Introduction

This operating model was specified from the Bayesian posterior estimates of an Awatea (Paul Starr) age-structured stock assessment corresponding to the following document:

DFO. 2017. Pacific Ocean Perch (Sebastes alutus) stock assessments for Queen Charlotte Sound, British Columbia in 2017. DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2017/043

Robustness tests

Two critical sources of uncertainty are the discard mortality rate and the level of dead discarding.

  1. The slot DR controls the general level of discarding. Here I assume it is between 10 and 20% but this is simply a guess and requires respecification.

  2. The default range for Fdisc is assumed to be 50-100 percent. This could be influencial with management measures such as size limits.

  3. Until a dedicated multistock version of OMx can be implemented the best option for considering the impact of male dynamics may be to consider male growth and mortality as a robustness set.

  4. The observation model needs to be updated with fits the observed indices, catches and numbers of catch composition observations per year.

Operating Model

The OM rdata file can be downloaded from here

Download and import into R using myOM <- readRDS('OM.rdata')

Species Information

Species: Sebastes alutus

Common Name: Pacific Ocean Perch

Management Agency: DFO

Region: Queen Charlotte Islands, B.C.

Latitude: 51.7

Longitude: -131.5

OM Parameters

OM Name: Name of the operating model: REPLACED – Pacific_Ocean_Perch_QC_BC_DFO

nsim: The number of simulations: 192

proyears: The number of projected years: 50

interval: The assessment interval - how often would you like to update the management system? 4

pstar: The percentile of the sample of the management recommendation for each method: 0.5

maxF: Maximum instantaneous fishing mortality rate that may be simulated for any given age class: 0.8

reps: Number of samples of the management recommendation for each method. Note that when this is set to 1, the mean value of the data inputs is used. 1

Source: A reference to a website or article from which parameters were taken to define the operating model

http://www.iccat.int/Documents/Meetings/Docs/2013_ALB_ASSESS_REP_ENG.pdf

Custom Parameters

The following correlated parameters were sampled from the MCMC output of the Awatea assessment model:

  • Fishing mortality rate by year

  • Natural mortality rate

  • Steepness of the Beverton-Holt stock recruitment curve

  • Selectivity at age

  • Maturity at age

  • Stock depletion

  • Unfished recruitment

  • Recruitment deviations

Stock Parameters

Mortality and age: maxage, R0, M, M2, Mexp, Msd

maxage: The maximum age of individuals that is simulated (there is no plus group ). Single value. Positive integer

Specified Value(s): 60

From the Awatea assessment.

R0: The magnitude of unfished recruitment. Single value. Positive real number

Specified in cpars: 13390.1, 23064.5

From the Awatea assessment.

M: Natural mortality rate. Uniform distribution lower and upper bounds. Positive real number

Specified in cpars: 0.05, 0.07

From the Awatea assessment.

M2: (Optional) Natural mortality rate at age. Vector of length maxage . Positive real number

Slot not used.

Mexp: Exponent of the Lorenzen function assuming an inverse relationship between M and weight. Uniform distribution lower and upper bounds. Real numbers <= 0.

Specified Value(s): 0, 0

We assumed age-invariant M.

Msd: Inter-annual variability in natural mortality rate expressed as a coefficient of variation. Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0.05, 0.1

Low interannual variability in M.

Natural Mortality Parameters

Sampled Parameters

Histograms of 48 simulations of M, Mexp, and Msd parameters, with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

The average natural mortality rate by year for adult fish for 3 simulations. The vertical dashed line indicates the end of the historical period:

M-at-Age

Natural mortality-at-age for 3 simulations in the first historical year, the last historical year (i.e., current year), and the last projected year:

M-at-Length

Natural mortality-at-length for 3 simulations in the first historical year, the last historical year (i.e., current year), and the last projected year:

Recruitment: h, SRrel, Perr, AC

h: Steepness of the stock recruit relationship. Uniform distribution lower and upper bounds. Values from 1/5 to 1

Specified Value(s): 0.3, 0.97

From the Awatea assessment.

SRrel: Type of stock-recruit relationship. Single value, switch (1) Beverton-Holt (2) Ricker. Integer

Specified Value(s): 1

Beverton-Holt dynamics.

Perr: Process error, the CV of lognormal recruitment deviations. Uniform distribution lower and upper bounds. Non-negative real numbers

Specified in cpars: 0.02, 13.47

From the Awatea assessment.

