NOAA Technical Memorandum NMFS-PIFSC-51

March 2016 

Stock Assessment Updates of the Bottomfish 

Management Unit Species of American Samoa, the 

Commonwealth of the Northern Mariana Islands, and 

Guam in 2015 Using Data through 2013

Annie Yau, Marc Nadon, Benjamin Richards, Jon Brodziak, 

Eric Fletcher

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

National Oceanic and Atmospheric Administration

U.S. Department of Commerce 

 doi:10.7289/V5PR7T0G
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following format:

Yau, A., M. O. Nadon, B. L. Richards, J. Brodziak, and E. Fletcher. 

2016. R Stock Assessment Updates of the Bottomfish Management Unit 

pecies of American Samoa, the Commonwealth of the Northern Mariana 

Islands, and Guam in 2015 Using Data through 2013. U.S. Dep. Commer., 

NOAA Tech. Memo., NOAA-TM-NMFS-PIFSC-51, 54p. 

__________________________ 

For further information direct inquiries to

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Pacific Islands Fisheries Science Center

National Marine Fisheries Service

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Phone: 808-725-5331 

Fax: 808-725-5532 

___________________________________________________________ 

 doi:10.7289/V5PR7T0G
Pacific Islands Fisheries Science Center 

National Marine Fisheries Service

National Oceanic and Atmospheric Administration

U.S. Department of Commerce

Stock Assessment Updates of the Bottomfish 

Management Unit Species of American Samoa, the 

Commonwealth of the Northern Mariana Islands, and 

Guam in 2015 Using Data through 2013

Annie Yau1

Marc Nadon2 

Benjamin Richards1

Jon Brodziak1

 

Eric Fletcher1

1

Pacific Islands Fisheries Science Center

National Marine Fisheries Service

1845 Wasp Boulevard 

Building 176 

Honolulu, Hawaii 96818 

2

Joint Institute for Marine and Atmospheric Research

University of Hawaii

1000 Pope Road 

Honolulu, Hawaii 96822 

NOAA Technical Memorandum NMFS-PIFSC-51

March 2016

 doi:10.7289/V5PR7T0G

iii

ABSTRACT

In this report, we conduct a strict stock assessment update of the Bottomfish Management Unit 

Species (BMUS) complexes in American Samoa, the Commonwealth of the Northern Mariana 

Islands, and Guam, using the same base case production model as used in the previous stock 

assessment (Brodziak et al., 2012), but with an additional 3 years of catch and nominal catchper-unit-effort (CPUE) as input data. A Bayesian statistical framework is applied to estimate 

parameters of a Schaefer production model fit to a time series of annual nominal CPUE statistics 

to provide direct estimates of parameter uncertainty for status determination. The surplus 

production model is a state-space model, including both process error in biomass production 

dynamics and observation error in the catch-per-unit effort data. Overall, the American Samoa, 

the Commonwealth of the Northern Mariana Islands, and the Guam bottomfish complexes were 

not overfished (overfished is defined as B < 0.7*BMSY) and were not experiencing overfishing 

(overfishing is defined as H > HMSY) in 2013, the most recent year of the stock assessment 

estimates.

We conducted stock projections for 2016 and 2017, which projected a range of hypothetical twoyear catches and calculated corresponding future risks of overfishing. For the American Samoa 

BMUS complex, the 2017 catch level that would produce a 50% risk of overfishing in 2017 was 

115 thousand pounds. For the Commonwealth of the Northern Mariana Islands BMUS complex, 

the 2017 catch level that would produce a 50% risk of overfishing in 2017 was 250 thousand

pounds. For the Guam BMUS complex, the 2017 catch level that would produce a 50% risk of 

overfishing in 2017 was 71 thousand pounds. All of these catch values associated with a 50% 

risk of overfishing in 2017 are much higher than actual bottomfish landings in 2013 for 

American Samoa, the Commonwealth of the Northern Mariana Islands, and Guam which were 

approximately 24 thousand, 23 thousand, and 30 thousand pounds, respectively.
iv
v

CONTENTS

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

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

Description of the Fisherires......................................................................................................2

Methods............................................................................................................................................3

Results............................................................................................................................................11

Discussion......................................................................................................................................18

Acknowledgement .........................................................................................................................19

Literature Cited ..............................................................................................................................19

Tables ............................................................................................................................................22

Figures............................................................................................................................................35

Appendices.....................................................................................................................................59
vi
1

INTRODUCTION

Deep-slope finfish resources are found around all central and western Pacific Islands and reefs 

where they support small vessel hook and line fisheries. The Western Pacific Regional Fishery 

Management Council manages these resources within the US Exclusive Economic Zone (EEZ) 

surrounding American Samoa, the Commonwealth of the Northern Mariana Islands (CNMI), and 

Guam under the Archipelagic Fishery Ecosystem Plans (FEPs) for American Samoa and the 

Marianas. The set of Bottomfish Management Unit Species (BMUS) identified within the FEP is 

comprised of 19 species of snappers, groupers, emperors, and jacks, 17 of which are found in the 

western Pacific (Table 1). Bottomfish resources are managed around each territory as one multispecies complex. These multi-species stocks are managed as a unit straddling both territorial and 

federal waters. Although managed as a multi-species stock, in the western Pacific, the BMUS 

can be further divided, albeit with considerable overlap, into shallow and deep components. In 

Hawaii, species of the shallow component (e.g., lethrinids and Lutjanus spp) are largely lacking 

compared to American Samoa, CNMI, and Guam. Amendment 6 of the FEP establishes 

methods for determining fishing mortality and stock biomass reference values and, by a 

comparison of current conditions to the reference values, determining if the stock is being 

overfished and if overfishing is occurring. Overfished is defined as the stock biomass B falling 

below the Minimum Stock Size Threshold (MSST) of (1-M)*BMSY, where M is the natural 

mortality rate and BMSY is the biomass that produces the maximum sustainable yield. In the 

previous assessment, M was defined as 0.30, so the overfished definition is defined as B <

0.7*BMSY. Overfishing is defined as a fishing/harvest rate H that exceeds the Maximum 

Fishing Mortality Threshold (MFMT) of HMSY, the harvest rate that produces maximum 

sustainable yield. The fishery status with respect to these criteria is reported to Congress 

annually and mandatory management measures are required when overfishing or overfished 

thresholds are breached according to the Magnuson-Stevens Fishery Conservation and 

Management Act. These status determinations are applicable to the multi-species stocks as a 

whole and not to their shallow and deep components separately.

In this report, we update the status of BMUS complexes of American Samoa, CNMI, and Guam 

using the same production model as was used in the previous stock assessment (Brodziak et al., 

2012, Moffitt et al., 2007). The production model relies on fishery-dependent data collected by 

territorial agencies (American Samoa Department of Marine and Wildlife Resources, CNMI 

Division of Fish and Wildlife, and Guam Division of Aquatic and Wildlife Resources) and 

shared with the Western Pacific Fisheries Information Network (WPacFIN). Currently, there are 

no fishery-independent measures of relative or absolute bottomfish abundance. The surplus 

production model is a state-space model that includes both process error in biomass production 

dynamics and observation error in the catch-per-unit effort data. We calculate status 

determinations resulting from the production model and stock projection results.
2

Description of the Fisheries

American Samoa

Prior to European contact, indigenous fishers of the Samoan Islands fished for subsistence from 

canoes using pearl shell hooks and sennit lines. They caught many fish species including some 

BMUS. By the 1950s, the Samoa fleet had adopted small boats equipped with outboard engines 

and fished with steel hooks and monofilament lines, but the fishery remained for subsistence 

only. Surveys conducted in the late 1960s by the American Samoa Office of Marine Resources 

revealed substantial deep bottomfish resources around the island of Tutuila, and by the early 

1970s a small commercial fishery was established. In an attempt to develop local fisheries, 2

subsidized boat building programs, the dory program in the 1970s and the alia program in the 

1980s, provided fishers with low cost vessels. The bottomfish fleet expanded in the mid-1980s 

with a government subsidized project aimed at exporting deep-water snappers to Hawaii (Itano 

1996). At the fishery’s peak in 1984, 48 vessels fished for bottomfish. Declines in participation 

in this fishery can be attributed to shifts in the importance of bottomfish fishing compared to 

trolling and longlining for pelagic species and to the periodic impact of hurricanes. In 1987, for 

example, hurricane Tusi damaged or destroyed a large segment of the small boat fishing fleet. In 

2005, a total of 16 part-time vessels participated in the bottomfish fishery (WPRFMC, 2006). 

Most vessels are small aluminum alia catamarans (< 30 ft) with low-tech fishing practices (e.g., 

no depth sounders, electric or hydraulic reels, global positioning systems, or ice chilling 

capability) (WPRFMC, 2006). In recent years, however, a number of larger (> 35 ft) vessels 

with higher technological capability have been entering the fishery (WPRFMC, 2006). As in 

Guam, during the period 1986-2005, fishing effort (in line hours) spent targeting the shallow 

bottomfish component was nearly double that spent on the deep component.

Commonwealth of the Northern Mariana Islands

The CNMI consists of a series of islands in the long Mariana Islands chain, excluding Guam, 

extending approximately 500 nm in a north- south direction, paralleled by a chain of seamounts 

about 150 nm to the west. Most of the fishing activity occurs around the population centers of 

Rota, Tinian, and Saipan and extends north to Zealandia Bank, approximately 120 nm north of 

Saipan. In 2005, a total of 62 vessels ranging in size from small skiffs to boats 70 feet in length 

reported commercial catches of bottomfish. It is likely, however, that in addition to commercial 

fishing many more small skiffs conduct bottomfishing for subsistence. The shallow BMUS 

component, dominated by Lethrinus rubrioperculatus, is fished both commercially and for 

subsistence with most fishing trips made by small vessels using handlines or homemade hand or 

electric reels and lasting a single day. In contrast, the deep BMUS component is fished primarily 

commercially. In the late 1980s to early 1990s there were 12-15 large vessels (~ 70 ft) on Saipan 

that would fish around Saipan but also to the Northern Islands, while currently the fishing effort 

includes two large vessels that fish seasonally. The larger vessels conduct multi-day trips and 

employ electric or hydraulic reels.
3

Guam

Guam is the largest and southernmost of the Mariana Islands. In Guam, bottomfish are caught by 

a combination of recreational, subsistence, and small-scale commercial fishing operations. In 

2005, a total of 233 vessels were reported to participate in bottomfishing activities. Most of the 

fleet consists of vessels less than 25 ft in length that target the shallow species components 

around Guam for recreational or subsistence purposes. Some recreational vessels (< 25 ft) also 

target the deep component at the offshore banks and other areas offshore of Guam where deep 

bottomfish habitat occurs. Larger vessels (> 25 ft) fishing commercially target the deep species 

components at offshore banks (e.g., Galvez and Santa Rosa Banks to the south and Rota Bank to 

the north). From 1982 to 2005, the fishing effort exerted on the shallow component was nearly 

double that spent on the deep component.

METHODS

In all 3 territory areas, creel surveys are used to collect fishery data by territorial agencies and 

then passed to the Western Pacific Fisheries Information Network. Participation in the surveys 

by the fishers is voluntary. Survey coverage and quality of data collected vary both by location 

and over time. The current American Samoa Offshore Creel Survey was initiated in October 

1985, and records landings and effort of commercial, recreational, and subsistence fishers. The 

CNMI creel survey is a more recent program, with data available starting in 2000. Prior to the 

creel survey, data were collected through the voluntary Commercial Purchase Database program, 

which provided data starting in 1983. Under this program, first-level purchasers of local fresh 

fish provided records of purchases by species categories that did not necessarily correspond to 

BMUS. Guam has been collecting voluntary fishery creel data since the late 1960s, although 

only boat-based creel data collected since 1982 are being used for analysis. Data collected prior 

to 1982 are not as extensive as required to apply the expansion algorithm used in the current 

database program, although efforts to incorporate species composition data and CPUE data 

(catch-per-unit-effort) for years prior to 1982 are ongoing. Collection of bottomfish catch data 

from the east side of the island is hampered by logistical problems and lack of voluntary 

reporting. The east side of the island is heavily fished for both shallow and deep bottomfish 

species during the calmer summer months. The current statistical expansion of fishery data, 

however, adjusts for this to the extent possible. 

For each territory, catch data from the surveyed subset of fishing trips are expanded to estimate 

total catch for the territory. This assessment uses data on catch and effort from each territory to 

calculate total BMUS catch and CPUE for each territory. While there are other datasets available 

such as the WPacFIN biosampling program and the federal permit logbook dataset of catch and 

effort for bottomfishing, those data were not considered for use in this stock assessment because 

it was conducted as a strict update. Future benchmark stock assessments will consider alternative 

and additional data sources. However, investigation of the federal logbook dataset of catch and 

effort for bottomfishing in each of the territories revealed that the data are sparse, starting in 

2009 with only 0-5 permit holders reporting annually. Until there is a higher reporting rate, this 

dataset is likely not useful for future stock assessment purposes. 
4

We will estimate BMSY by using independent estimates of MSY-level landings reported in Our 

Living Oceans report (OLO) (Humphreys and Moffitt, 1999; Moffitt and Humphreys, 2009), 

which used methods developed by Polovina and Ralston (1986). Determinations of overfishing 

and overfished status can then be made by comparing current biomass and harvest rates to MSYlevel reference points. In accordance with the FEP, these status determinations are made for the 

multi-species BMUS stock as a whole for each territory, and not for their deep and shallow 

components separately.

