Introduction

The principal document for populating this operating model was the 2016 Stock Synthesis assessment (the ‘assessment report’ herein) conducted by CSIRO.

Custom parameters

The operating model was populated from a Maximum Likelihood Estimation run of Stock Synthesis using the function SS2OM. The SS input and output files include some accounting for uncertainty in various aspects of the operating model and following SS2OM the custom parameter slot (cpars) of the operating model includes simulation by simulation samples for:

  • Size vulnerability (V)

  • Recruitment variation (Perr)

  • Growth (Wt_age, K, Linf)

  • Steepness of the stock-recruitment curve (hs)(following bootstrapping)

  • Historical patterns in exploitation rate (Find)

However, when using an MLE fit of SS, SS2OM still produces a number of point (MLE) values for various parameters and variables that are likely to be uncertain.

In the build.R script we add custom parameters to introduce uncertainty into various parameters

  • Natural mortality rate - a lognormal random variable with CV of 15%. Ranges in the literature are from 0.21 to 0.46 and the assessment considers a point estimate of 0.27 (Day and Klaer, 2013).

  • Length at 50% maturity - a lognormal random variable with CV of 5%.

  • Stock Depletion - a lognormal random variable with a CV of 10 per cent following the uncertainty in spawning biomass shown in Figure 16.

Critical areas of uncertainty for robustness testing

  • TAC TAE and Size limit adherence (slots TACFrac, TACSD, TAEFrac, TAESD, SizeLimFrac, SizeLimSD). It is not clear how well management advice will be adhered to in the future.

  • The assessment document lists the sensitivity tests in section 2.2.5 any of which could be used for robustness testing.

Operating Model

The OM rdata file can be downloaded from here

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

Species Information

Species: Neoplatycephalus richardsoni

Common Name: tiger flathead

Management Agency: AFMA

Region: South Eastern Australia

Latitude: -33.5

Longitude: -147.5

OM Parameters

OM Name: Name of the operating model: OM generated by SS2OM function

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? 1

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: 3

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

No source provided. Author: No author provided.

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): 20

From SS3

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

Specified Value(s): 16777.22

From SS3. Unless MPs make catch recommendations in specific units (e.g. 200 tonnes) then R0 is just a scaler and it not consequential to MSE results.

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

Specified in cpars: 0.27, 0.27

From SS3. We add uncertainty in this using cpars, see above.

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

Specified Value(s): 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27, 0.27

We assumed age-invariant M.

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 size-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, 0

No variability in M simulated.

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): 0.61, 0.61

From the SS assessment. Variable steepness was created in the cpars slot by bootstrapping the SSB and recruitment estimates of the SS MLE estimates.

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

Specified Value(s): 1

As the SS assessment: the Beverton-Holt model.

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

Specified Value(s): 0.4, 0.4

From the SS 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): 0.57, 0.57

From the SS 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): 0, 0

From the SS assessment.

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

Specified Value(s): 0, 0

From the SS assessment.

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

Specified Value(s): 0, 0

From the SS 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): 0.15, 0.25

From Figure 5 of the assessment document, length CVs are around 0.15-0.25.

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

Seems to be stated in Table 16 that this might have a CV of 0.106

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

No data is available on changes in growth over time. The range was set to allow a small amount of inter-annual variation in the Linf parameter.

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 in cpars: 29.82, 29.82

From the SS Assessment. Using cpars, we arbitrarily added some uncertainty to the inflection point of the maturity curve of around +/- 10 percent.

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

Specified Value(s): 0, 0

From the SS 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: 0.35, 0.51

The assessment has a CV in depletion of around 10%.

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

Specified Value(s): 0, 0

We assume a relative high level of post release survival

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 SS assessment.

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

Specified Value(s): 3.31

From the SS 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): 0.5, 0.5

A mixed stock is assumed

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

Specified Value(s): 0.5, 0.5

A mixed stock is assumed

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): 0.5, 0.5

A mixed stock is assumed

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): 101

The assessment is from 1915 - 2015 so a total of 101 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

We use the default assumption that 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 SS assessment.

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

From the SS assessment.

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

From the SS assessment.