AC: Autocorrelation in recruitment deviations rec(t)=ACrec(t-1)+(1-AC)sigma(t). Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0.22, 0.61

Calculated by simulation from the posterior estimates of recruitment deviations from the Awatea assessment

Recruitment Parameters

Sampled Parameters

Histograms of 48 simulations of steepness (h), recruitment process error (Perr) and auto-correlation (AC) for the Beverton-Holt stock-recruitment relationship, with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

Non-stationarity in stock productivity: Period, Amplitude

Period: (Optional) Period for cyclical recruitment pattern in years. Uniform distribution lower and upper bounds. Non-negative real numbers

Slot not used.

Amplitude: (Optional) Amplitude in deviation from long-term average recruitment during recruitment cycle (eg a range from 0 to 1 means recruitment decreases or increases by up to 100% each cycle). Uniform distribution lower and upper bounds. 0 < Amplitude < 1

Slot not used.

Growth: Linf, K, t0, LenCV, Ksd, Linfsd

Linf: Maximum length. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 44.16, 44.16

From the Awatea assessment.

K: von Bertalanffy growth parameter k. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.16, 0.16

From the Awatea assessment.

t0: von Bertalanffy theoretical age at length zero. Uniform distribution lower and upper bounds. Non-positive real numbers

Specified Value(s): -0.65, -0.65

From the Awatea assessment.

LenCV: Coefficient of variation of length-at-age (assumed constant for all age classes). Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.05, 0.05

Derived from the St.Dev. at maximum length reported by the assessment divided by the maximum length (Linf) of the assessment input file.

Ksd: Inter-annual variability in growth parameter k expressed as coefficient of variation. Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0, 0.02

No interannual variability in growth rate.

Linfsd: Inter-annual variability in maximum length expressed as a coefficient of variation. Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0, 0.02

No interannual variability in maximum length.

Growth Parameters

Sampled Parameters

Histograms of 48 simulations of von Bertalanffy growth parameters Linf, K, and t0, and inter-annual variability in Linf and K (Linfsd and Ksd), with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

The Linf and K parameters in each year for 3 simulations. The vertical dashed line indicates the end of the historical period:

Growth Curves

Sampled length-at-age curves for 3 simulations in the first historical year, the last historical year, and the last projection year.

Maturity: L50, L50_95

L50: Length at 50 percent maturity. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 29.44, 29.44

Maturity at age is takend directly from the assessment.

L50_95: Length increment from 50 percent to 95 percent maturity. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 2.21, 2.21

From the Awatea assessment.

Maturity Parameters

Sampled Parameters

Histograms of 48 simulations of L50 (length at 50% maturity), L95 (length at 95% maturity), and corresponding derived age at maturity parameters (A50 and A95), with vertical colored lines indicating 3 randomly drawn values used in other plots:

Maturity at Age and Length

Maturity-at-age and -length for 3 simulations in the first historical year, the last historical year (i.e., current year), and the last projected year:

Stock depletion and Discard Mortality: D, Fdisc

D: Current level of stock depletion SSB(current)/SSB(unfished). Uniform distribution lower and upper bounds. Fraction

Specified in cpars: 0.18, 0.43

From the Awatea assessment.

Fdisc: Fraction of discarded fish that die. Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0.5, 1

Discard mortality rate was set to an uncertain range of 50 per cent to 100 percent, a lower bound below that published by the Pacific Management Council

Depletion and Discard Mortality

Sampled Parameters

Histograms of 48 simulations of depletion (spawning biomass in the last historical year over average unfished spawning biomass; D) and the fraction of discarded fish that are killed by fishing mortality (Fdisc), with vertical colored lines indicating 3 randomly drawn values.

Length-weight conversion parameters: a, b

a: Length-weight parameter alpha. Single value. Positive real number

Specified Value(s): 0

From the Awatea assessment.

b: Length-weight parameter beta. Single value. Positive real number

Specified Value(s): 3.11

From the Awatea assessment.

Spatial distribution and movement: Size_area_1, Frac_area_1, Prob_staying

Size_area_1: The size of area 1 relative to area 2. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.1, 0.1

Population is uniformly distributed: size area 1 is same as frac area 1.

Frac_area_1: The fraction of the unfished biomass in stock 1. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.1, 0.1

To simulate a mixed stock we simulate a stock in which 10% of individuals are in area 1 and 90% are in area 2, 0.1.