Calculating Catch

Catch is calculated using the same methods employed in the previous assessment (Brodziak et 

al., 2012) and further described below.

For American Samoa, we first selected catch of all BMUS species listed in Table 1, using 

expanded species annual catch files from WPacFIN. We then divided catch from miscellaneous 

species groupings (categories such as “grouper”, “snapper”, and “bottomfish”) into BMUS and 

non-BMUS portions using a ratio of 75% BMUS to non-BMUS, as determined in the previous 

assessment, based on data from years with the most extensive reporting of species composition 

statistics. Total annual BMUS catch for American Samoa was calculated as the sum of expanded 

catch for all BMUS species and the estimated BMUS portion of expanded catch for 

miscellaneous bottomfish groupings. We calculated annual catch of bottomfish in American 

Samoa for 2006-2013, and compared catch estimates from 2006 to 2010 to catch estimates from 

the previous assessment to double check against current methods.

For CNMI, the previous assessments used commercial purchase data instead of creel survey data 

to calculate catch because of the shorter duration of creel survey data availability. In these data, 

landings are for all species caught with bottomfishing gear and include more species than those 

included in BMUS (Table 1). This commercial purchase dataset was again used to calculate 

annual BMUS catch in CNMI. We calculated annual catch of bottomfish in CNMI for 2006-

2013, and compared catch estimates for 2006-2010 to catch estimates from the previous 

assessment to double check against current methods.

For Guam, we used the expanded species annual catch files from WPacFIN to extract total 

annual catch of all BMUS species listed in Table 1. We calculated annual catch of bottomfish in 

Guam for 2006-2013, and compared catch estimates from 2006 to 2010 to catch estimates from 

the previous assessment to double check against current methods.

Calculating CPUE

Nominal CPUE is also calculated using the same methods employed in the previous assessment 

(Brodziak et al., 2012) and described here. For American Samoa and Guam, we used the 

WPacFIN non-expanded interview data on catch and effort, and calculated annual nominal 

CPUE in units of lbs/line hour. For CNMI, we used the WPacFIN commercial purchase dataset 

and calculated annual nominal CPUE in units of lbs/trip. 

For each territory except CNMI, we first selected trips where 50% or more of the catch was 

BMUS. In CNMI, we selected trips that reported using bottomfishing gear. Then we calculated 
5

the pounds of BMUS species (Table 1) caught in each of these bottomfishing trips, and divided 

this catch by the total effort reported for each trip (line hours for American Samoa and Guam, 

and trip for CNMI). We calculated annual nominal CPUE of bottomfish for each territory for 

2006-2013, and compared catch estimates from 2006 to 2010 to catch estimates from the 

previous assessment to ground-truth current methods. The catch time series are nominal and have 

not been standardized, so they do not account for factors other than changes in stock abundance 

that may be influencing CPUE.

Production Model Assessment Method

The bottomfish surplus production model used in this report is a state-space model with explicit 

process and observation error terms (see Meyer and Millar, 1999). This Bayesian model has been 

used in some groundfish assessments where more complex assessment approaches were not 

successful due to limited data or other factors (see, for example, Brodziak et al., 2001; Brodziak 

et al., 2011). In this approach, the unobserved biomass states are estimated from the observed 

relative abundance indices (CPUE) and catches based on an observation error likelihood function 

and prior distributions for model parameters (θ). The observation error likelihood measures the 

discrepancy between observed and model predictions of CPUE.

The process dynamics are based on a Schaefer surplus production model with an annual time 

step and a time horizon of N years. Under this 2-parameter model, current biomass (BT) depends 

on the previous biomass, catch (CT-1), the intrinsic growth rate (r) and carrying capacity (K) for T

= 2, ..., N as

(1) 1

1

1 1 1 − − − −  −



 



 = + − T

T

T T T C

K

B B B rB

Maximum surplus production occurs when biomass is equal to 1⁄2 of K. The values of biomass 

and harvest rate that maximize surplus production are relevant for fishery management; the 

biomass that maximizes surplus production is BMSY=K/2. The corresponding harvest rate that 

maximizes surplus production is HMSY=r/2 and the maximum surplus production is MSY =

rK/4.

The production model can be reparameterized by considering the ratio (or proportion) of stock 

biomass to carrying capacity (P=B/K) to improve the efficiency of the Markov Chain Monte 

Carlo estimation algorithm. Given this parameterization, the process dynamics are

(2) ( ) K

C P P rP P T

T T T T

1

1 1 1 1 − = − + − − − −

The process dynamics are subject to natural variation due to fluctuations in life history 

parameters, trophic interactions, environmental conditions, and other factors. In this context, the 

process error can be assumed to represent the joint effect of a large number of random 

multiplicative events which combine to form a multiplicative lognormal process under the 

Central Limit Theorem. Given this assumption, the process error terms are independent and 
6

lognormally distributed random variables UT T η = e where the UT are normal random variables

with mean 0 and variance σ

2

.

The state equations define the stochastic process dynamics by relating the unobserved biomass 

states to the observed catches and the population dynamics parameters. Given the lognormal 

process error assumption, the state equations for the initial time period T = 1 and subsequent 

periods T > 1 are

(3) ( ) T

T

T T T T K

C P P rP P

P

η

η





 



 = + − −

=

− − − − 1

1 1 1

1 1

1

These equations set the prior distribution for the ratio of biomass to carrying capacity, p(PT), in each 

time period T, conditioned on the previous proportion.

Observation Error Model

There are two components to the observation error model. The first component relates the 

observed fishery CPUE to the biomass of the bottomfish complex. Here it will be assumed that 

the CPUE index (I) is proportional to biomass with catchability coefficient Q:

(4) T QBT QKPT I = =

The observed CPUE dynamics are also subject to sampling variation which is assumed to be 

lognormally distributed. The observation errors are T e T ν ν = where the νT are iid normal random 

variables with mean 0 and variance τ

2

. Given this, the observation equations for T = 1,..., N are

(5) T QKPT T I = ν

This specifies the CPUE observation error likelihood function p(IT|θ) for each period.

The second component of the observation error model relates to previously developed estimates 

of the maximum sustainable yield for the Guam, American Samoa, and CNMI bottomfish 

complexes to the model parameters r and K. Published estimates of MSY based on research 

conducted in the Marianas (Polovina et al., 1985), and extended to include American Samoa, are 

found in the Our Living Oceans report (OLO) by Humphreys and Moffitt (1999) and Moffitt and

Humphreys (2009). The methods used to estimate MSY are described in Polovina and Ralston 

(1986), and are a fishery-independent estimate which combines life history assumptions (von 

Bertalanffy growth, constant natural mortality, and constant recruitment) with data on lengthfrequency, CPUE, and an estimate of catchability from an intensive fishing experiment. The 

results are extrapolated along pre-determined isobaths for each territory. 

These OLO MSY estimates are 75,000 pounds, 172,000 pounds, and 55,000 pounds respectively 

for American Samoa, CNMI, and Guam. Each MSY estimate (MSYOBS) is taken to be a data 

point and compared to the prediction of the MSY value (MSYPRED) for each territory. The 
7

predicted MSY value is a function of r and K with MSYPRED = rK/4. The observation error for 

the MSY value is assumed to be W ω = e where W is a normal random variable with mean 0 and

variance w

2

. Given this, the observation equation for the MSY data is

(6) ω

4

rK MSYOBS =

This specifies the MSY observation error likelihood function p(MSY| θ). Given this, the product 

of the CPUE error likelihood and the MSY observation error likelihood is the complete 

observation error model.

Prior Distributions

To use this Bayesian approach, prior distributions are needed to quantify existing knowledge, or 

the lack thereof, for each parameter and the unobserved biomass state. The model parameters 

consist of the carrying capacity, intrinsic growth rate, catchability, the process and observation 

error variances, and ratio of initial biomass to carrying capacity. The unobserved states are the 

ratios of biomass to carrying capacity, PT, for T > 1, each conditioned on the previous 

proportion. Table 2 contains a summary of the assumed prior distribution values for all 

parameters used, and a detailed description of each prior follows.

Prior for Carrying Capacity

The prior distribution for the carrying capacity p(K) of bottomfish for each territory was chosen 

to be a diffuse (noninformative) normal distribution with mean (μK) variance (σ2

K) parameters:

(7) 













 − = − 2

2

2

( ) exp 21 ( )

K

K

K

K

p K

σ

μ

pσ

Initial mean values of the K parameters for each area were 700 thousand, 1400 thousand, and 

300 thousand pounds for American Samoa, CNMI, and Guam respectively, based on the base 

case values of the previous stock assessment (Brodziak et al., 2012) which explored alternative 

starting values for K before choosing base case values based on goodness-of-fit. The coefficient 

of variation of K was set to 20% for each territory to allow for a range of fitted carrying capacity 

estimates. 

Prior for Intrinsic Growth Rate

The prior distribution for intrinsic growth rate p(r) was chosen to be a beta distribution with 

parameters c and d:

(8) ( 1) ( 1) (1 ) ( ) ( )

( ) ( ) − − − Γ ΓΓ + = c d x x

c d

c d

p r
8

This choice constrained the intrinsic growth rate estimate to be within the interval [0, 1] which 

was considered to be a reasonable range given the life history of species in the bottomfish 

complexes. The values of c and d were chosen to produce a mean of μr = 0.46 for each territory, 

with a coefficient of variation of 50%. This mean value is the same for all territories and was 

used in the base case model in the previous stock assessment (Brodziak et al., 2012) based on r 

values estimated in an earlier stock assessment (Moffitt et al., 2006). This prior for intrinsic 

growth rate was moderately informative and allowed for variation about the mean value.

Prior for Catchability

The prior for catchability p(Q) in each territory was chosen to be a diffuse (noninformative)

inverse-gamma distribution with scale parameter λ and shape parameter k.

(9) 













 −

Γ =

− +

k Q

Q

p Q

k k λ λ

exp ( ) ( )

( 1)

The scale and shape parameters were set to be λ = k = 0.001. This choice of parameters gives the 

inverse of Q a mean of 1 and a variance of 1000. As a result, the prior for catchability is 

approximately p(Q) is proportional to Q-1

. Since 1/Q is unbounded at Q = 0, the MCMC sampler 

was constrained to ensure that Q was in the interval [10-5

, 105].

Priors for Error Variances

Priors for the process error variance p(σ2) and observation error variance p(τ2) for each territory 

were chosen to be moderately informative inverse-gamma distributions with scale parameter λ >

0 and shape parameter k > 0:

(10) ( )

( ) exp

( )

2

2 ( 1)

2

k p

k k

Γ





 



 −

=

− −

σ

λ λ σ

σ

The inverse-gamma distribution is a useful choice for priors that describe model error variances 

(see, for example, Congdon, 2001). The scale parameter was set to λ = 0.1 and the shape 

parameter was k = 0.2 for the process error variance prior. For this choice of parameters, the 

expected value of the inverse-gamma distribution is not bounded, and we used the mode for σ2

, 

denoted as MODE[σ2] = 1/12 ≈ 0.083 to measure the central tendency of the distribution. For the 

observation error variance prior, the scale parameter was set to λ = 1 and the shape parameter 

was k = 0.2. As a result, the mode of τ2 was MODE[τ2] = 10/12 ≈ 0.83. The ratio of the modes of 

the observation error prior to the process error prior was MODE[τ2]/MODE[σ2] = 10 and the 

central tendency of the observation error variance prior was assumed to be about tenfold greater 

than the process error variance prior. The choice of the process error prior matched the expected 

scaling of process errors which were on the order of 0.1 for the state equations describing 

changes in the proportion of carrying capacity. Similarly, the choice of the observation error 

prior matched the expected scaling of observation errors which were on the order of 1 to 10 for 
9

the observation equations describing the model fit to the observed CPUE. In summary, the prior 

for the observation error variance was assumed to be an order of magnitude greater than the 

process error variance.

Priors for Ratios of Biomass to Carrying Capacity

The prior distributions for the time series of the ratio of biomass to carrying capacity, p(PT), were 

determined by the lognormal distributions for the process error dynamics. The priors for the 

initial proportion of carrying capacity P[1] in each territory were the same as in the base case 

assessment model from the previous assessment (Brodziak et al., 2012). For American Samoa, 

CNMI, and Guam, the P[1] prior was modeled as a lognormal distribution with mean μP[1] set to 

0.80, 0.45, and 0.75 respectively, and a CV of 20% for each territory: 

(11) 













 − = − 2

[1]

2

[1]

[1] 2

(log( [1] )

exp 2 [1]

1 ( [1])

P

P

P

P

P p P

σ

μ

p σ

Posterior Distribution

The posterior distribution was calculated to make inferences about the model parameters given 

the data, the likelihood, and the priors. In particular, the joint posterior distribution given catch, 

MSY, and CPUE data D, p(θ|D), was proportional to the product of the priors and the 

observation error likelihood:

(12) ∏ ∏ = =

∝

N

T

N

T

p D p K p r p Q p p p PT p IT p MSY

1 1

2 2 (θ ) ( ) ( ) ( ) (σ ) (τ ) ( ) ( θ ) ( θ )

There was no closed form expression to calculated parameter estimates from the posterior 

distribution and we used standard methods to numerically simulate samples from the posterior 

distribution.

