Operating model for Indian Ocean Blue Shark (Prionace glauca)
An example DLMtool operating model, November 2017
Tom Carruthers, University of British Columbia [email protected]
Rui Coelho, Portuguese Institute for the Ocean and Atmosphere, I.P. (IPMA) [email protected]
December 2017
Introduction
The principal document for populating this operating model was the 2017 Stock Synthesis assessment document:
- Rice.J., (2017) Stock assessment of blue shark (Prionace glauca) in the Indian Ocean using Stock Synthesis. IOTC Secretariat.
For this initial base-case analysis we are running 100 simulations. It may be necessary to check convergence of the MSE using CheckConverge() and then increase the number of simulations in the build.R script.
Custom parameters
The operating model was populated from a Maximum Likelihood Estimation run of Stock Synthesis using the funciton SS2DLM. The SS input and output files include some accounting for uncertainty in various aspects of the operating model and following SS2DLM 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%. This is a guess and based on the typical level of uncertainty assumed in the M parameter for other stocks.
Length at 50% maturity - a lognormal randoom variable with CV of 5%. This uncertainty is based on the maturity study of Santiago et al. 2014
- Stock Depletion - a lognormal random variable with a CV of 8.5 per cent following the variation in CPUE indices:
Critical areas of uncertainty for robustness testing
Discarding rate (slot DR). In this preliminary model I had no idea what fraction of the landings were discarded
TAC TAE and Size limit adherence (slots TACFrac, TACSD, TAEFrac, TAESD, SizeLimFrac, SizeLimSD). I had no idea how well management advice was adhered to
Observation model parameters. I had no idea how well data are observed. A possible robustness test could use the parameters of the Imprecise_Biased observation model, for example.
Catches (see data and plot): The final model uses only one of the
alternative catches reconstructed (called “Estimated-GAM”). But we have
several other options (see data and plot). Of those, the ones we should
probably include and test are the actual IOTC reported catches (that would
be the lowest values) and other series based on target/bycatch ratios
(called “Estimated_EUPOA”). I am not sure if you can use several catch
series to condition the OM, but if that is possible it would be great to use
those 3 hypothesis:1) Estimate-GAM (base case); 2) IOTC Nominal (low option) and 3) Estimate
EUPOA (ratio based estimates with highest values).
- Steepness (see doc IOTC-2017-WPEB13-21_Rev1 attached): Our final model uses
only 1 fixed option at 0.79 (comes from the pop dynamics paper attached). As
this is a very important parameter to consider in conditioning the OM and
test for robustness, I would say we could use 3 scenarios (as used, plus a
high and a low). Maybe something like increase/decrease 0.1 from the base
case:1) 0.79 (base case); 2) 0.89 (high option); 3) 0.69 (low option).
- Natural mortality: We are using an age-specific M (Peterson and Wroblewski,
1984, based on life history). We didn’t thought too much about this one, but
this vector of age/sex-specific M should be OK.
- CPUE series options: We have various CPUEs options from various fleets (see
file attached). The final model uses 3 series: Portugal, France and
Japan-late. We do have other options of series that were used as
sensitivities in the assessment. See the Table 3 in the PDF doc
(IOTC-2017-WPEB12-33_BSH_SS3…) attached that lists the CPUE series
alternatives. It would be great if we could use those same options in the
OM. The dataset (and plot) of the CPUE series are attached.
- CPUEs catchability increase: This is a good point, likely also related with
hyper-stability, and we don’t really consider this too much in the
assessments. In the conditioning of the OM we could maybe consider 2
options:1) Use the CPUE series as is (we assume the GLMs can standardize the series
for everything…); 2) add maybe a 1% annual yearly increase to the CPUEs,
to take into account factors like increasing efficiency that are not taken
into account in the GLMs.
Operating Model
The OM rdata file can be downloaded from here
Download and import into R using myOM <- readRDS('OM.rdata')
Species Information
Species: Prionace glauca
Common Name: Blue Shark
Management Agency: IOTC
Region: Indian Ocean
Latitude: -50, -50, -27, -10, 25, 25, 35
Longitude: 20, 150, 150, 100, 95, 45, 20
OM Parameters
OM Name: Name of the operating model: REPLACED – Blue_Shark_IOTC
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): 25
From SS3
R0: The magnitude of unfished recruitment. Single value. Positive real number
Specified Value(s): 1515.18
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: 0.1, 0.1
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): 0.25, 0.2, 0.17, 0.15, 0.14, 0.13, 0.12, 0.12, 0.11, 0.11, 0.11, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.09, 0.09, 0.09, 0.09, 0.09
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): 0.05, 0.1
Given that M is very poorly quantified in most fishery settings, the annual variability in M is poorly known but likely to vary due to variability in predation pressure, food availability, disease and the density of the stock and related stocks that compete for prey or cannibalize recruits. To address the possibility of M changing among years in these simulations we set a modest, arbitrary level of inter-annual variability with a lognormal CV of between 5% and 10% (i.e. 0.05 - 0.1), corresponding with 95% probability interval of +/-10% to +/- 20%.
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): 0.79, 0.79
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): 0.2, 0.2
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): 0.67, 0.67
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): 0.1, 0.15
A resonably wide uncertainty at length in age was specified following the growth curve of the assessment: 
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
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 K parameter.