Prob_staying: The probability of inviduals in area 1 remaining in area 1 over the course of one year. Uniform distribution lower and upper bounds. Positive fraction.

Specified Value(s): 0.8, 0.9

To simulate uncertain mixing among areas I assume that between 85 per cent and 95 per cent of individuals remain in the same area among years.

Spatial & Movement

Sampled Parameters

Histograms of 48 simulations of size of area 1 (Size_area_1), fraction of unfished biomass in area 1 (Frac_area_1), and the probability of staying in area 1 in a year (Frac_area_1), with vertical colored lines indicating 3 randomly drawn values used in other plots:

Fleet Parameters

Historical years of fishing, spatial targeting: nyears, Spat_targ

nyears: The number of years for the historical spool-up simulation. Single value. Positive integer

Specified Value(s): 77

The data from the assessment run from 1941-2017, a total of 77 years.

Spat_targ: Distribution of fishing in relation to spatial biomass: fishing distribution is proportional to B^Spat_targ. Uniform distribution lower and upper bounds. Real numbers

Specified Value(s): 1, 1

Spatial targetting. We’re going to stick to the default level of 1 (effort distributed in proportion to density)

Trend in historical fishing effort (exploitation rate), interannual variability in fishing effort: EffYears, EffLower, EffUpper, Esd

EffYears: Years representing join-points (vertices) of time-varying effort. Vector. Non-negative real numbers

From the Awatea assessment.

EffLower: Lower bound on relative effort corresponding to EffYears. Vector. Non-negative real numbers

From the Awatea assessment.

EffUpper: Upper bound on relative effort corresponding to EffYears. Vector. Non-negative real numbers

From the Awatea assessment.

EffYears EffLower EffUpper
1940 0.00285 0.00459
1941 0.00164 0.00265
1942 0.01880 0.03020
1943 0.05680 0.09140
1944 0.02610 0.04200
1945 0.22500 0.36100
1946 0.12500 0.20000
1947 0.06530 0.10400
1948 0.10400 0.16600
1949 0.12700 0.20500
1950 0.12200 0.19700
1951 0.13300 0.21800
1952 0.11900 0.19700
1953 0.09540 0.15900
1954 0.31200 0.50700
1955 0.07390 0.12100
1956 0.17500 0.29000
1957 0.13400 0.22300
1958 0.11800 0.20000
1959 0.23300 0.39600
1960 0.19900 0.32700
1961 0.11600 0.19500
1962 0.17600 0.28400
1963 0.35100 0.55900
1964 0.32900 0.54000
1965 0.76400 1.28000
1966 2.30000 3.90000
1967 2.09000 3.52000
1968 1.72000 2.85000
1969 1.46000 2.37000
1970 1.25000 2.00000
1971 0.72700 1.14000
1972 1.13000 1.77000
1973 1.07000 1.69000
1974 1.78000 2.73000
1975 1.14000 1.69000
1976 0.58300 0.85000
1977 0.26400 0.38000
1978 0.65800 0.92500
1979 0.41400 0.57100
1980 0.94600 1.29000
1981 0.97400 1.34000
1982 1.29000 1.78000
1983 1.29000 1.77000
1984 1.04000 1.40000
1985 0.98700 1.33000
1986 0.35500 0.49400
1987 0.99400 1.31000
1988 1.11000 1.40000
1989 0.73300 0.91400
1990 0.77000 0.96600
1991 0.82400 1.01000
1992 0.82000 0.99200
1993 0.68000 0.82500
1994 1.13000 1.37000
1995 1.38000 1.66000
1996 1.23000 1.46000
1997 1.21000 1.42000
1998 1.25000 1.47000
1999 1.25000 1.51000
2000 1.43000 1.81000
2001 1.31000 1.70000
2002 1.44000 1.94000
2003 1.67000 2.38000
2004 1.62000 2.46000
2005 1.37000 2.19000
2006 1.64000 2.77000
2007 1.42000 2.56000
2008 1.11000 2.10000
2009 1.14000 2.23000
2010 1.43000 2.86000
2011 0.97800 2.07000
2012 0.92600 2.11000
2013 0.61600 1.48000
2014 0.48400 1.19000
2015 0.73900 1.78000
2016 0.74700 1.90000

Esd: Additional inter-annual variability in fishing mortality rate. Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0.1, 0.4

Since the Awatea assessment provides detailed annual variability information there is no need to superimpose greater variability. It is set to 0.