Bayesian parameter estimation for multi-parameter nonlinear models, such as the bottomfish 

production model, is typically based on simulating a set of independent samples from the 

posterior distribution. For the production model, we used Markov Chain Monte Carlo (MCMC) 

simulation (Gilks et al., 1996) to numerically generate a sequence of samples from the posterior 

distribution. The WINBUGS software (version 1.4, Spiegelhalter et al., 2003) was called up 

using the R Language (R Development Core Team, 2009) and R2WinBUGS package (Sturtz et 

al., 2005) to set the initial conditions, perform the MCMC calculations, and summarize the 

MCMC results. Generic code for running this Bayesian state-space surplus production model is 

provided in Appendix A, and input data files for each territory are provided in Appendix B with 

the same data also provided in Table 3. Note that due to the nature of MCMC sampling, it is not 

possible to replicate results exactly, but results should be fairly close. 

MCMC simulations were conducted in an identical manner for each of the models used for each 

territory. Three chains of 260,000 samples were simulated in each model run. The first 10,000 
10

samples of each chain were excluded from the inference process as a burn-in period to remove

any dependence of the MCMC samples on the initial conditions. Each chain was also thinned by 

25 to remove autocorrelation; every twenty-fifth sample was used for inference. As a result, there 

were 30,000 samples from the posterior for summarizing model results. 

Model and Convergence Diagnostics

Convergence of the MCMC simulations to the posterior distribution was checked using the 

Brooks-Gelman-Rubin (BGR) convergence diagnostic (Brooks and Gelman, 1998). This 

diagnostic was monitored using WinBUGS for key model parameters (intrinsic growth rate, 

carrying capacity, catchability, initial ratio of biomass to carrying capacity, process and 

observation error variances) with values near unity indicating convergence. Convergence of the 

MCMC samples to the posterior distribution was also checked using the Gelman and Rubin 

(1992), Geweke (1992), and Heidleberger and Welch (1992) diagnostics as implemented in the R 

language (R Development Core Team 2014) and the CODA package (Plummer et al., 2006).

Residuals from the model fits to CPUE were used to determine the goodness-of-fit of the 

production models. Any non-random patterns in the CPUE residuals could indicate that the 

observed CPUE may not conform to one or more model assumptions. Model residuals were 

tested for significant time trends. Residuals for the CPUE series are the log-scale observation 

errors εT:

(13) ln( ) ln( ) T T QKPT ε = I −

Projection Methods

Stock projections were conducted to provide information on the risk that each territorial 

Bottomfish Management Unit Species complex would experience overfishing and/or become 

overfished under alternative future catches in fishing years 2016-2017. The stock projections 

started with the model-estimated parameters and 2013 stock biomass for each territory, and in 

this way they included uncertainty in the distribution of model-estimated parameters. To bridge 

the gap between 2013, the most recent year of the assessment, and the projected future years of 

2016 and 2017, we assumed that the 2014-2015 harvest rate for each territory was equal to a 

normal distribution with a mean and standard deviation calculated from the model-estimated 

harvest rates from 2011-2013, and randomly drew from this normal distribution during the 

MCMC iterations. The projections included process error. 

For the 2016-2017 projections, we used a grid of possible future catches ranging from 0 to 

600,000 pounds in increments of 1,000 pounds. Each projection was sampled using the same 

setup as for the base case models: three chains of 260,000 samples were simulated, with the first 

10,000 samples of each chain excluded as a burn-in period and each chain thinned by 25 to 

remove autocorrelation. As a result, there were 30,000 samples from the posterior for each 

territory’s projection for summarizing model results. 

In the projections, we assumed that the 2016 catch was equal to the 2017 catch. To translate the 

projection results for management purposes, it is helpful to consider that these projected catch 
11

levels are equal to the ACLs and that 100% of the ACLs are caught. For each territory’s 

projection we calculated the biomass, harvest rate, and risks of overfishing and being overfished, 

under each alternative future catch. 

RESULTS

Input Data: Catch and CPUE

Fishery-dependent catch and effort data for assessing the bottomfish complexes were tabulated 

using the most recent and best available data from the Western Pacific Fisheries Information 

Network (WPacFIN). The processed data for American Samoa, CNMI, and Guam was finalized 

for use in the stock assessment update on 12 March 2015.

American Samoa

We compared estimates of American Samoa bottomfish catch from the previous 2012 

assessment and the 2015 assessment update for the years 2006-2010 and found that they were 

generally similar (Fig. 1), indicating methods used to calculate catch were similar. As a result, 

for this assessment we retained the catch time series from 1986 to 2010 used in the previous 

assessment and added new years of catch data for 2011-2013. The annual bottomfish catch used 

in the current assessment update averaged approximately 24,000 pounds during 1986-2013 and 

ranged from 7,913 to 64,587 pounds with a coefficient of variation of about 53% (Table 3, Fig. 

2). Recent average yield (2011-2013 average) for American Samoa bottomfish was 

approximately 21,000 pounds.

Estimates of American Samoa bottomfish CPUE (lbs/line hr) were calculated using the same 

approach as used in the 2012 assessment. We compared CPUE estimates from the previous 

assessment and the 2015 assessment update for the years 2006-2010, and found the general trend 

and magnitude in CPUE was similar (Fig. 1). As a result, for this assessment we retained the 

catch time series from 1986 to 2010 used in the previous assessment, and added new years of 

CPUE data for 2011-2013. This choice should have a negligible impact on the assessment results 

because the differences in CPUE are small, and production model fit to CPUE often averages out 

fluctuations (see Base Case Model Fit to CPUE section in Results, and Figs. 7-8). Bottomfish 

CPUE fluctuated around its long-term average of 3.83 lbs/line hr during 1986-2013 (Table 3, 

Fig. 2) and ranged from 2.44 to 6.53 lbs/line hr with a CV of 30%. Recent average CPUE from 

2011 to 2013 was 4.65 lbs/line hr.
12

Commonwealth of the Northern Mariana Islands

We compared estimates of the Commonwealth of the Northern Mariana Islands bottomfish catch 

from the previous 2012 assessment and the 2015 assessment update for the years 2006-2010 and 

found that they were generally similar (Fig. 3), indicating methods to calculate catch were 

similar. As a result, for this assessment we retained the catch time series from 1983 to 2010 used 

in the previous assessment, and added new years of catch data for 2011-2013. The annual 

bottomfish catch used in the current assessment update averaged approximately 38,221 pounds

during 1983-2013 and ranged from 7,092 to 71,256 pounds with a coefficient of variation of 

about 47% (Table 3, Fig. 4). Recent average yield (2011-2013 average) for CNMI bottomfish 

was approximately 20,100 pounds.

Estimates of CNMI bottomfish CPUE (lbs/trip) were calculated using the same approach as used 

in the 2012 assessment. We compared CPUE estimates from the previous assessment and the 

2015 assessment update for the years 2006-2010. In the 2012 assessment, CPUE starting in 2006 

was not included in the assessment because the values were very different from CPUE prior to 

2006, likely due to changes in the reporting method and sampling frame to collect CPUE data. 

We found a similar result in our CPUE calculations for recent years (Fig. 3). As a result and for 

consistency with the previous assessment, for this assessment update we retained the catch time 

series from 1983 to 2005 used in the previous assessment and did not add additional years of 

CPUE data because doing so would require modifications to the assessment model which are not 

generally done with a strict assessment update. The model fits the parameters to the CPUE data 

for the 23-year time series from 1983 to 2005, and then uses these fitted parameters to calculate 

biomass and harvest rates for 2006-2013 according to the production model equation. Note that 

recent data is included in the assessment in the form of catch data from 2006-2013. Table 5 

indicates that adding a few years of additional CPUE information does not drastically change 

assessment results. The addition of CPUE values starting in 2006 is possible for future 

assessments and will be explored in the next benchmark assessment of territorial bottomfish. 

Bottomfish CPUE fluctuated around its long-term average of 98 pounds/trip during 1983-2005 

(Table 3, Fig. 4) and ranged from 43 to 181 pounds/trip with a CV of 40%. The average CPUE 

of most recently used values from 2003 to 2005 was 90 pounds/trip.

Guam

We compared estimates of Guam bottomfish catch from the previous 2012 assessment and the 

2015 assessment update for the years 2006-2010 and found that they were generally similar (Fig.

5), indicating methods to calculate catch were similar. As a result, for this assessment we 

retained the catch time series from 1982 to 2010 used in the previous assessment, and added new 

years of catch data for 2011-2013. The annual bottomfish catch used in the current assessment 

update averaged approximately 40,292 pounds during 1982-2013 and ranged from 19,322 to 

66,666 pounds with a coefficient of variation of about 31% (Table 3, Fig. 6). Recent average 

yield (2011-2013 average) for Guam bottomfish was approximately 37,183 pounds.

Estimates of Guam bottomfish CPUE (pounds/line hr) were calculated using the same approach 

as used in the 2012 assessment. We compared CPUE estimates from the previous assessment and 

the 2015 assessment update for the years 2006-2010 and found the general trend and magnitude 
13

in CPUE was similar (Fig. 5). As a result, for this assessment we retained the catch time series 

from 1982 to 2010 used in the previous assessment, and added new years of CPUE data for 

2011-2013. This choice should have a negligible impact on the assessment results because the 

differences in CPUE are small, and production model fit to CPUE often averages out fluctuations 

(see Base Case Model Fit to CPUE section in Results, and Figs. 11-12).Bottomfish CPUE 

fluctuated around its long-term average of 3.1 pounds/line hr during 1986-2013 (Table 3, Fig. 6) 

and ranged from 1.3 to 11.7 pounds/line hr with a CV of 57%. Recent average CPUE from 2011

to 2013 was 3.4 pounds/line hr.

Base Case Model Convergence Diagnostics

American Samoa

Convergence diagnostics were calculated from the three chains used in the MCMC simulations 

for the base case model. The diagnostics were computed for nine key model parameters: K, r, Q, 

σ

2

, τ

2

, P[1], BMSY, HMSY, and MSY. The Geweke Z-score diagnostic values were less than 2 

in absolute value for all 27 tests, which indicated that there were no significant differences in 

means for the first and last sets of iterations of the chains. The Gelman and Rubin potential scale 

reduction factors were identically 1 for each of the 9 parameters which also indicated 

convergence. Each of the 9 parameters passed the Heidelberger and Welch stationary and halfwidth diagnostic tests. Overall, the convergence results indicated that the MCMC chains 

produced representative samples from the joint posterior distribution of model parameters.

Commonwealth of the Northern Mariana Islands

Convergence diagnostics were calculated from the three chains used in the MCMC simulations 

for the base case model. The diagnostics were computed for nine key model parameters: K, r, Q, 

σ

2

, τ

2

, P[1], BMSY, HMSY and MSY. The Geweke Z-score diagnostic values were less than 2 in 

absolute value for 26 out of 27 tests, which indicated that there were no significant differences in 

means for the first and last sets of iterations of the chains. The sole exception was Q, where 1 of 

the 3 chains had a Geweke Z-score of -2.188. The Gelman and Rubin potential scale reduction 

factors were identically 1 for each of the nine parameters, which also indicated convergence. 

Each of the nine parameters passed the Heidelberger and Welch stationary and half-width 

diagnostic tests. Overall, the convergence results indicated that the MCMC chains produced 

representative samples from the joint posterior distribution of model parameters.

Guam

Convergence diagnostics were calculated from the three chains used in the MCMC simulations 

for the base case model. The diagnostics were computed for nine key model parameters: K, r, Q, 

σ

2

, τ

2

, P[1], BMSY, HMSY and MSY. The Geweke Z-score diagnostic values were less than 2 in 

absolute value for 25 out of 27 tests, which indicated that there were no significant differences in 

means for the first and last sets of iterations of the chains. The only exceptions were for BMSY 

and K, where 1 of the 3 chains had a Geweke Z-score slightly above 2. The Gelman and Rubin 

potential scale reduction factors were identically 1 for each of the nine parameters, which also 

indicated convergence. Last, each of the nine parameters passed the Heidelberger and Welch 
14

stationary and half-width diagnostic tests. Overall, the convergence results indicated that the 

MCMC chains produced representative samples from the joint posterior distribution of model 

parameters.

Base Case Model Fit to CPUE

American Samoa

The predicted CPUE from the base case model fit reasonably well and produced a smooth fit to 

the observed CPUE data (Fig. 7). The standardized log-scale residuals from the CPUE fit were 

all within two standard errors of zero although there were some time blocks of positive and 

negative residuals (Fig. 8). Regression of the standardized log-scale residuals on time indicated 

there was no significant time trend and tests indicated that the residuals were normally 

distributed with constant variance. Overall, the CPUE diagnostics indicated that the observation 

errors likely conformed to the statistical assumptions of the production model.

Commonwealth of the Northern Mariana Islands

The predicted CPUE from the base case model fit the observed CPUE trends reasonably well 

(Fig. 9). The standardized log-scale residuals from the CPUE fit were all within two standard 

errors of zero although there were some time blocks of positive and negative residuals (Fig. 10). 

Regression of the standardized log-scale residuals on time indicated there was no significant time 

trend and tests indicated that the residuals were normally distributed with constant variance. 