EffYears EffLower EffUpper
1915 0.0121 0.0121
1916 0.0123 0.0123
1917 0.0143 0.0143
1918 0.0218 0.0218
1919 0.0368 0.0368
1920 0.0293 0.0293
1921 0.0405 0.0405
1922 0.0264 0.0264
1923 0.0252 0.0252
1924 0.0440 0.0440
1925 0.0666 0.0666
1926 0.0773 0.0773
1927 0.1040 0.1040
1928 0.1350 0.1350
1929 0.1750 0.1750
1930 0.1820 0.1820
1931 0.1580 0.1580
1932 0.1710 0.1710
1933 0.1480 0.1480
1934 0.1550 0.1550
1935 0.1740 0.1740
1936 0.2350 0.2350
1937 0.2500 0.2500
1938 0.2780 0.2780
1939 0.2710 0.2710
1940 0.1760 0.1760
1941 0.1620 0.1620
1942 0.0395 0.0395
1943 0.0525 0.0525
1944 0.2970 0.2970
1945 0.3770 0.3770
1946 0.3780 0.3780
1947 0.3860 0.3860
1948 0.5850 0.5850
1949 0.2950 0.2950
1950 0.3290 0.3290
1951 0.4820 0.4820
1952 0.4630 0.4630
1953 0.5620 0.5620
1954 0.4270 0.4270
1955 0.4250 0.4250
1956 0.4330 0.4330
1957 0.3780 0.3780
1958 0.2750 0.2750
1959 0.2880 0.2880
1960 0.3860 0.3860
1961 0.2910 0.2910
1962 0.2340 0.2340
1963 0.5160 0.5160
1964 0.5530 0.5530
1965 0.5230 0.5230
1966 0.4160 0.4160
1967 0.4000 0.4000
1968 0.2930 0.2930
1969 0.3730 0.3730
1970 0.2930 0.2930
1971 0.2260 0.2260
1972 0.2740 0.2740
1973 0.3480 0.3480
1974 0.2410 0.2410
1975 0.3000 0.3000
1976 0.2310 0.2310
1977 0.2110 0.2110
1978 0.1690 0.1690
1979 0.1780 0.1780
1980 0.1650 0.1650
1981 0.1380 0.1380
1982 0.1560 0.1560
1983 0.1810 0.1810
1984 0.1780 0.1780
1985 0.1680 0.1680
1986 0.1570 0.1570
1987 0.1840 0.1840
1988 0.2040 0.2040
1989 0.2140 0.2140
1990 0.1980 0.1980
1991 0.2130 0.2130
1992 0.2090 0.2090
1993 0.1670 0.1670
1994 0.1700 0.1700
1995 0.1590 0.1590
1996 0.1620 0.1620
1997 0.2170 0.2170
1998 0.2250 0.2250
1999 0.3130 0.3130
2000 0.3150 0.3150
2001 0.2370 0.2370
2002 0.2540 0.2540
2003 0.2800 0.2800
2004 0.3330 0.3330
2005 0.3250 0.3250
2006 0.2700 0.2700
2007 0.2480 0.2480
2008 0.3010 0.3010
2009 0.2660 0.2660
2010 0.2870 0.2870
2011 0.2830 0.2830
2012 0.3100 0.3100
2013 0.2390 0.2390
2014 0.2390 0.2390
2015 0.2610 0.2610

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

Specified Value(s): 0, 0

From the SS assessment.

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): 0, 0

In the future, is a unit of effort going to lead to higher fishing mortality (positive qinc) or lower fishing mortality (negative qinc). We set the % annual increase to be very close to zero. We may wish to revisit this assumption after discussions in the workshop.

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, 0

Interannual variability in fishing efficiency. We allow for some variability in fishing efficiency among years, but may wish to adjust the range for this parameter after discussions in the workshop.

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, 0

From the SS assessment.

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

Specified Value(s): 0, 0

From the SS assessment.

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

Specified Value(s): 0, 0

From the SS assessment.

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

Specified Value(s): FALSE

The selectivity parameters are set as absolute sizes (not relative to the size of maturity).

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

We assume that discarding is size independent

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

Specified Value(s): 0, 0

We assume that discarding is size independent

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

Specified Value(s): 1, 1

We assume that discarding is size independent

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

Specified Value(s): 0.05, 0.1

Discard rates of between 5% and 10% following Figure 3 and Table 3 of the assessment report.

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): 2015

The most recent year of fishery data used in the stock assessment is 2015.

Existing Spatial Closures: MPA

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

Slot not used.

Obs Parameters

The observation model parameters are taken from the Generic_Obs model in DLMtool. Some adjustments are made below following the stock assessment.

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): 0.01, 0.01

Borrowed from Generic_Obs

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): 0.1

Borrowed from Generic_Obs

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

Specified Value(s): 800, 1200

Specified in the range of 800 - 1200 samples per year following recent sampling, described in Table 9 of the assessment document.

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): 800, 1200

Rate of non-independence uncertain so here we assume it is the same as CAA_nsamp

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

Specified Value(s): 4000, 5000

As above for the catch at age data. Here we use ranges from the most recent years e.g. 4000 - 5000.

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): 4000, 5000

Assumed to be the same as CAL_nsamp

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): 0.15, 0.3

Tables 5 and 6 indicate standardized indices range in precision between CVs of 0.15 and 0.3. Other indices have implausibly tight confidence intervals (‘D seine’) and are not considered.

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

Borrowed from Generic_Obs

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

Borrowed from 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

Borrowed from Generic_Obs

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): 0.5, 2

Unsure about hyperstability of indices.

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

Borrowed from 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

Borrowed from 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

Borrowed from Generic_Obs

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

Borrowed from Generic_Obs

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): 0.05

Borrowed from Generic_Obs

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

Borrowed from Generic_Obs

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

Borrowed from 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

Borrowed from Generic_Obs

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

Borrowed from Generic_Obs

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

Borrowed from Generic_Obs

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

Borrowed from Generic_Obs

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

Borrowed from Generic_Obs

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

Borrowed from Generic_Obs

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

Borrowed from Generic_Obs

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

Borrowed from Generic_Obs

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

Borrowed from Generic_Obs

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

Borrowed from 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): 0.92, 0.98

There is some degree of underages on average. Tables 1 and 2. I assume the range of underage is on average 0.925 - 0.975.

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): 0.02, 0.05

The catch limit is assumed to vary around the mean bias, relatiely closely (e.g. CV ~ 5%)

This is supported by Tables 1 and 2 from the assessment.

Effort Control Implementation Error: TAEFrac, TAESD

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

Specified Value(s): 0.92, 0.98

Hypothetical, assumed to be the same as for TAC

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): 0.02, 0.05

Hypothetical, assumed to be the same as for TAC

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): 0.8, 1.2

Hypothestical, assumed to vary among years by a small margin. CV between 5% and 10%.

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): 0.05, 0.1

Hypothetical, size limits may be adhered to between 80% and 120% of the size limit set.

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