Historical Effort

Sampled Parameters

Histograms of 48 simulations of inter-annual variability in historical fishing mortality (Esd), with vertical colored lines indicating 3 randomly drawn values used in the time-series plot:

Time-Series

Time-series plot showing 3 trends in historical fishing mortality (OM@EffUpper and OM@EffLower or OM@cpars$Find):

Annual increase in catchability, interannual variability in catchability: qinc, qcv

qinc: Average percentage change in fishing efficiency (applicable only to forward projection and input controls). Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): -2, 2

There is no compelling reason to expect fishing to become more or less efficient and we set the % annual increase to be very close to zero, -0.1 to 0.1.

qcv: Inter-annual variability in fishing efficiency (applicable only to forward projection and input controls). Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0.1, 0.3

Very low variability in catchability among years

Future Catchability

Sampled Parameters

Histograms of 48 simulations of inter-annual variability in fishing efficiency (qcv) and average annual change in fishing efficiency (qinc), with vertical colored lines indicating 3 randomly drawn values used in the time-series plot:

Time-Series

Time-series plot showing 3 trends in future fishing efficiency (catchability):

Fishery gear length selectivity: L5, LFS, Vmaxlen, isRel

L5: Shortest length corresponding to 5 percent vulnerability. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.2, 0.4

From the Awatea assessment.

LFS: Shortest length that is fully vulnerable to fishing. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.75, 1.1

From the Awatea assessment.

Vmaxlen: The vulnerability of fish at . Uniform distribution lower and upper bounds. Fraction

Specified Value(s): 0, 1

From the Awatea assessment.

isRel: Selectivity parameters in units of size-of-maturity (or absolute eg cm). Single value. Boolean.

Specified Value(s): FALSE

In this case we are not specifying L5 and LFS as a fraction of length at maturity but rather in absolute units (cm) the same as those of the growth and maturity parameters.

Fishery length retention: LR5, LFR, Rmaxlen, DR

LR5: Shortest length corresponding ot 5 percent retention. Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0, 0

Retention follows selectivity.

LFR: Shortest length that is fully retained. Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0, 0

Retention follows selectivity.

Rmaxlen: The retention of fish at . Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 1, 1

Retention follows selectivity.

DR: Discard rate - the fraction of caught fish that are discarded. Uniform distribution lower and upper bounds. Fraction

Specified Value(s): 0.1, 0.2

Assume between 10% and 20% discard rate.

Time-varying selectivity: SelYears, AbsSelYears, L5Lower, L5Upper, LFSLower, LFSUpper, VmaxLower, VmaxUpper

SelYears: (Optional) Years representing join-points (vertices) at which historical selectivity pattern changes. Vector. Positive real numbers

Slot not used.

AbsSelYears: (Optional) Calendar years corresponding with SelYears (eg 1951, rather than 1), used for plotting only. Vector (of same length as SelYears). Positive real numbers

Slot not used.

L5Lower: (Optional) Lower bound of L5 (use ChooseSelect function to set these). Vector. Non-negative real numbers

Slot not used.

L5Upper: (Optional) Upper bound of L5 (use ChooseSelect function to set these). Vector. Non-negative real numbers

Slot not used.

LFSLower: (Optional) Lower bound of LFS (use ChooseSelect function to set these). Vector. Non-negative real numbers

Slot not used.

LFSUpper: (Optional) Upper bound of LFS (use ChooseSelect function to set these). Vector. Non-negative real numbers

Slot not used.

VmaxLower: (Optional) Lower bound of Vmaxlen (use ChooseSelect function to set these). Vector. Fraction

Slot not used.

VmaxUpper: (Optional) Upper bound of Vmaxlen (use ChooseSelect function to set these). Vector. Fraction

Slot not used.

Current Year: CurrentYr

CurrentYr: The current calendar year (final year) of the historical simulations (eg 2011). Single value. Positive integer.

Specified Value(s): 2016

Existing Spatial Closures: MPA

MPA: (Optional) Matrix specifying spatial closures for historical years.

Slot not used.

Obs Parameters

The observation model parameter are taken from the Generic_Obs model subject to a few addtional changes which are documented here.