Overall, the CPUE diagnostics indicated that the residuals were likely consistent with the 

assumed statistical formulation of the production model.

Guam

The predicted CPUE from the base case model fit the observed CPUE data reasonably well, 

except for the spike in the observed CPUE in 1984 (Fig. 11). The standardized log-scale 

residuals from the CPUE fit were within two standard errors of zero in all years except for 1982, 

which was slightly above two (Fig. 12). Regression of the standardized log-scale residuals on 

time indicated there was no significant time trend however; tests indicated that the residuals were 

not normally distributed but did have constant variance. Overall, the CPUE diagnostics indicated 

that the observation errors were generally consistent with the statistical assumptions of the 

production model.

Base Case Model Parameter Estimates

Model-estimated posterior mean parameter values and reference points, along with standard 

errors, are provided in Table 4 for each territory. 

American Samoa

The posterior means (± 1 standard error) of K, r, and P[1] from the base case model were: K = 

667.5 ± 130.6 thousand pounds, r = 0.48 ± 0.13, and P[1] = 0.81 ± 0.14. Posterior mean 
15

estimates of biological reference points from the base case model were: BMSY = 333.7 ± 65.3 

thousand pounds, HMSY = 0.24 ± 0.06, and MSY = 76.7 ± 14.1 thousand pounds (Table 4). The 

posterior mean of MSY was 1.74 thousand pounds higher than the input OLO estimate of MSY 

= 75.0 thousand pounds. Table 5 provides a comparison of model-estimated parameters and 

reference points from this assessment with estimates from the previous 2012 assessment 

(Brodziak et al., 2012), and indicates that values are similar. 

Estimates of American Samoa bottomfish exploitable biomass have fluctuated around 600 

thousand pounds since 1986, and estimates of relative biomass indicate that the mean biomass of 

the American Samoa bottomfish complex has been above BMSY during 1986-2013 (Table 6, 

Fig. 13). Biomass increased moderately in the 1990s, declined slightly from 1996 through 2010, 

and has increased slightly since. Lower bounds of the 95% confidence intervals for biomass 

show that, while there is overlap in the majority of years, estimates of biomass remained above 

0.7*BMSY throughout the 1986 to 2013 period.

Estimates of American Samoa bottomfish annual harvest rate have fluctuated around 5% since 

the late-1980s, increased to about 10% in 2009, and declined to less than 5% in 2013 (Table 6, 

Fig. 14). Estimates of relative harvest rate and upper bounds of the 95% confidence intervals for 

harvest rate indicate that the annual harvest rate has been below HMSY from 1986 to 2013.

The biomass status of the American Samoa bottomfish complex in 2013 was healthy, with a < 1

% risk that the stock was overfished (defined as B < BMSY). Similarly, in 2013 there was a low 

< 1% risk that overfishing was occurring (defined as H > HMSY). The trends in stock status over 

time are provided in a Kobe plot in Figure 15. Overall, the production model results suggest that 

the American Samoa bottomfish complex was not overfished and did not experience overfishing 

during 1986-2013 (Table 6, Fig. 15).

Commonwealth of the Northern Mariana Islands

The posterior means (± 1 standard error) of K, r, and P[1] from the base case model were: K = 

1367.0 ± 253.3, r = 0.53 ± 0.13, and P[1] = 0.46 ± 0.08. Posterior mean estimates of biological 

reference points from the base case model were: BMSY = 683.5 ± 126.7 thousand pounds, 

HMSY = 0.26 ± 0.06, and MSY = 173.1 ± 32.2 thousand pounds (Table 4). The posterior mean 

of MSY was 1.1 thousand pounds higher than the input OLO estimate of MSY = 172 thousand 

pounds. Table 5 provides a comparison of model-estimated parameters and reference points from 

this assessment with estimates from the previous 2012 assessment (Brodziak et al., 2012), and 

indicates that values are similar. 

Estimates of CNMI bottomfish exploitable biomass have fluctuated around 1200 thousand 

pounds since 1983. Estimates of relative biomass indicate that the mean biomass of the CNMI 

bottomfish complex was slightly below BMSY in 1983 and has likely been above BMSY during 

1984-2013 (Table 7, Fig. 16). Biomass increased moderately from 1983 to 1988 and then 

declined through 1991. Biomass increased again from 1991 to 1999 and then fluctuated through 

2013. Lower bounds of the 95% confidence intervals for biomass show that, while there is 

overlap in the majority of years, estimates of biomass remained above 0.7*BMSY throughout the 

1986 to 2013 period.
16

Estimates of CNMI bottomfish annual harvest rate have fluctuated around 4% since 1983 (Table 

7, Fig. 17). Estimates of relative harvest rate and upper bounds of the 95% confidence intervals 

for harvest rate indicate that the annual harvest rate has been below HMSY from 1983 to 2013. 

The biomass status of the CNMI bottomfish complex in 2013 was healthy, with only a 2.5% risk 

of the stock being overfished (defined as B < 0.7*BMSY). Similarly, in 2013 there was a low <

1% risk that overfishing was occurring, defined as (H > HMSY). The trends in stock status over 

time are provided in a Kobe plot in Figure 18. Overall, the production model results suggest that 

the CNMI bottomfish complex was not overfished and did not experience overfishing during 

1983-2013 (Table 7, Fig. 18). 

Guam

The posterior means (± 1 standard error) of K, r, and P[1] from the base case model were: K = 

324.5 ± 47.5, r = 0.703 ± 0.119, and P[1] = 0.768 ± 0.136. Posterior mean estimates of 

biological reference points from the base case model were: BMSY = 162.3 ± 23.8 thousand 

pounds, HMSY = 0.352 ± 0.059, and MSY = 56.13 ± 7.79 thousand pounds (Table 4). The 

posterior mean of MSY was 1.1 thousand pounds higher than the input OLO estimate of MSY = 

55.0 thousand pounds. Table 5 provides a comparison of model-estimated parameters and 

reference points from this assessment with estimates from the previous 2012 assessment 

(Brodziak et al., 2012), and indicates that values are similar. 

Estimates of Guam bottomfish exploitable biomass have fluctuated around 240 thousand pounds 

since 1982.Estimates of relative biomass indicate that the mean biomass of the Guam bottomfish 

complex has likely been above BMSY during 1982-2013, except for 1997 when the relative 

biomass was 0.96 (Table 8, Fig. 19). Biomass decreased moderately from a high in 1984 to a low 

in 1997 and has risen slightly and leveled off since then. Lower bounds of the 95% confidence 

intervals for biomass show that generally, estimates of biomass remained above 0.7*BMSY with 

the exception of 1990 to 2002, when there was very low risk of the stock being overfished (<

16%) .

Estimates of Guam bottomfish annual harvest rate increased from a low of about 10% throughout 

the late 1980s and 1990s until they reached a peak of about 35% in 2000 (Table 8, Fig. 20). In 

2000, the harvest rate was at HMSY. After 2000, harvest rates suddenly decreased and have 

fluctuated around 15% through 2013. Estimates of relative harvest rate and upper bounds of 95% 

confidence intervals for harvest rate indicate that the annual harvest rate has been below HMSY 

for all years, except for 1987 to 2002 and 2011 when there was a low to moderate risk that 

harvest rate was at or above HMSY. 

The biomass status of the Guam bottomfish complex in 2013 was healthy; there was a low < 1% 

risk that the stock was overfished (defined as B < 0.7*BMSY). Similarly, in 2013 there was a 

low < 1% risk that overfishing was occurring (defined as H > HMSY). The trends in stock status 

over time are provided in a Kobe plot in Figure 21. Overall, the production model results suggest 

that the Guam bottomfish complex was not overfished and did not experience overfishing during 

1982-2013, with the possible exception of 2000 when overfishing may have occurred (Table 8, 

Fig. 21). 
17

Base Case Model Projection Results

American Samoa

The constant 2-year catch projection scenarios for American Samoa bottomfish from 2016 to 

2017 resulted in projected probabilities of overfishing, relative biomasses, and probabilities of 

being overfished (Table 9, Fig. 22). The 2017 catch level corresponding to a 50% risk of 

overfishing in 2017 (i.e., H > HMSY) was 115 thousand pounds, which is 5 times higher than 

recent average catch of 21 thousand pounds from the past 3 years. For comparison, the 2017 

catch that would lead to a lower 25% risk of overfishing in 2017 was 97 thousand pounds. If the 

recent average catch of 21 thousand pounds was harvested in 2016 and 2017, the 2017 risks of 

overfishing and being overfished are < 1%. 

Commonwealth of the Northern Mariana Islands

The constant 2-year catch projection scenarios for CNMI bottomfish from 2016-2017 resulted in 

projected probabilities of overfishing, relative biomasses, and probabilities of being overfished 

(Table 10, Fig. 23). The 2017 catch level corresponding to a 50% risk of overfishing in 2017 

(i.e., H > HMSY) was 250 thousand pounds, which is over 10 times higher than recent average 

catch of 20 thousand pounds from the past 3 years. For comparison, the 2017 catch that would 

lead to a lower 25% risk of overfishing in 2017 was 206 thousand pounds. If the recent average 

catch of 20 thousand pounds was harvested in 2016 and 2017, the 2017 risks of overfishing and 

being overfished are < 1%. 

Guam

The constant 2-year catch projection scenarios for Guam bottomfish from 2016 to 2017 resulted 

in projected probabilities of overfishing, relative biomasses, and probabilities of being overfished 

(Table 11, Fig. 24). The 2017 catch level corresponding to a 50% risk of overfishing in 2017 

(i.e., H > HMSY) was 71 thousand pounds, which is 2 times higher than recent average catch of 

33 thousand pounds from the past 3 years. For comparison, the 2017 catch that would lead to a 

lower 25% risk of overfishing in 2017 was 61 thousand pounds. If the recent average catch of 33 

thousand pounds was harvested in 2016 and 2017, the 2017 risks of overfishing and being 

overfished are < 1%. 
18

DISCUSSION

Overall, this assessment determines that the Bottomfish Management Unit Species (BMUS) 

complexes of the territories of American Samoa, the Commonwealth of the Northern Mariana 

Islands, and Guam are not overfished and overfishing is not occurring. The stock statuses are in 

good condition and recent average catch levels are sustainable and below MSY. 

There are several caveats to mention for interpreting the production model results. First, the 

production model fits are conditioned on OLO previous estimates of MSY for each island group 

(Humphreys and Moffitt, 1999; Moffitt and Humphreys, 2009, using methods from Polovina and

Ralston, 1986). If these estimates are not accurate, then the scale of the production model 

estimates of biomass and harvest rate may change, even though the relative scale of biomass to 

BMSY and harvest rate to HMSY may not change substantially. 

Second, there are several potential problems with the fishery-dependent data for the three island 

groups that also warrant consideration in developing management advice. A primary concern is 

that the estimates of total fishery removals may be incomplete or inconsistent due to the 

voluntary nature of catch reporting, changes in data collection protocols, misidentification of 

species, or other potential issues. If the fishery removals are inaccurate, then the production 

model results will include this problem. In this context, the previous 2012 assessment 

investigated the effect of changes in magnitude of catch on model results if fishery removals are 

underestimated. Those analyses generally found that if catch levels are higher than reported in 

data from the WPacFIN, then the calculated stock status might be in poorer condition (risks of 

being overfished and overfishing would increase) and the resulting projected future catch levels 

corresponding to 50% risks of overfishing would decrease (Brodziak et al., 2012).

Third, the quality and coverage of the input data provide a challenge for moving to more 

complicated models. Besides uncertainty in catch, there is also uncertainty around effort 

measurements used as input data. Additionally, there is little contrast in the data series, meaning 

there is not much information contained in them for the model to estimate parameters with high 

confidence. As an example, the CPUE data were not particularly informative about the ratio of 

initial biomass to carrying capacity P[1], and posterior estimates of P[1] are likely informed by 

the chosen prior values. The selection of the prior values for P[1] can affect estimates of stock 

status. The prior values for P[1] used in this assessment are the values chosen for base case 

models in the 2012 assessment, which were determined after statistical comparison of several 

models with varying prior P[1] values (Brodziak et al., 2012). 

Fourth, another potential problem is that changes in the bottomfish fishery CPUE over time may 

not be proportional to changes in the relative abundance of bottomfish due to changes in fishing 

practices, fleet composition, or other factors that could alter standard measures of effective 

fishing effort on bottomfish. This assessment uses nominal, non-standardized CPUE time series 

because of the challenges of standardizing CPUE given data scarcity and quality. Thus the 

nominal CPUE series do not account for factors that could be affecting CPUE other than relative 

changes in stock abundance. If the nominal CPUE indices are inaccurate and if they do not 
19

reflect relative changes in stock abundance, then the trends from the production model will 

include this problem.

Finally, this assessment was a strict update of the 2012 assessment, meaning that the exact same 

model configuration, methods, and assumptions from the previous assessment were used.The 

only difference is the addition of 3 more years of catch and nominal CPUE data. There are more 

analyses that can be done to improve future iterations of territorial bottomfish assessments. Since 

the assessments rely so heavily on the fishery-dependent data from the WPacFIN, it would be 

useful to continue to improve the bottomfish fishery catch reporting systems of the three island 

groups to account for potential problems mentioned above. Further, it is notable that the data 

reporting systems in the island groups have begun to collect some length frequency samples of 

individual bottomfish species in biosampling programs. This ongoing data collection program 

will provide additional information on the average size of fish in the catch, which can eventually 

be incorporated into future assessments. 