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: 152.62, 152.62
From the SS Assessment. Using cpars, we arbitrarily added some uncertainty to the inflection point of the maturity curve of around +/- 10 percent following maturity studies in the Atlantic Santiago et al. 2014
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: 0.4, 0.62
Uncertainty in the depletion from the CPUE series is imposed around the MLE estimate of the model.
Fdisc: Fraction of discarded fish that die. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0.05, 0.25
We assume a relative high level of post release survival following Gallagher et al. 2014.
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): 3.16
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): 66
The assessment is from 1950 - 2015
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 |
|---|---|---|
| 1950 | 0.000159 | 0.000159 |
| 1951 | 0.000905 | 0.000905 |
| 1952 | 0.005770 | 0.005770 |
| 1953 | 0.008840 | 0.008840 |
| 1954 | 0.016400 | 0.016400 |
| 1955 | 0.024600 | 0.024600 |
| 1956 | 0.013700 | 0.013700 |
| 1957 | 0.018400 | 0.018400 |
| 1958 | 0.021300 | 0.021300 |
| 1959 | 0.020400 | 0.020400 |
| 1960 | 0.022200 | 0.022200 |
| 1961 | 0.020000 | 0.020000 |
| 1962 | 0.013800 | 0.013800 |
| 1963 | 0.019000 | 0.019000 |
| 1964 | 0.032700 | 0.032700 |
| 1965 | 0.022000 | 0.022000 |
| 1966 | 0.021900 | 0.021900 |
| 1967 | 0.038500 | 0.038500 |
| 1968 | 0.029900 | 0.029900 |
| 1969 | 0.033200 | 0.033200 |
| 1970 | 0.019400 | 0.019400 |
| 1971 | 0.020100 | 0.020100 |
| 1972 | 0.017900 | 0.017900 |
| 1973 | 0.008830 | 0.008830 |
| 1974 | 0.014500 | 0.014500 |
| 1975 | 0.013400 | 0.013400 |
| 1976 | 0.008750 | 0.008750 |
| 1977 | 0.008530 | 0.008530 |
| 1978 | 0.010900 | 0.010900 |
| 1979 | 0.014700 | 0.014700 |
| 1980 | 0.013200 | 0.013200 |
| 1981 | 0.016000 | 0.016000 |
| 1982 | 0.011800 | 0.011800 |
| 1983 | 0.013700 | 0.013700 |
| 1984 | 0.014100 | 0.014100 |
| 1985 | 0.009910 | 0.009910 |
| 1986 | 0.012700 | 0.012700 |
| 1987 | 0.011800 | 0.011800 |
| 1988 | 0.013800 | 0.013800 |
| 1989 | 0.017900 | 0.017900 |
| 1990 | 0.012500 | 0.012500 |
| 1991 | 0.014700 | 0.014700 |
| 1992 | 0.013800 | 0.013800 |
| 1993 | 0.016900 | 0.016900 |
| 1994 | 0.022300 | 0.022300 |
| 1995 | 0.024100 | 0.024100 |
| 1996 | 0.025300 | 0.025300 |
| 1997 | 0.037700 | 0.037700 |
| 1998 | 0.024600 | 0.024600 |
| 1999 | 0.033100 | 0.033100 |
| 2000 | 0.044700 | 0.044700 |
| 2001 | 0.033500 | 0.033500 |
| 2002 | 0.042100 | 0.042100 |
| 2003 | 0.057200 | 0.057200 |
| 2004 | 0.076600 | 0.076600 |
| 2005 | 0.084000 | 0.084000 |
| 2006 | 0.077900 | 0.077900 |
| 2007 | 0.078100 | 0.078100 |
| 2008 | 0.088000 | 0.088000 |
| 2009 | 0.098000 | 0.098000 |
| 2010 | 0.099600 | 0.099600 |
| 2011 | 0.109000 | 0.109000 |
| 2012 | 0.123000 | 0.123000 |
| 2013 | 0.150000 | 0.150000 |
| 2014 | 0.154000 | 0.154000 |
| 2015 | 0.154000 | 0.154000 |
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 Stock@Linf . 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 Stock@Linf . 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): 0.2, 0.6
We specify a wide range for this parameter as we don’t know what fraction of reported landings are discarded at sea.
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 fishery data is from 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. These parameters could updated after discussion with the blue shark species experts.
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): 0.1, 0.3
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): 100, 200
Borrowed from Generic_Obs
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): 25, 50
Borrowed from Generic_Obs
CAL_nsamp: Number of catch-at-length observation per time step. Uniform distribution lower and upper bounds. Positive integers
Specified Value(s): 100, 200
Borrowed from Generic_Obs
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): 25, 50
Borrowed from Generic_Obs
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): 0.1, 0.4
Borrowed from Generic_Obs
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
Borrowed from Generic_Obs
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): 1, 2
There is a high degree of variability in adherence to the TAC among years (a CV of 50%)
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): 0.5, 0.5
Catches can be up to twice those recommended by the TAC
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, 2
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): 0.5, 0.5
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): 1, 2
As for TAC
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): 0.5, 0.5
As for TAC
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
References
Rice.J., (2017) Stock assessment of blue shark (Prionace glauca) in the Indian Ocean using Stock Synthesis. IOTC Secretariat.