Catch statistics: Cobs, Cbiascv, CAA_nsamp, CAA_ESS, CAL_nsamp, CAL_ESS

Cobs: Log-normal catch observation error expressed as a coefficient of variation. Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0.05, 0.1

Catches are observed more precisely than the Generic_Obs object with a CV of between 5 and 10 per cent.

Cbiascv: Log-normal coefficient of variation controlling the sampling of bias in catch observations for each simulation. Uniform distribution lower and upper bounds. Non-negative real numbers

Specified Value(s): 0.05

Mean bias (under / over reporting) in catches is assumed to be small with a CV of 0.05 95% of simulations are reported between 90% and 110% of true simulated catches.

CAA_nsamp: Number of catch-at-age observation per time step. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 100, 200

CAA_ESS: Effective sample size (independent age draws) of the multinomial catch-at-age observation error model. Uniform distribution lower and upper bounds. Positive integers

Specified Value(s): 100, 200

Effective sample size is identical to CAA_nsamp

CAL_nsamp: Number of catch-at-length observation per time step. Uniform distribution lower and upper bounds. Positive integers

Specified Value(s): 100, 200

Assumed to be the same as catch at age

CAL_ESS: Effective sample size (independent length draws) of the multinomial catch-at-length observation error model. Uniform distribution lower and upper bounds. Positive integers

Specified Value(s): 100, 200

Assumed to be the same as catch at age

Index imprecision, bias and hyperstability: Iobs, Ibiascv, Btobs, Btbiascv, beta

Iobs: Observation error in the relative abundance indices expressed as a coefficient of variation. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.08, 0.12

Relative abundance indices are assumed to be observed imprecisely with a CV of roughly 20 percent.

Ibiascv: Not Used. Log-normal coefficient of variation controlling error in observations of relative abundance index. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.2

This parameter is not used in this version of DLMtool.

Btobs: Log-normal coefficient of variation controlling error in observations of current stock biomass among years. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.2, 0.5

As Generic_Obs.

Btbiascv: Uniform-log bounds for sampling persistent bias in current stock biomass. Uniform-log distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.33, 3

The bias in the absolute abundance index is assumed to be reasonably high and could be 1/3 to 3 times the true value.

beta: A parameter controlling hyperstability/hyperdepletion where values below 1 lead to hyperstability (an index that decreases slower than true abundance) and values above 1 lead to hyperdepletion (an index that decreases more rapidly than true abundance). Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.67, 1.5

Since the survey is carried out according to a systematic design we assume that it varies roughly proportionally to real abundance and specify a beta parameter between 2/3 and 3/2.

Bias in maturity, natural mortality rate and growth parameters: LenMbiascv, Mbiascv, Kbiascv,t0biascv, Linfbiascv

LenMbiascv: Log-normal coefficient of variation for sampling persistent bias in length at 50 percent maturity. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.1

As Generic_Obs.

Mbiascv: Log-normal coefficient of variation for sampling persistent bias in observed natural mortality rate. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.2

As Generic_Obs.

Kbiascv: Log-normal coefficient of variation for sampling persistent bias in observed growth parameter K. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.1

Twice as accurate as Generic_Obs with K values within plus or minus 5% of true value.

t0biascv: Log-normal coefficient of variation for sampling persistent bias in observed t0. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.1

We choose not to simulate bias in this growth parameter and assume in all cases it is correct.

Linfbiascv: Log-normal coefficient of variation for sampling persistent bias in observed maximum length. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.02

Twice as accurate as Generic_Obs with L-infinity values within plus or minus 5% of true value.

Bias in length at first capture, length at full selection: LFCbiascv, LFSbiascv

LFCbiascv: Log-normal coefficient of variation for sampling persistent bias in observed length at first capture. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.05

Given the reasonably extensive length sampling data, it is straightforward to estimate Length at First Capture for rockfish from the length frequency data and this is likely to be reasonably well known without substantial bias.

LFSbiascv: Log-normal coefficient of variation for sampling persistent bias in length-at-full selection. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.05

As Generic_Obs

Bias in fishery reference points, unfished biomass, FMSY, FMSY/M ratio, biomass at MSY relative to unfished: FMSYbiascv, FMSY_Mbiascv, BMSY_B0biascv

FMSYbiascv: Not used. Log-normal coefficient of variation for sampling persistent bias in FMSY. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.2

It is not clear how biased such an estimate could be but assuming that this uncertainty reflects the possible range of prescribed values (and brackets the true ratio) and this occurs on top of bias in estimates of natural mortality, the range of possible biases must be higher than that assigned to M (0.2). This is set at 0.3 to reflect the potential for inaccurate estimates of FMSY.