ACKNOWLEDGEMENT

We thank Donald Kobayashi for peer review within PIFSC.

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Archipelago, 1982-1985. Mar. Fish. Rev. 47(4):19-25.

Polovina, J., and S. Ralston.

1986. An approach to yield assessment for unexploited resources with application to the deep 

slope fishes of the Marianas. Fishery Bulletin, 84(4): 759-770.

R Development Core Team.

2014. R: A language and environment for statistical computing. R Foundation for Statistical

Computing, Vienna, Austria. http://www.R- project.org.

Spiegelhalter, D., N. Best, B. Carlin, and A. van der Linde.

2002. Bayesian measures of model complexity and fit. J. R. Statist. Soc. B, 64:583- 639.

Spiegelhalter, D., A. Thomas, N. Best, and D. Lunn.

2003. WinBUGS User Manual. Available at:

http://www.mrc.bsu.carn.ac.uk/bugs/winbugs/manual14.pdf

Sturtz, S., U. Ligges, and A. Gelman.

2005. R2WinBUGS: A Package for Running WinBUGS from R. Journal of Statistical 

Software 12(3): 1-16.

Western Pacific Regional Fishery Management Council (WPRFMC).

2006. Bottomfish and Seamount Groundfish Fisheries of the Western Pacific Region. 2005 

Annual Report. Available at: http://www.wpcouncil.org/bottomfish.htm
22

Table 1.--List of Bottomfish Management Unit Species (BMUS) of American Samoa, Guam, 

and the Commonwealth of the Northern Mariana Islands that are identified in relevant Fishery 

Ecosystem Plans and assessed in this document.

Species name Common name Deep or shallow component

Aphareus rutilans Lehi Deep

Aprion virescens Uku Shallow

Caranx ignobilis Giant trevally Shallow

Caranx lugubris Black trevally Deep

Epinephelus fasciatus Blacktip grouper Shallow

Etelis carbunculus Ehu Deep

Etelis coruscans Onaga Deep

Lethrinus amboinensis Ambon emperor Shallow

Lethrinus rubrioperculatus Redgill emperor Shallow

Lutjanus kasmira Blueline snapper Shallow

Pristipomoides auricilla Yellowtail snapper Deep

Pristipomoides filamentosus Opakapaka Deep

Pristipomoides flavipinnis Yelloweye opakapaka Deep

Pristipomoides sieboldii Kalekale Deep

Pristipomoides zonatus Gindai Deep

Seriola dumerili Amberjack Shallow

Variola louti Lunartail grouper Deep
23

Table 2.--Assumed prior distribution values for parameters in the production models for 

Bottomfish Management Unit Species in American Samoa, Commonwealth of the Northern 

Mariana Islands (CNMI), and Guam. Values are the same as those used in base case models of 

the previous stock assessment.

Prior mean

Parameter Description (units) Distribution

American 

Samoa CNMI Guam

Prior 

CV

R Intrinsic growth rate (1/yr) Beta 0.46 0.46 0.46 50%

K Carrying capacity (1000 lbs) Diffuse normal 700 1400 300 20%

P[1]

Ratio of biomass to K in 

initial year Lognormal 0.8 0.45 0.75 20%

Q Catchability

Diffuse inverse 

gamma 1 1 1 32%

τ2 Observation error variance Inverse gamma 0.83 0.83 0.83 NA

σ2 Process error variance Inverse gamma 0.083 0.083 0.083 NA
24

Table 3.--Annual estimates of catch and nominal CPUE of Bottomfish Management Unit Species 

(BMUS) in American Samoa, the Commonwealth of the Northern Marianas (CNMI), and Guam. 

These data were used as an input for this stock assessment update. The values for 1982-2010 are 

the same as those used in the previous assessment, and the values for 2011-2013 are new for this 

assessment and were calculated in April 2015. Data source is the Western Pacific Fisheries 

Information Network (WPacFIN) boat-based creel survey data for American Samoa and Guam, 

and WPacFIN commercial purchase data for CNMI. See Appendix B for this same input data 

provided in a format for use with Appendix A, generic stock assessment model code. 

Year

American 

Samoa BMUS 

Catch (lbs)

American Samoa 

BMUS CPUE 

(lbs/line hr)

CNMI 

BMUS 

Catch (lbs)

CNMI BMUS 

CPUE 

(lbs/trip)

Guam 

BMUS 

Catch (lbs)

Guam BMUS 

CPUE 

(lbs/line hr)

1982 26384 3.05

1983 28529 43 40782 2.66

1984 42665 70 19322 11.66

1985 40974 117 49195 2.46

1986 64587 3.26 29912 104 20427 3.57

1987 19628 2.98 49714 169 29301 3.98

1988 33726 6.35 47313 181 46318 2.37

1989 32647 4.02 24439 73 58582 2.28

1990 11332 3.54 12929 81 42384 3.4

1991 13010 2.64 7092 47 39596 2

1992 9985 2.44 10598 59 50394 2.25

1993 14554 3.27 18461 84 55609 2.98

1994 33845 3.16 25470 74 49055 2.73

1995 27699 4.24 36100 93 40855 2.05

1996 30808 6.53 66388 119 54186 2.26

1997 32308 3.82 64143 137 30611 1.32

1998 12413 3.96 59024 148 37687 1.65
25

Year

American 

Samoa BMUS 

Catch (lbs)

American Samoa 

BMUS CPUE 

(lbs/line hr)

CNMI 

BMUS 

Catch (lbs)

CNMI BMUS 

CPUE 

(lbs/trip)

Guam 

BMUS 

Catch (lbs)

Guam BMUS 

CPUE 

(lbs/line hr)

1999 15857 3.67 55991 156 53339 1.88

2000 19816 4.57 45258 56 66666 1.89

2001 37847 4.95 71256 68 54352 3.25

2002 34149 2.45 46765 101 24044 2.87

2003 19199 5.42 41903 89 43253 4.26

2004 17206 4.31 54475 104 36915 2.77

2005 16329 3.13 70404 76 36529 4.81

2006 7913 2.65 29340 38054 3.78

2007 21874 2.57 39476 27459 2.32

2008 34812 2.9 42070 37316 1.93

2009 47458 3.62 41176 40222 3.17

2010 9509 2.96 22395 28958 3.65

2011 26277 3.95 22487 59618 3.62

2012 13110 5.75 15302 22085 3.47

2013 23630 4.25 22510 29848 3.15
26

Table 4.--Model-estimated mean and standard error for parameters and reference points from 

production models for Bottomfish Management Unit Species in American Samoa, 

Commonwealth of the Northern Mariana Islands (CNMI), and Guam. 

Parameter Description

American 

Samoa 

estimated 

mean

American 

Samoa 

estimated 

standard 

error

CNMI 

estimated 

mean

CNMI 

estimated 

standard 

error

Guam 

estimated 

mean

Guam 

estimated 

standard 

error

r

Intrinsic growth 

rate (1/yr) 0.477 0.125 0.525 0.126 0.703 0.119

K

Carrying capacity 

(1000 lbs) 667.5 130.6 1367.0 253.3 324.5 47.5

P[1]

Ratio of biomass to 

K in initial year 0.808 0.141 0.458 0.082 0.768 0.136

Q Catchability 0.00674 0.00175 0.0859 0.0230 0.0129 0.0028

τ

2

Observation error 

variance 0.165 0.055 0.225 0.089 0.194 0.062

σ

2

Process error 

variance 0.0560 0.0306 0.0881 0.0525 0.0629 0.0344

Reference 

Point Description

American 

Samoa 

estimated 

mean

American 

Samoa 

estimated 

standard 

error

CNMI 

estimated 

mean

CNMI 

estimated 

standard 

error

Guam 

estimated 

mean

Guam 

estimated 

standard 

error

MSY

Maximum 

sustainable yield 

(1000 lbs) 76.74 14.06 173.10 32.19 56.13 7.79

HMSY

Harvest rate that 

produces MSY 0.238 0.062 0.261 0.063 0.352 0.059

BMSY

Biomass that 

produces MSY 

(1000 lbs) 333.7 65.3 683.5 126.7 162.3 23.8
27

Table 5.--Comparison of model-estimated mean and standard error for parameters and stock reference points from production models 

for Bottomfish Management Unit Species in American Samoa, Commonwealth of the Northern Mariana Islands (CNMI), and Guam. 

2012 values come from the previous assessment (Brodziak et al., 2012), and 2015 values come from this assessment. 

American Samoa CNMI Guam

Parameter Description

2012 

mean

2012

stand 

error

2015 

mean

2015 

stand 

error

2012 

mean

2012 

stand 

error

2015 

mean

2015 

stand 

error

2012 

mean

2012 

stand 

error

2015 

mean

2015 

stand 

error

r

Intrinsic 

growth rate 

(1/yr) 0.47 0.12 0.477 0.125 0.52 0.13 0.525 0.126 0.70 0.12 0.703 0.119

K

Carrying 

capacity (1000 

lbs) 670.7 132.3 667.5 130.6 1367.0 256.4 1367.0 253.3 324.5 48.1 324.5 47.5

P[1]

Ratio of 

biomass to K 

in initial year 0.82 0.14 0.808 0.141 0.46 0.08 0.458 0.082 0.77 0.14 0.768 0.136

Q Catchability 0.01 0.002 0.00674 0.00175 0.09 0.023 0.0859 0.0230 0.01 0.0030 0.0129 0.0028

τ

2 Observation 

error variance 0.18 0.06 0.165 0.055 0.22 0.09 0.225 0.089 0.22 0.07 0.194 0.062

σ2 Process error 

variance 0.06 0.04 0.0560 0.0306 0.09 0.05 0.0881 0.0525 0.07 0.04 0.0629 0.0344
28

American Samoa CNMI Guam

Ref

Point

Description

2012 

mean

2012 

stand 

error

2015 

mean

2015 

stand 

error

2012 

mean

2012 

stand 

error

2015 

mean

2015 

stand 

error

2012 

mean

2012 

stand 

error

2015 

mean

2015 

stand 

error

MSY

Maximum 

sustainable 

yield (1000 

lbs) 76.2 14.3 76.74 14.06 172.9 32.2 173.10 32.19 55.9 7.9 56.13 7.79

HMSY

Harvest rate 

that produces 

MSY 0.24 0.06 0.238 0.062 0.26 0.06 0.261 0.063 0.35 0.06 0.352 0.059

BMSY

Biomass that 

produces MSY 

(1000 lbs) 335.4 66.1 333.7 65.3 683.6 128.2 683.5 126.7 162.2 24.03 162.3 23.8

H2010

Harvest rate in 

2010 0.02 0.01 0.02 0.01 0.02 0.02 0.02 0.02 0.12 0.04 0.12 0.04

B2010

Biomass in 

2010 (1000 

lbs) 533.2 180.6 547.0 174.2 1216.0 530.4 1217.0 521.4 259.2 82.68 264.2 78.8
29

Table 6.--American Samoa Bottomfish Management Unit Species: Base case production model 

estimates of mean exploitable biomass, relative biomass, risk of being overfished (B <

0.7*BMSY), harvest rate, relative harvest rate, and risk of overfishing (H > HMSY) from 1986

to 2013.

Year

Exploitable 

biomass (B, units 

of 1000 pounds)

Relative 

biomass 

(B/BMSY)

Risk of being 

overfished 

(B < 0.7*BMSY)

Harvest rate 

(H, an annual 

proportion)

Relative 

harvest rate 

(H/HMSY)

Risk of 

overfishing 

(H > HMSY)

1986 539.7 1.62 0.000 0.129 0.56 0.014

1987 547.7 1.64 0.000 0.039 0.17 0.000

1988 659.2 1.98 0.000 0.056 0.25 0.000

1989 608.6 1.82 0.000 0.059 0.26 0.000

1990 558.9 1.67 0.001 0.022 0.10 0.000

1991 520.5 1.56 0.003 0.028 0.12 0.000

1992 513.3 1.54 0.003 0.022 0.09 0.000

1993 554.9 1.66 0.002 0.029 0.13 0.000

1994 587.5 1.76 0.001 0.063 0.28 0.001

1995 639.1 1.91 0.000 0.048 0.21 0.000

1996 707.1 2.12 0.000 0.048 0.21 0.000

1997 641.4 1.92 0.000 0.056 0.24 0.000

1998 625.8 1.87 0.000 0.022 0.09 0.000

1999 631.3 1.89 0.000 0.028 0.12 0.000

2000 661.5 1.98 0.000 0.033 0.14 0.000

2001 657.5 1.97 0.000 0.063 0.28 0.001

2002 574.2 1.72 0.001 0.066 0.28 0.000

2003 635.4 1.90 0.000 0.033 0.15 0.000

2004 613.3 1.84 0.000 0.031 0.14 0.000

2005 554.8 1.66 0.001 0.032 0.14 0.000

2006 516.5 1.55 0.003 0.017 0.07 0.000

2007 515.7 1.55 0.003 0.047 0.20 0.000

2008 532.3 1.60 0.002 0.072 0.31 0.001

2009 556.4 1.67 0.001 0.094 0.41 0.005

2010 547.0 1.64 0.002 0.019 0.08 0.000

2011 624.0 1.87 0.000 0.046 0.20 0.000

2012 682.7 2.04 0.000 0.021 0.09 0.000

2013 661.3 1.98 0.000 0.039 0.17 0.000
30

Table 7.--Commonwealth of the Northern Mariana Islands (CNMI) Bottomfish Management 

Unit Species: Base case production model estimates of mean exploitable biomass, relative 

biomass, risk of being overfished (B < 0.7*BMSY), harvest rate, relative harvest rate, and risk of 

overfishing (H > HMSY) from 1983 to 2013.

