Discards + 

Halibut + 

Chinook PSC

610_HAL 192.3 1.00 1.00 1.00 1.00 1.00

610_POT 256.8 1.00 1.00 1.00 1.00 0.95

610_TRW 940.1 1.00 1.00 1.00 1.00 1.00

620_HAL 148.9 1.00 1.00 1.00 1.00 1.00

620_POT 90.0 1.00 1.00 1.00 0.77 0.32

620_TRW 503.2 1.00 1.00 1.00 1.00 1.00

630_HAL 764.6 1.00 1.00 1.00 1.00 1.00

630_POT 175.1 1.00 1.00 1.00 0.99 0.77

630_TRW 964.3 1.00 1.00 1.00 1.00 1.00

640_HAL 176.2 1.00 1.00 1.00 1.00 1.00

649_HAL 74.9 1.00 1.00 1.00 1.00 0.99

650_HAL 419.7 1.00 1.00 1.00 1.00 1.00

659_HAL 234.0 1.00 1.00 1.00 1.00 1.00
24

Table 6. -- The number of trips and associated likelihood of observing at least three trips within 

each NMFS Reporting Area and stratum combination in the Bering Sea and Aleutian 

Islands for each allocation design under the Gear ×Tender stratification scheme. If 

the likelihood of observing at least three trips is less than 0.50, the cell is bolded in 

order to identify potential gaps more easily. The number of trips in an Area Stratum 

combination are not whole numbers since fishing trips can span more than one NMFS 

Reporting Area.

BSAI Gear × Tender Stratification

NMFS 

Area_Stratum Trips

Equal 

Allocation

15% + 

Optimized 

on Discards 

+ Halibut 

PSC

15% + 

Optimized on 

Discards + 

Halibut + 

Chinook PSC

Optimized 

on Discards 

+ Halibut 

PSC

Optimized 

on Discards 

+ Halibut + 

Chinook 

PSC

509_POT 124.8 1.00 1.00 1.00 0.90 0.48

509_Tender_POT 4.2 0.03 0.02 0.02 0.00 0.00

509_Tender_TRW 1.5 0.00 0.00 0.00 0.00 0.00

509_TRW 132.2 1.00 1.00 1.00 1.00 1.00

513_HAL 5.4 0.06 0.06 0.05 0.07 0.02

514_HAL 11.2 0.31 0.31 0.28 0.35 0.12

517_HAL 5.4 0.06 0.06 0.05 0.07 0.02

517_POT 92.2 1.00 1.00 1.00 0.74 0.29

517_Tender_POT 4.2 0.03 0.02 0.02 0.00 0.00

517_Tender_TRW 0.5 0.00 0.00 0.00 0.00 0.00

517_TRW 104.2 1.00 1.00 1.00 1.00 1.00

518_HAL 32.7 0.94 0.94 0.92 0.96 0.70

518_POT 18.6 0.65 0.58 0.57 0.04 0.01

519_HAL 21.3 0.74 0.74 0.70 0.78 0.42

519_POT 194.6 1.00 1.00 1.00 0.99 0.79

519_Tender_POT 0.9 0.00 0.00 0.00 0.00 0.00

519_TRW 39.7 0.98 0.98 0.99 1.00 1.00

521_HAL 30.8 0.92 0.92 0.89 0.94 0.66

521_POT 0.5 0.00 0.00 0.00 0.00 0.00

523_HAL 4.2 0.03 0.03 0.03 0.04 0.01
25

Table 6. -- Continued.

BSAI Gear × Tender Stratification

NMFS 

Area_Stratum Trips

Equal 

Allocation

15% + 

Optimized on 

Discards + 

Halibut PSC

15% + 

Optimized on 

Discards + 

Halibut + 

Chinook PSC

Optimized 

on Discards 

+ Halibut 

PSC

Optimized on 

Discards + 

Halibut + 

Chinook PSC

524_HAL 12.0 0.35 0.35 0.32 0.40 0.14

541_HAL 76.3 1.00 1.00 1.00 1.00 0.99

541_POT 1.0 0.00 0.00 0.00 0.00 0.00

542_HAL 25.2 0.83 0.83 0.80 0.87 0.52

543_HAL 2.2 0.00 0.00 0.00 0.00 0.00
26

Table 7. -- The number of trips and associated likelihood of observing at least three trips within 

each NMFS Reporting Area and stratum combination in the Gulf of Alaska for each 

allocation design under the Gear ×Tender stratification scheme. If the likelihood of 

observing at least three trips is less than 0.50, the cell is bolded in order to identify 

potential gaps more easily. The number of trips in an Area Stratum combination are 

not whole numbers since fishing trips can span more than one NMFS Reporting Area.