FMSY_Mbiascv: Log-normal coefficient of variation for sampling persistent bias in FMSY/M. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.2

A number of MPs aim to fish at a fixed rate proportional to the estimate of M (e.g. Fratio). Other MPs use this ratio to undertake stock reduction analysis (e.g. DB-SRA). Given the references above we set this to be moderately inaccurate given a CV of 0.15.

BMSY_B0biascv: Log-normal coefficient of variation for sampling persistent bias in BMSY relative to unfished. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.2

We have assigned a relatively precise CV for potential accuracy at 0.05.

Management targets in terms of the index (i.e., model free), the total annual catches and absolute biomass levels: Irefbiascv, Crefbiascv, Brefbiascv

Irefbiascv: Log-normal coefficient of variation for sampling persistent bias in relative abundance index at BMSY. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.2

Here we assume that the index and MSY (a desirable catch level) can be known more accurately than a desirable absolute biomass level (e.g. BMSY) and assign these a range determined by a CV of 0.2.

Crefbiascv: Log-normal coefficient of variation for sampling persistent bias in MSY. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.2

As Irefbiascv.

Brefbiascv: Log-normal coefficient of variation for sampling persistent bias in BMSY. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.5

Arbitrarily we make this twice as potentially biased as Iref and Cref.

Depletion bias and imprecision: Dbiascv, Dobs

Dbiascv: Log-normal coefficient of variation for sampling persistent bias in stock depletion. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.5

These are probably the most controversial observation model quantities, after all the most valuable output of a data-rich assessment is arguably the level of stock depletion (typically measured as spawning stock biomass today relative to unfished). If we could know this, for stocks with stationary productivity (where depletion is a very good predictor of stock productivity) we could achieve very good management performance with simple harvest control rules (in essence this is how the outputs of data-rich stock assessments are used).

Having said this, in most cases assessments are evaluated based on their fit to a fishery dependent (e.g. catch per unit effort) or fishery independent (e.g. trawl survey, acoustic survey) relative abundance index. It follows that often the depletion estimate arising from a stock assessment follows the raw data fairly well. Consequently, even anecdotal historical catch rate data may be used in a data-limited context to frame estimates of stock depletion. Similarly, if unfished densities of a species can be quantified (e.g. urchins per sq km of habitat), total estimates of habitat and current density surveys could be used to extrapolate a range of stock depletion.

Alternatively, length frequency data can provide an imprecise estimate of stock epletion when accompanied with estimates of natural mortality rate and growth (and some assumption about the pattern of recent fishing rates).

Here we assign an arbitrary value of 0.25 which is relatively imprecise and means that assume depletion could up to double or half of the true simulated value.

Dobs: Log-normal coefficient of variation controlling error in observations of stock depletion among years. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.05, 0.1

In a data-limited situation it is unlikely that radically new data would become available regarding depletion meaning that while estimates may be biased, they are likely to be relatively precise. We assign a level of imprecision consistent with observations of catch rate data among years at between 0.05- 0.1.

Recruitment compensation and trend: hbiascv, Recbiascv

hbiascv: Log-normal coefficient of variation for sampling persistent bias in steepness. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.2

The stock assessment provides very little support for particular values of recruitment compensation. In DLMtool this is parameterized as steepness (the fraction of unfished recruitment at 20% of unfished spawning biomass, a value ranging from 0.2-1). Here we assume that any MP could get this wrong by a large margin.

Recbiascv: Log-normal coefficient of variation for sampling persistent bias in recent recruitment strength. Uniform distribution lower and upper bounds. Positive real numbers

Specified Value(s): 0.1, 0.3

As Generic_Obs

Obs Plots

Observation Parameters

Catch Observations

Sampled Parameters

Histograms of 48 simulations of inter-annual variability in catch observations (Csd) and persistent bias in observed catch (Cbias), with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

Depletion Observations

Sampled Parameters

Histograms of 48 simulations of inter-annual variability in depletion observations (Dobs) and persistent bias in observed depletion (Dbias), with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

Abundance Observations

Sampled Parameters

Histograms of 48 simulations of inter-annual variability in abundance observations (Btobs) and persistent bias in observed abundance (Btbias), with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