Year

Exploitable 

biomass (B, 

units of 1000 

pounds)

Relative 

biomass 

(B/BMSY)

Risk of being 

overfished (B < 

0.7*BMSY)

Harvest rate 

(H, an annual 

proportion)

Relative 

harvest rate 

(H/HMSY)

Risk of 

overfishing 

(H > HMSY)

1983 627 0.92 0.076 0.049 0.193 0.000

1984 882 1.29 0.013 0.054 0.213 0.000

1985 1160 1.70 0.003 0.040 0.158 0.000

1986 1293 1.89 0.001 0.026 0.103 0.000

1987 1509 2.20 0.001 0.037 0.147 0.000

1988 1518 2.22 0.001 0.035 0.141 0.000

1989 1143 1.67 0.006 0.024 0.096 0.000

1990 1058 1.55 0.013 0.014 0.055 0.000

1991 928 1.36 0.034 0.009 0.035 0.000

1992 974 1.43 0.024 0.012 0.050 0.000

1993 1084 1.59 0.010 0.019 0.077 0.000

1994 1126 1.65 0.007 0.025 0.102 0.000

1995 1235 1.81 0.003 0.033 0.130 0.000

1996 1372 2.01 0.001 0.054 0.214 0.000

1997 1443 2.10 0.001 0.050 0.197 0.000

1998 1469 2.14 0.001 0.045 0.179 0.000

1999 1425 2.08 0.001 0.045 0.177 0.000

2000 1075 1.57 0.009 0.048 0.189 0.000

2001 1082 1.58 0.008 0.074 0.295 0.002

2002 1166 1.71 0.005 0.045 0.180 0.000

2003 1181 1.73 0.004 0.040 0.159 0.000

2004 1214 1.78 0.003 0.050 0.201 0.000

2005 1139 1.67 0.007 0.070 0.278 0.002

2006 1172 1.71 0.018 0.030 0.118 0.000

2007 1211 1.77 0.022 0.039 0.156 0.001

2008 1219 1.78 0.025 0.042 0.166 0.002

2009 1216 1.78 0.027 0.041 0.165 0.002

2010 1217 1.78 0.029 0.023 0.090 0.001

2011 1237 1.81 0.028 0.022 0.089 0.001

2012 1248 1.83 0.026 0.015 0.061 0.000

2013 1262 1.85 0.025 0.022 0.088 0.001
31

Table 8.--Guam Bottomfish Management Unit Species: Base case production model estimates of 

mean exploitable biomass, relative biomass, risk of being overfished (B < 0.7*BMSY), harvest 

rate, relative harvest rate, and risk of overfishing (H > HMSY) from 1982 to 2013.

Year

Exploitable 

biomass (B, 

units of 1000 

pounds)

Relative 

biomass 

(B/BMSY)

Risk of being 

overfished

(B < 0.7*BMSY)

Harvest rate 

(H, an 

annual 

proportion)

Relative 

harvest rate 

(H/HMSY)

Risk of 

overfishing 

(H > HMSY)

1982 249.6 1.54 0.00 0.112 0.322 0.00

1983 280.3 1.72 0.00 0.156 0.448 0.00

1984 361.1 2.22 0.00 0.059 0.171 0.00

1985 276.9 1.70 0.00 0.192 0.553 0.01

1986 264.2 1.62 0.00 0.084 0.243 0.00

1987 279.8 1.72 0.00 0.114 0.330 0.00

1988 249.0 1.53 0.00 0.202 0.583 0.02

1989 231.0 1.42 0.00 0.275 0.794 0.17

1990 223.4 1.37 0.01 0.208 0.602 0.04

1991 208.6 1.28 0.02 0.208 0.601 0.04

1992 214.9 1.32 0.01 0.256 0.741 0.13

1993 218.7 1.35 0.01 0.279 0.808 0.20

1994 203.4 1.25 0.02 0.266 0.770 0.16

1995 185.9 1.14 0.04 0.243 0.702 0.11

1996 180.6 1.11 0.06 0.330 0.957 0.39

1997 156.3 0.96 0.16 0.218 0.630 0.06

1998 172.2 1.06 0.08 0.241 0.697 0.10

1999 190.7 1.18 0.03 0.305 0.882 0.29

2000 201.6 1.24 0.01 0.358 1.037 0.49

2001 214.4 1.32 0.01 0.278 0.804 0.20

2002 224.6 1.38 0.01 0.118 0.342 0.00

2003 271.1 1.67 0.00 0.175 0.507 0.01

2004 257.2 1.58 0.00 0.156 0.452 0.00

2005 283.6 1.74 0.00 0.141 0.408 0.00

2006 267.5 1.64 0.00 0.156 0.451 0.00

2007 232.5 1.43 0.01 0.129 0.373 0.00

2008 230.6 1.42 0.01 0.176 0.510 0.01

2009 250.6 1.54 0.00 0.174 0.505 0.01

2010 264.2 1.62 0.00 0.119 0.345 0.00

2011 277.6 1.71 0.00 0.233 0.673 0.07

2012 251.7 1.55 0.00 0.096 0.277 0.00

2013 264.7 1.63 0.00 0.123 0.356 0.00
32

Table 9.--American Samoa Bottomfish Management Unit Species: Results of projections 

through 2017, including 2016 and 2017 risks of overfishing (H > HMSY), the corresponding 

catch level, harvest rate in 2016, relative biomass in 2017, and risk of being overfished (B < 

0.7*BMSY) in 2017.

Catch (lbs) of 

American Samoa 

bottomfish in 2016 

and 2017

Risk of 

overfishing 

(H > HMSY)

in 2016

Risk of 

overfishing 

(H > HMSY)

in 2017

Harvest 

rate in 

2016

Relative 

biomass 

(B/BMSY) 

in 2017

Risk of being 

overfished 

(B < 0.7*BMSY)

in 2017

50,000 1.0% 1% 0.09 1.69 0.5%

69,000 4.2% 5% 0.13 1.64 0.7%

80,000 7.7% 10% 0.15 1.60 0.8%

87,000 10.9% 15% 0.16 1.58 0.9%

92,000 13.5% 20% 0.17 1.56 1.0%

97,000 16.6% 25% 0.18 1.55 1.0%

98,000 17.2% 27% 0.18 1.55 1.1%

99,000 18.0% 28% 0.18 1.54 1.1%

100,000 18.6% 29% 0.18 1.54 1.1%

101,000 19.3% 30% 0.19 1.54 1.1%

102,000 19.9% 31% 0.19 1.53 1.1%

103,000 20.7% 33% 0.19 1.53 1.2%

104,000 21.5% 34% 0.19 1.53 1.2%

105,000 22.3% 35% 0.19 1.52 1.2%

106,000 22.9% 37% 0.19 1.52 1.2%

107,000 23.7% 38% 0.20 1.52 1.3%

108,000 24.5% 40% 0.20 1.51 1.3%

109,000 25.4% 41% 0.20 1.51 1.3%

110,000 26.1% 42% 0.20 1.51 1.3%

111,000 26.9% 44% 0.20 1.50 1.3%

112,000 27.8% 45% 0.21 1.50 1.4%

113,000 28.6% 47% 0.21 1.50 1.4%

114,000 29.4% 48% 0.21 1.50 1.4%

115,000 30.2% 50% 0.21 1.49 1.5%
33

Table 10.--Commonwealth of the Northern Mariana Islands (CNMI) Bottomfish Management 

Unit Species: Results of projections through 2017, including 2016 and 2017 risks of overfishing 

(H > HMSY), the corresponding catch level, harvest rate in 2016, relative biomass in 2017, and 

risk of being overfished (B < 0.7*BMSY) in 2017.

Catch (lbs) of 

CNMI 

bottomfish in 

2016 and 2017

Risk of 

overfishing 

(H > HMSY) 

in 2016

Risk of 

overfishing 

(H > HMSY) 

in 2017

Harvest 

rate in 

2016

Relative 

biomass 

(B/BMSY) in 

2017

Risk of being 

overfished 

(B < 0.7*BMSY)

in 2017

78,000 1.0% 1% 0.07 1.69 1.7%

134,000 4.7% 5% 0.13 1.60 2.3%

162,000 8.5% 10% 0.15 1.56 2.7%

180,000 12.1% 15% 0.17 1.54 3.0%

194,000 15.2% 20% 0.18 1.51 3.1%

206,000 18.1% 25% 0.20 1.50 3.3%

208,000 18.7% 26% 0.20 1.49 3.4%

210,000 19.1% 27% 0.20 1.49 3.4%

212,000 19.7% 28% 0.20 1.49 3.4%

214,000 20.2% 29% 0.20 1.48 3.5%

216,000 20.8% 30% 0.21 1.48 3.5%

218,000 21.3% 31% 0.21 1.48 3.5%

220,000 21.9% 32% 0.21 1.48 3.6%

222,000 22.5% 33% 0.21 1.47 3.7%

224,000 23.1% 34% 0.21 1.47 3.7%

226,000 23.6% 35% 0.21 1.47 3.7%

228,000 24.2% 36% 0.22 1.46 3.8%

230,000 24.9% 37% 0.22 1.46 3.8%

232,000 25.6% 38% 0.22 1.46 3.8%

234,000 26.2% 40% 0.22 1.45 3.9%

236,000 26.8% 41% 0.22 1.45 3.9%

238,000 27.4% 42% 0.23 1.45 4.0%

240,000 28.1% 43% 0.23 1.45 4.0%

242,000 28.7% 45% 0.23 1.44 4.1%

244,000 29.3% 46% 0.23 1.44 4.1%

246,000 30.0% 47% 0.23 1.44 4.2%

248,000 30.6% 48% 0.24 1.43 4.2%

250,000 31.2% 50% 0.24 1.43 4.3%
34

Table 11.--Guam Bottomfish Management Unit Species: Results of projections through 2017, 

including 2016 and 2017 risks of overfishing (H > HMSY), the corresponding catch level, 

harvest rate in 2016, relative biomass in 2017, and risk of being overfished (B < 0.7*BMSY) in 

2017.

Catch (lbs) of 

Guam

bottomfish in 

2016 and 2017

Risk of 

overfishing 

(H > HMSY) 

in 2016

Risk of 

overfishing 

(H > HMSY)

in 2017

Harvest 

rate in 

2016

Relative 

biomass 

(B/BMSY) in 

2017

Risk of being 

overfished 

(B < 0.7*BMSY) 

in 2017

33,000 1.2% 1% 0.15 1.50 1.0%

45,000 5.0% 5% 0.20 1.42 1.6%

51,000 8.9% 10% 0.23 1.39 2.0%

55,000 12.3% 15% 0.25 1.36 2.2%

58,000 15.2% 20% 0.26 1.34 2.4%

61,000 18.6% 25% 0.27 1.32 2.7%

62,000 19.8% 26% 0.28 1.32 2.8%

63,000 21.0% 29% 0.28 1.31 2.9%

64,000 22.3% 31% 0.29 1.30 3.1%

65,000 23.7% 33% 0.29 1.30 3.2%

66,000 25.0% 36% 0.30 1.29 3.3%

67,000 26.4% 38% 0.30 1.28 3.4%

68,000 27.8% 41% 0.31 1.28 3.5%

69,000 29.2% 44% 0.31 1.27 3.6%

70,000 30.7% 46% 0.32 1.27 3.7%

71,000 32.1% 49% 0.32 1.26 3.9%
35

Figure 1.--(top) Comparison of catch calculated for American Samoa BMUS (Bottomfish 

Management Unit Species complex) for the 2012 assessment (blue line) and in 2015 (red line). 

The catch time series are very similar in the years they overlap, from 2006 to 2010. For this 

assessment update, newly calculated catch for 2011-2013 was added to the original time series of 

catch for 1986-2010 calculated in 2012.

(bottom) Comparison of nominal catch per unit effort (CPUE, in lbs/line hour) calculated for 

American Samoa BMUS for the 2012 assessment (blue line) and in 2015 (red line). The time 

series are very similar in magnitude and trend in the years they overlap, from 2006 to 2010. For 

this assessment update, newly calculated CPUE for 2011-2013 was added to the original time

series of CPUE for 1986-2010 calculated in 2012.

0

10000

20000

30000

40000

50000

60000

70000

1986198719881989199019911992199319941995199619971998199920002001200220032004200520062007200820092010201120122013

BMUS catch (lbs)

Year

Comparison of American Samoa BMUS expanded total catch

2012

2015

0

1

2

3

4

5

6

7

1986198719881989199019911992199319941995199619971998199920002001200220032004200520062007200820092010201120122013

BMUS CPUE (lbs/line hour)

Year

Comparison of American Samoa nominal CPUE 2012

2015
36

Figure 2.--(top) Final catch (thousand lbs) of American Samoa BMUS (Bottomfish Management 

Unit Species Complex) from 1986-2013 used in this stock assessment update.