GOA Gear × Tender Stratification

NMFS 

Area_Stratum Trips

Equal 

Allocation

15% + 

Optimized 

on Discards 

+ Halibut 

PSC

15% + 

Optimized on 

Discards + 

Halibut + 

Chinook PSC

Optimized 

on Discards 

+ Halibut 

PSC

Optimized 

on Discards 

+ Halibut + 

Chinook 

PSC

610_HAL 192.3 1.00 1.00 1.00 1.00 1.00

610_POT 191.1 1.00 1.00 1.00 0.99 0.78

610_Tender_POT 65.6 1.00 1.00 1.00 0.91 0.47

610_Tender_TRW 250.5 1.00 1.00 1.00 1.00 1.00

610_TRW 689.6 1.00 1.00 1.00 1.00 1.00

620_HAL 148.9 1.00 1.00 1.00 1.00 1.00

620_POT 70.8 1.00 1.00 1.00 0.56 0.17

620_Tender_POT 19.2 0.70 0.64 0.61 0.11 0.02

620_Tender_TRW 5.3 0.06 0.06 0.05 0.02 0.00

620_TRW 497.9 1.00 1.00 1.00 1.00 1.00

630_HAL 764.6 1.00 1.00 1.00 1.00 1.00

630_POT 164.3 1.00 1.00 1.00 0.97 0.68

630_Tender_POT 10.8 0.30 0.25 0.24 0.02 0.00

630_Tender_TRW 1.0 0.00 0.00 0.00 0.00 0.00

630_TRW 963.3 1.00 1.00 1.00 1.00 1.00

640_HAL 176.2 1.00 1.00 1.00 1.00 1.00

649_HAL 74.9 1.00 1.00 1.00 1.00 0.99

650_HAL 419.7 1.00 1.00 1.00 1.00 1.00

659_HAL 227.5 1.00 1.00 1.00 1.00 1.00

659_Tender_HAL 6.6 0.00 0.00 0.00 0.00 0.00
27

Figure 1. -- Flow chart depicting methods used in this analysis for each allocation and 

stratification design under consideration for the 2018 ADP. Blocks highlighted in 

bold are new methods this year.
28

Figure 2. -- The distribution of trip duration in days (top panels), discarded groundfish catch 

including Pacific halibut PSC in metric tons (middle panels), and Chinook PSC in 

counts (bottom panels) for each stratum in the Gear and Gear × Tender stratification 

schemes. Shaded boxes denote the 25th, 50th, and 75th percentiles, and individual 

trips are shown as open circles.
29

Figure 3. -- Comparison of preliminary draft coverage rates resulting from two stratification 

schemes (Gear and Gear × Tender) and three allocation designs (Equal Allocation, 

15% + Optimized, and Optimized). Metrics used for optimization included discards 

with Pacific halibut prohibited species catch (PSC) (teal) and a blended optimization 

of discards with Pacific halibut and Chinook PSC (blue). Rates in the top panels are 

shown in black because no optimization occurred. Error bars depict uncertainty 

(+/- 1 standard deviation) in predicted coverage rates caused by the fact that the 

population of hook-and-line (HAL) and pot gear (POT) vessels in the partial 

coverage category is not defined in the Draft 2018 ADP due to open enrollment into 

Electronic Monitoring.
30

Figure 4. -- Empirical cumulative distribution curves for the probability of observing at least 

three trips in a domain defined by NMFS Area and stratum for two stratification 

schemes (Gear and Gear × Tender) and three allocation designs (Equal Allocation, 

15% + Optimized, and Optimized). Metrics used for optimization included discards 

with Pacific halibut prohibited species catch (PSC) (teal) and a blended optimization 

of discards with Pacific halibut and Chinook PSC (blue). Curves in the top panels 

are shown in black because no optimization occurred. Better performing designs are 

those that reach a value of 1 furthest to the left of the plot. The shaded regions 

around the curves reflect uncertainty in the gap analyses caused by the fact that the 

population of hook-and-line and pot gear vessels in the partial coverage category is 

not defined due to open enrollment into Electronic Monitoring.
RECENT TECHNICAL MEMORANDUMS

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

(web site: www.ntis.gov). Paper and electronic (.pdf) copies vary in price. 

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361 MCCONNAUGHEY, R. A., K. E. BLACKHART , M. P. EAGELTON , and J. MARSH. 2017. 

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360 TURNER, K., C. N. ROOPER, E. A. LAMAN, S. C. ROONEY, D. W. COOPER, and M. 

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359 RODGVELLER, C. J., P. W. MALECHA, and C. R. LUNSFORD. 2017. Long-term survival and 

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