Index Observations

Sampled Parameters

Histograms of 48 simulations of inter-annual variability in index observations (Iobs) and hyper-stability/depletion in observed index (beta), with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

Time-series plot of 3 samples of index observation error:

Plot showing an example true abundance index (blue) with 3 samples of index observation error and the hyper-stability/depletion parameter (beta):

Recruitment Observations

Sampled Parameters

Histograms of 48 simulations of inter-annual variability in index observations (Recsd) , with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

Composition Observations

Sampled Parameters

Histograms of 48 simulations of catch-at-age effective sample size (CAA_ESS) and sample size (CAA_nsamp) and catch-at-length effective (CAL_ESS) and actual sample size (CAL_nsamp) with vertical colored lines indicating 3 randomly drawn values:

Parameter Observations

Sampled Parameters

Histograms of 48 simulations of bias in observed natural mortality (Mbias), von Bertalanffy growth function parameters (Linfbias, Kbias, and t0bias), length-at-maturity (lenMbias), and bias in observed length at first capture (LFCbias) and first length at full capture (LFSbias) with vertical colored lines indicating 3 randomly drawn values:

Reference Point Observations

Sampled Parameters

Histograms of 48 simulations of bias in observed FMSY/M (FMSY_Mbias), BMSY/B0 (BMSY_B0bias), reference index (Irefbias), reference abundance (Brefbias) and reference catch (Crefbias), with vertical colored lines indicating 3 randomly drawn values:

Imp Parameters

Output Control Implementation Error: TACFrac, TACSD

TACFrac: Mean fraction of TAC taken. Uniform distribution lower and upper bounds. Positive real number.

Specified Value(s): 1, 1.1

Here we assume that the actual catches can be up to 10% higher than the recommended TAC.

TACSD: Log-normal coefficient of variation in the fraction of Total Allowable Catch (TAC) taken. Uniform distribution lower and upper bounds. Non-negative real numbers.

Specified Value(s): 0.1, 0.1

We assume that the bias in the actual catch is relatively consistent between years and set the range for this parameter to a low value.

Effort Control Implementation Error: TAEFrac, TAESD

TAEFrac: Mean fraction of TAE taken. Uniform distribution lower and upper bounds. Positive real number.

Specified Value(s): 1, 1.1

We have little information to inform this parameter, and set the implementation error in effort equal to the TAC implementation error.

TAESD: Log-normal coefficient of variation in the fraction of Total Allowable Effort (TAE) taken. Uniform distribution lower and upper bounds. Non-negative real numbers.

Specified Value(s): 0.1, 0.1

We assume that the bias in the effort is relatively consistent between years and set the range for this parameter to a low value.

Size Limit Control Implementation Error: SizeLimFrac, SizeLimSD

SizeLimFrac: The real minimum size that is retained expressed as a fraction of the size. Uniform distribution lower and upper bounds. Positive real number.

Specified Value(s): 0.9, 1.1

We assume that, on average, a size limit would be well-implemented within +/- 10% of the true limit.

SizeLimSD: Log-normal coefficient of variation controlling mismatch between a minimum size limit and the real minimum size retained. Uniform distribution lower and upper bounds. Non-negative real numbers.

Specified Value(s): 0.1, 0.1

We assume that the implementation of the size limit is relatively consistent between years.

Imp Plots

Implementation Parameters

TAC Implementation

Sampled Parameters

Histograms of 48 simulations of inter-annual variability in TAC implementation error (TACSD) and persistent bias in TAC implementation (TACFrac), with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

TAE Implementation

Sampled Parameters

Histograms of 48 simulations of inter-annual variability in TAE implementation error (TAESD) and persistent bias in TAC implementation (TAEFrac), with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

Size Limit Implementation

Sampled Parameters

Histograms of 48 simulations of inter-annual variability in size limit implementation error (SizeLimSD) and persistent bias in size limit implementation (SizeLimFrac), with vertical colored lines indicating 3 randomly drawn values used in other plots:

Time-Series

Historical Simulation Plots

Historical Time-Series

Spawning Biomass

Depletion

Absolute

Vulnerable Biomass

Depletion

Absolute

Total Biomass

Depletion

Absolute

Recruitment

Relative

Absolute

Catch

Relative

Absolute

Historical Fishing Mortality

Historical Time-Series