(bottom) Final nominal catch per unit effort (CPUE, in lbs/line hour) of American Samoa BMUS 

(Bottomfish Management Unit Species Complex) from 1986 to 2013 used in this stock 

assessment update.
37

Figure 3.--(top) Comparison of catch calculated for CNMI BMUS (Bottomfish Management 

Unit Species complex) for the 2012 assessment (blue line) and in 2015 (red line). The catch time 

series are extremely similar in the years they overlap, from 2006 to 2010. For this assessment 

update, newly calculated catch for 2011-2013 was added to the original time series of catch for 

1983-2010 calculated in 2012.

(bottom) Comparison of nominal catch per unit effort (CPUE, in lbs/trip) calculated for CNMI 

BMUS for the 2012 assessment (blue line) and in 2015 (red line). The time series are very 

different in magnitude, likely due to changes in reporting method and sampling frame. For this 

assessment update, the original time series of CPUE for 1983-2005 was used.

0

10000

20000

30000

40000

50000

60000

70000

80000

1983198519871989199119931995199719992001200320052007200920112013

BMUS catch (lbs)

Year

Comparison of CNMI BMUS expanded total catch 2012

2015

0

20

40

60

80

100

120

140

160

180

200

1983198519871989199119931995199719992001200320052007200920112013

BMUS CPUE (lbs/trip)

Year

Comparison of CNMI nominal CPUE 2012

2015
38

Figure 4.--(top) Final catch (thousand lbs) of CNMI (Commonwealth of the Northern Mariana 

Islands) BMUS (Bottomfish Management Unit Species Complex) from 1983 to 2013 used in this 

stock assessment update.

(bottom) Final nominal catch per unit effort (CPUE, in lbs/trip) of CNMI BMUS (Bottomfish 

Management Unit Species Complex) from 1983 to 2005 used in this stock assessment update.
39

Figure 5.--(top) Comparison of catch calculated for Guam BMUS (Bottomfish Management Unit 

Species complex) for the 2012 assessment (blue line) and in 2015 (red line). The catch time 

series are very similar in the years they overlap, from 2006 to 2010. For this assessment update, 

newly calculated catch for 2011-2013 was added to the original time series of catch for 1982-

2010 calculated in 2012.

(bottom) Comparison of nominal catch per unit effort (CPUE, in lbs/line hour) calculated for 

Guam BMUS for the 2012 assessment (blue line) and in 2015 (red line). The time series are very 

similar in magnitude and trend in the years they overlap, from 2006 to 2010. For this assessment 

update, newly calculated CPUE for 2011-2013 was added to the original time series of CPUE for 

1982-2010 calculated in 2012.

0

10000

20000

30000

40000

50000

60000

70000

1982198419861988199019921994199619982000200220042006200820102012

BMUS catch (lbs)

Year

Comparison of Guam BMUS expanded total catch 2012

2015

0

2

4

6

8

10

12

14

1982198419861988

1990

1992199419961998200020022004

2006

200820102012

BMUS CPUE (lbs/line hour)

Year

Comparison of Guam nominal CPUE

2012

2015
40

Figure 6.--(top) Final catch (thousand lbs) of Guam BMUS (Bottomfish Management Unit 

Species Complex) from 1982 to 2013 used in this stock assessment update.

(bottom) Final nominal catch per unit effort (CPUE, in lbs/line hour) of Guam BMUS 

(Bottomfish Management Unit Species Complex) from 1982 to 2013 used in this stock 

assessment update.
41

Figure 7.--Comparison of observed nominal CPUE (circles with solid line) with model-predicted 

nominal CPUE (squares with dotted line) of American Samoa bottomfish, 1986-2013. 
42

Figure 8.--Residuals of production model fit to nominal observed CPUE for American Samoa 

bottomfish, 1986-2013. 
43

Figure 9.--Comparison of observed nominal CPUE (circles with solid line) with model-predicted 

nominal CPUE (squares with dotted line) of CNMI bottomfish, 1983-2005. 
44

Figure 10.--Residuals of production model fit to nominal observed CPUE for CNMI bottomfish, 

1983-2005. 
45

Figure 11.--Comparison of observed nominal CPUE (circles with solid line) with modelpredicted nominal CPUE (squares with dotted line) of Guam bottomfish, 1982-2013. 
46

Figure 12.--Residuals of production model fit to nominal observed CPUE for Guam bottomfish, 

1982-2013. 
47

American Samoa Bottomfish

Figure 13.--American Samoa bottomfish biomass: Model-estimated trends in mean values (black 

circles, solid line) with 95% confidence intervals (black dotted line). BMSY is indicated with a 

horizontal gray dotted line, and the overfished limit of 0.7*BMSY is indicated with a horizontal 

solid red line. Biomass estimates were generally above the overfished limit, indicating stock 

status was not and is not overfished. 
48

American Samoa Bottomfish

Figure 14.--American Samoa bottomfish harvest rates: Model-estimated trends in mean annual 

harvest rate (black circles, solid line) with 95% confidence intervals (black dotted line). The 

overfishing limit of HMSY is indicated with a horizontal solid red line. Harvest rate estimates 

were generally below the overfishing limit, indicating overfishing was not and is not occurring.
49

American Samoa Bottomfish

Figure 15.--American Samoa bottomfish stock status: Kobe plot indicating annual stock status 

over time, from 1986 to 2013. The ratio of B/BMSY was generally > 0.7 and the ratio of 

H/HMSY was generally < 1, indicating that the stock status over time was not overfished and 

overfishing was not occurring. 
50

CNMI Bottomfish

Figure 16.--CNMI bottomfish biomass: Model-estimated trends in mean values (black circles, 

solid line) with 95% confidence intervals (black dotted line). BMSY is indicated with a 

horizontal gray dotted line, and the overfished limit of 0.7*BMSY is indicated with a horizontal 

solid red line. Biomass estimates were generally above the overfished limit with the exception of 

a few years. In recent years the stock status is not considered to be overfished. 
51

CNMI Bottomfish

Figure 17.--CNMI bottomfish harvest rates: Model-estimated trends in mean annual harvest rate 

(black circles, solid line) with 95% confidence intervals (black dotted line). The overfishing limit 

of HMSY is indicated with a horizontal solid red line. Harvest rate estimates were generally 

below the overfishing limit, indicating overfishing was not and is not occurring.
52

CNMI Bottomfish

Figure 18.--CNMI bottomfish stock status: Kobe plot indicating annual stock status over time, 

from 1983 to 2013. The ratio of B/BMSY was generally > 0.7, and the ratio of H/HMSY was 

generally < 1, indicating that the stock status over time was not overfished and overfishing was 

not occurring. 
53

Guam bottomfish

Figure 19.--Guam bottomfish biomass: Model-estimated trends in mean values (black circles, 

solid line) with 95% confidence intervals (black dotted line). BMSY is indicated with a 

horizontal gray dotted line, and the overfished limit of 0.7*BMSY is indicated with a horizontal 

solid red line. Biomass estimates were generally above the overfished limit, with the exception of 

a few years in the mid-1990s. In recent years the stock status is not overfished. 
54

Guam bottomfish

Figure 20.--Guam bottomfish harvest rates: Model-estimated trends in mean annual harvest rate 

(black circles, solid line) with 95% confidence intervals (black dotted line). The overfishing limit 

of HMSY is indicated with a horizontal solid red line. Harvest rate estimates were generally 

below the overfishing limit, possibly with the exception of a few years in the 1990s. In recent 

years, overfishing is not occurring.
55

Guam bottomfish

Figure 21.--Guam bottomfish stock status: Kobe plot indicating annual stock status over time, 

from 1982 to 2013. The ratio of B/BMSY was generally > 0.7, and the ratio of H/HMSY was 

generally < 1, indicating that the stock status over time was not overfished and overfishing was 

not occurring. 
56

Figure 22.--Projections of catch in 2016 and 2017 for American Samoa bottomfish and 

associated risks of overfishing (H > HMSY) in each corresponding year (blue line). 
57

Figure 23.--Projections of catch in 2016 and 2017 for CNMI bottomfish and associated risks of 

overfishing (H > HMSY) in each corresponding year (blue line). 
58

Figure 24.--Projections of catch in 2016 and 2017 for Guam bottomfish and associated risks of 

overfishing (H > HMSY) in each corresponding year (blue line).
59

APPENDIX A

This is generic code for running Bayesian state-space surplus production models, used to conduct 

stock assessment updates for the Bottomfish Management Unit Species (BMUS) complexes of 

American Samoa, the Commonwealth of the Northern Mariana Islands, and Guam. Code as it’s 

currently set up will run an assessment for American Samoa BMUS. Please do not reproduce or 

distribute without authors’ written consent. If you use this code or a major part of this code to 

produce results for a final product such as a document or presentation, please acknowledge and 

cite this document. 

##############################################################

# Generic code for running Bayesian Schaefer production models

# for Territorial bottomfish

# 2-parameter production model (r and K)

#

# Original WinBUGS code by Jon Brodziak

# Last modified March 2012

#

# Modified into R2WinBUGS format by Annie Yau

# November 2014

# last modified April 9, 2015

# 

# Catch is in thousands of pounds

# CPUE for American Samoa and Guam is in pounds / line-hour

# CPUE for CNMI is in pounds / trip

#

# Guam time period 1982-2013

# American Samoa time period 1986-2013
60

# CNMI time period 1983-2013

#

# Constant catchability, no catch error 

# Includes likelihood for observed MSY based on calculation from

# 1999 Our Living Oceans report, Humphreys & Moffitt 1999

##############################################################

rm(list=ls())

terr = "AmSam" # "CNMI" # "Guam"; # Select territory of assessment, change accordingly

 addname <- paste0(terr,'_base') ##<--------name of model---------- # change accordingly

src.dir <- paste('X:/AYau/Bottomfish/TerritAssess_2015/2015assess/',addname,'/',sep="") # 

Change accordingly

setwd(src.dir)

DATA = read.csv( paste0(src.dir,terr,"BF_data.csv"),header=T) # import data file

head(DATA)

library(R2WinBUGS) # Load the R2WinBUGS library

nc <- 3 # of chains

nt <- 25 # Thinning rate

nb <- 10000*nt # nt*numbertoburnin # round(ni*(1/10)) # Num of draws to discard as burn in

ni <- 30000/nc*nt + nb # Number of total iterations per chain, including burn-in = num 

samples/nc*nt _nb. 

## WinBUGS code equivalent:

## A total of 30,000 samples, 10,000 per chain for 3 chains. Discard every 25, burn-in 10,000 

total.

######################################################################

# DATA

# Model variable set-up
61

######################################################################

# Vector Catch() is total catch weight in thousands of pounds

# Vector S1() is the CPUE index in thousands of pounds / trip-hour

# sigma2 is process error

# tau2 is observation error by survey

NTIME <- length(DATA$Catch)

Catch <- DATA$Catch

OBS_CPUE <- DATA$CPUE 

NCPUE <- length(DATA$CPUE[!is.na(DATA$CPUE)]) # Total CPUE obs, minus NAs

#########################

# Model parameters

#########################

 K_Prior_Avg <- 700 # AmSam=700, Guam=300, CNMI=1400

 CV_K <- 0.2 # 0.20 for all territories

 r_Prior_Avg <- 0.46 # 0.46 for all territories

 CV_r <- 0.50 # 0.50 for all territories

 Target_P1_Prior_Avg <- 0.80 # AmSam=0.80, Guam=0.75, CNMI=0.45

 CV_P1 <- 0.2 # 0.20 for all territories

 OLO_MSY <- 75.0 # AmSam=75.0, Guam=55.0, CNMI=172.0

 CV_OLO_MSY <- 0.20 # 0.20 for all territories

 process_shape <- 0.2

 process_scale <- 0.1

 observation_shape <- 0.2

 observation_scale <- 1.0

 q_shape <- 0.001
62

 q_scale <- 0.001

 pLIM_B <- 0.70 # same for all territories

###############################################################

# Bundle Data

###############################################################

win.data <- list(

 NTIME = NTIME,

 Catch = Catch,

 OBS_CPUE = OBS_CPUE,

 NCPUE = NCPUE,

 K_Prior_Avg = K_Prior_Avg,

 CV_K = CV_K,

 r_Prior_Avg = r_Prior_Avg,

 CV_r = CV_r,

 Target_P1_Prior_Avg = Target_P1_Prior_Avg,

 CV_P1 = CV_P1,

 OLO_MSY = OLO_MSY,

 CV_OLO_MSY = CV_OLO_MSY,

 process_shape = process_shape,

 process_scale = process_scale,

 observation_shape = observation_shape,

 observation_scale = observation_scale,

 q_shape = q_shape,

 q_scale = q_scale,

 
63

 pLIM_B = pLIM_B

 ) # end data list

## END DATA

###################################################################

# Sampling using WinBUGS

# Define model written in WinBUGS code

sink("model.txt") 

cat("

model 

{

##############################################################################

# PRIOR DISTRIBUTIONS

##############################################################################

# Diffuse normal prior for carrying capacity parameter, K

#(PRIOR 1)###############################################################

K_Prior_Precision <- 1.0/pow(K_Prior_Avg*CV_K,2.0)

K ~ dnorm(K_Prior_Avg,K_Prior_Precision)I(0.00001,100000.0)

K_prior ~ dnorm(K_Prior_Avg,K_Prior_Precision)I(0.00001,100000.0)

# Beta prior for intrinsic growth rate parameter, r

#(PRIOR 2)###############################################################

tmp1 <- (1.0 - r_Prior_Avg)/r_Prior_Avg

tmp2 <- CV_r*CV_r

r_prior_a <- (tmp1-tmp2)/(tmp1*tmp2+tmp2)

r_prior_b <- r_prior_a*tmp1

r ~ dbeta(r_prior_a,r_prior_b)
64

r_prior ~ dbeta(r_prior_a,r_prior_b)

# Proper inverse-gamma prior for CPUE catchability coefficient

# proportional to 1/q within interval (0.00001,100000)

#(PRIOR 3)##################################################################

 q_inverse ~ dgamma(q_shape,q_scale)I(0.00001,100000)

 q <- 1/q_inverse

 q_prior <- 1/q_inverse

# Inverse gamma prior for process error variance, sigma2

#(PRIOR 4)##################################################################

 Process_Precision ~ dgamma(process_shape,process_scale)I(0.00001,100000)

 sigma2 <- 1/Process_Precision

 sigma2_prior <- 1/Process_Precision

# inverse gamma prior for observation error variance, tau2

#(PRIOR 5)################################################################

 CPUE_Precision ~ dgamma(observation_shape,observation_scale)I(0.00001,100000)

 tau2 <- 1/CPUE_Precision

 tau2_prior <- 1/CPUE_Precision

# Lognormal priors for time series of proportions of K, P[]

#(PRIOR 6)#################################################################

 P1_Prior_Precision <- 1.0/log(1.0+CV_P1*CV_P1)

 P1_Prior_Avg <-log(Target_P1_Prior_Avg) - (0.5/P1_Prior_Precision)

 P[1] ~ dlnorm(P1_Prior_Avg,P1_Prior_Precision) I(0.0001,10000)

 P_prior ~ dlnorm(P1_Prior_Avg,P1_Prior_Precision) I(0.0001,10000)

# Process dynamics for proportions of carrying capacity

#(PRIOR 7)#################################################################
65

 for (i in 2:NTIME) {

 PRED_P[i] <- log(max(P[i-1] + r*P[i-1]*(1-P[i-1]) - Catch[i-1]/K,0.0001))

 P[i] ~ dlnorm(PRED_P[i],Process_Precision)I(0.0001,10000)

 }

# Normal likelihood for observed MSY estimate from 1999 OLO

#(LIKELIHOOD 1)#############################################################

# MSY LIKELIHOOD 1999 OLO_MSY

 BMSY <- K/2.0

 BMSY_prior <- K/2.0

 PRED_MSY <- r*BMSY/2.0

 OBS_MSY <- OLO_MSY

 OLO_MSY_Precision <- 1.0/pow(OLO_MSY*CV_OLO_MSY,2.0)

 OBS_MSY ~ dnorm(PRED_MSY, OLO_MSY_Precision)

 RESID_MSY <- OBS_MSY - PRED_MSY

 STD_RESID_MSY <- RESID_MSY*sqrt(OLO_MSY_Precision)

# Lognormal likelihood for observed CPUE indices

#(LIKELIHOOD 2)############################################################

# CPUE LIKELIHOOD with OBS_CPUE

for (i in 1:NCPUE) {

 LOG_PRED_CPUE[i] <- log(q*K*P[i])

 OBS_CPUE[i] ~ dlnorm(LOG_PRED_CPUE[i],CPUE_Precision)

 LOG_RESID[i] <- log(OBS_CPUE[i]) - LOG_PRED_CPUE[i]

}

# Compute RSS and RMSE for CPUE in log-scale

LOG_RSS <- inprod(LOG_RESID[], LOG_RESID[])
66

LOG_RMSE <- sqrt(LOG_RSS/NTIME)

# Compute standardized log-scale residuals, predicted CPUE, and CPUE residuals

for (i in 1:NTIME) {

 STD_LOG_RESID[i] <- LOG_RESID[i]/LOG_RMSE

 PRED_CPUE[i] <- exp(LOG_PRED_CPUE[i])

 RESID[i] <- OBS_CPUE[i] - PRED_CPUE[i]

}

# Compute RSS and RMSE for CPUE

RSS <- inprod(RESID[], RESID[])

RMSE <- sqrt(RSS/NTIME)

# Compute standardized CPUE residuals

for (i in 1:NTIME) {

 STD_RESID[i] <- RESID[i]/RMSE

}

# Compute exploitable biomass and exploitation rate time series 

# (DERIVED OUTPUT 1)#######################################################

# Compute B and H with P

for (i in 1:NTIME) {

 B[i] <- P[i]*K

 H[i] <- min(Catch[i]/B[i],0.999)

}

 Pnext <- max(P[NTIME]+r*P[NTIME]*(1-P[NTIME])-Catch[NTIME]/K,0.0001) 

 Bnext <- Pnext*K

# Compute MSY-based biological reference points

# (DERIVED OUTPUT 2)#######################################################
67

 MSY <- r*K/4.0

 HMSY <- r/2.0

 PMSY <- BMSY/K

 FMSY <- -log(1-HMSY)

 INDEXMSY <- q*BMSY

 MSY_prior <- r*K/4.0

 HMSY_prior <- r/2.0

 PMSY_prior <- BMSY/K

 FMSY_prior <- -log(1-HMSY)

 INDEXMSY_prior <- q*BMSY

# Compute overfished and overfishing status and biomass production for 1982-2013

# (DERIVED OUTPUT 3)########################################################

for (i in 1:NTIME) {

 BSTATUS[i] <- B[i]/BMSY

 HSTATUS[i] <- H[i]/HMSY

 production[i] <- r*B[i]*(1.0-(B[i]/K))

}

 BSTATUSnext <- Bnext/BMSY

# Compute probabilities of overfishing and overfished 

# (DERIVED OUTPUT 4)#######################################################

for (i in 1:NTIME) {

 pOFL_H[i] <- step(HSTATUS[i] - 1.0)

 pBMSY_B[i] <- step(1.0 - BSTATUS[i])

 pOFL_B[i] <- step(pLIM_B - BSTATUS[i])

}
68

##############################################################################

} 

## END OF WinBUGS MODEL

",fill=TRUE)

sink() # ends the last diversion

###################################################################

# END OF CODE/MODEL

###################################################################

#######################################################################

############ Create list of inits for WinBUGS use #####################

#######################################################################

inits <- list( # create inits list of functions

 ## Initial Condition 1

list(

 K=700.0, 

 r=0.70,

 P=c(rep(0.80,15), rep(0.70, NTIME-15)),

 K_prior = 500.0,

 P_prior = 0.70,

 r_prior = 0.65,

 q_inverse=10,

 Process_Precision=100,

 

 CPUE_Precision=100
69

 )##END init 1

 ,

 ## Initial Condition 2

list(

 K=500.0, 

 r=0.60,

 P=c(rep(0.80,15), rep(0.7, NTIME-15)),

 K_prior = 600.0,

 P_prior = 0.70,

 r_prior = 0.45,

 q_inverse=10,

 Process_Precision=100,

 CPUE_Precision=100

 )##END init 2

 ,

 ## Initial Condition 3

list(

 K=700.0, 

 r=0.40,

 P=c(rep(0.80,15), rep(0.7, NTIME-15)),

 K_prior = 700.0,

 P_prior = 0.70,

 r_prior = 0.50,

 q_inverse=10,

 Process_Precision=100,
70

 

 CPUE_Precision=100

 )##END init 3

) ## close list of functions

##### end initials function ############################################

########################################################################

## Parameters to estimate

########################################################################

params <- c(

## model parameters ##

"K","r","q","sigma2","tau2", 

## time-series derived variables ##

"P","B","H","PRED_CPUE","Pnext","Bnext",

"PRED_P","production",

## management metrics ##

"MSY", "PRED_MSY","RESID_MSY","STD_RESID_MSY", #"OBS_MSY",

"PMSY","BMSY","HMSY","BSTATUS","HSTATUS","FMSY","BSTATUSnext",

"pOFL_H","pOFL_B", "pBMSY_B",

## statistics and diagnoses ##

"STD_LOG_RESID", "STD_RESID",

"LOG_RESID", "RESID",

"LOG_RSS", "LOG_RMSE",

"RSS", "RMSE"

)
71

# Check that number of Markov chains equals number of inits 

if (nc != length(inits)) {

 print("WARNING!!!! Num of inits does not equal num of chains")

 } else {

 print("Number of inits = number of chains")

}

begin_time = proc.time()[3]

###################################################################

# Start Gibbs sampling, cycle through the initials

bugs(win.data,inits,params,"model.txt",n.chains=nc,n.iter=ni,n.burnin=nb,n.thin=nt,

 debug=FALSE,codaPkg=FALSE,working.directory=src.dir)

###################################################################

end_time = proc.time()[3]

print(paste("RUN_COST = ",(end_time-begin_time)/60," mins",sep=""))

#######################################################################
72
73

APPENDIX B

Input data tables for running Bayesian state space surplus production models for the Bottomfish 

Management Unit Species of each of the 3 U.S. Pacific territories of American Samoa, the 

Commonwealth of the Northern Mariana Islands, and Guam, using data through 2013. These 

tables provide the same data as in Table 3, but formatted by each territory for use with Appendix 

A, generic stock assessment model code.

American Samoa Bottomfish Management Unit Species

Year Catch CPUE

1986 64.587 3.26

1987 19.628 2.98

1988 33.726 6.35

1989 32.647 4.02

1990 11.332 3.54

1991 13.01 2.64

1992 9.985 2.44

1993 14.554 3.27

1994 33.845 3.16

1995 27.699 4.24

1996 30.808 6.53

1997 32.308 3.82

1998 12.413 3.96

1999 15.857 3.67

2000 19.816 4.57

2001 37.847 4.95

2002 34.149 2.45

2003 19.199 5.42

2004 17.206 4.31

2005 16.329 3.13

2006 7.913 2.65

2007 21.874 2.57

2008 34.812 2.9

2009 47.458 3.62

2010 9.509 2.96

2011 26.277 3.95

2012 13.11 5.75

2013 23.63 4.25
74

Commonwealth of the Northern Mariana Islands Bottomfish Management Unit Species

Year Catch CPUE

1983 28.529 43

1984 42.665 70

1985 40.974 117

1986 29.912 104

1987 49.714 169

1988 47.313 181

1989 24.439 73

1990 12.929 81

1991 7.092 47

1992 10.598 59

1993 18.461 84

1994 25.47 74

1995 36.1 93

1996 66.388 119

1997 64.143 137

1998 59.024 148

1999 55.991 156

2000 45.258 56

2001 71.256 68

2002 46.765 101

2003 41.903 89

2004 54.475 104

2005 70.404 76

2006 29.34 NA

2007 39.476 NA

2008 42.07 NA

2009 41.176 NA

2010 22.395 NA

2011 22.487 NA

2012 15.302 NA

2013 22.51 NA
75

Guam Bottomfish Management Unit Species

Year Catch CPUE

1982 26.384 3.05

1983 40.782 2.66

1984 19.322 11.66

1985 49.195 2.46

1986 20.427 3.57

1987 29.301 3.98

1988 46.318 2.37

1989 58.582 2.28

1990 42.384 3.4

1991 39.596 2

1992 50.394 2.25

1993 55.609 2.98

1994 49.055 2.73

1995 40.855 2.05

1996 54.186 2.26

1997 30.611 1.32

1998 37.687 1.65

1999 53.339 1.88

2000 66.666 1.89

2001 54.352 3.25

2002 24.044 2.87

2003 43.253 4.26

2004 36.915 2.77

2005 36.529 4.81

2006 38.054 3.78

2007 27.459 2.32

2008 37.316 1.93

2009 40.222 3.17

2010 28.958 3.65

2011 59.618 3.62

2012 22.085 3.47

2013 29.848 3.15
76

(This page is left blank intentionally.)
77

Availability of NOAA Technical Memorandum NMFS

Copies of this and other documents in the NOAA Technical Memorandum NMFS series issued 

by the Pacific Islands Fisheries Science Center are available online at the PIFSC Web site 

http://www.pifsc.noaa.gov in PDF format. In addition, this series and a wide range of other 

NOAA documents are available in various formats from the National Technical Information 

Service, 5285 Port Royal Road, Springfield, VA 22161, U.S.A. [Tel: (703)-605-6000]; URL: 

http://www.ntis.gov. A fee may be charged.

Recent issues of NOAA Technical Memorandum NMFS–PIFSC are listed below:

NOAA-TM-NMFS-PIFSC-48 Status review report: humphead wrasse (Cheilinus Undulatus) 

GRAHAM, K. S., C. H. BOGGS, E. E. DEMARTINI, R. E. 

SCHROEDER, M. S. TRIANNI.

(October 2015) 

49 Development and testing of two towed volumetric hydrophone 

array prototypes to improve localization accuracy during 

shipboard line-transect cetacean surveys. 

BARKLEY, Y., J. BARLOW, S. RANKIN, G. D’SPAIN, AND 

E. OLESON.

(March 2016)

50 Injury determinations for marine mammals observed interacting

With Hawaii and American Samoa longline fisheries during 

2009-2013. 

BARKLEY, Y., J. BARLOW, S. RANKIN, G. D’SPAIN, AND 

E. OLESON.

(March 2016)