Operating Model

The OM rdata file can be downloaded from here

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

Species Information

Species: Hippoglossus hippoglossus

Common Name: Atlantic Halibut

Management Agency: DFO

Region: Atlantic 3NOPS4VWX5Zc

Latitude: -59

Longitude: 42.5

OM Parameters

OM Name: Name of the operating model: Halibut_Atl_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: 2

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

DFO. 2015. 2014 Assessment of Atlantic Halibut on the Scotian Shelf and Southern Grand Banks (NAFO Divisions 3NOPs4VWX5Zc). DFO Can. Sci. Advis. Sec. Sci. Advis. Rep. 2015/012

Custom Parameters

In order to generate the low initial stock size and subsequent rebuilding, it was necessary to use cpars to prescribe historical recruit deviations predicted by the assessment. This was done manually by making the initial cohort in 1970 very small (via the perr custom parameter).

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

Maximum age reported in Armsworthy and Campana 2010 was 50, added 10 years to this as there is no way in DLMtool to specify a “greater than” category. Adding 10 to the max age may allow for better calculation of the tail of the distribution.

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

Specified Value(s): 100

Currently the operating model is scale-less and R0 is a nuisance parameter. An arbitrary value is used here.

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

Specified Value(s): 0.14, 0.22

Lower value of M is taken from DFO 2015, but no confidence interval is presented. Upper value of M taken from den Heyer et al. 2013

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

Begin trials with age invariant M (likely a simplification)

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): 0.08, 0.08

No SD for M in DFO 2015. SD for M of den Heyer et al. 2013 used.

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): 0.4, 0.95

DFO 2015 states: “The stock-recruit relationship for halibut could not be well described by the more commonly used models”. Therefore, a broad range was used. Cox et al. 2016 used a value of 0.95, but this is likely an estimate as well.

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

Specified Value(s): 1

The model predictions of Stock biomass and recruitment suggest that if anything the relationship is asymptotic, so Beverton Holt dynamics are assumed.

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

Specified in cpars: 0, 4.07

Standard deviation of log-recruitment reported by Cox et al. 2016.

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): 0.74, 0.74

Lag-1 autocorrelation is moderate, see recruitment deviation figure above.

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): 210.8, 287.9

Female values for Scotian Shelf and Grand Banks longline (which most closely matches the fishery) from Armsworthy and Campana 2010 used.

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

Specified Value(s): 0.03, 0.09

Female values for Scotian Shelf and Grand Banks longline (which most closely matches the fishery) from Armsworthy and Campana 2010 used.

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

Specified Value(s): -5.4, -0.76

Female values for the Grand Banks Scotian Shelf and longline (which most closely matches the fishery) from Armsworthy and Campana 2010 used.

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, 0.2

Estimated from values presented for females in Table 2 of Armsworthy and Campana 2010

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

Assume time-invariant

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

Assume time-invariant

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): 98, 108

Based on the female L50 value of 102.99 +/- 4.78 presented in Sigourney et al. 2006

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

Specified Value(s): 15, 30

DFO 2015 states female maturity for 3NOPs4VWX at 119 cm, Sigourney et al. 2006 says L99 is 192.94 cm, and L75 is 129.07. Picked value of 15-30 cm sa intermediate between the 119 in DFO 2015 and the L75 of 129.07 from Sigourney et al. 2006.

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 Value(s): 0.08, 0.25

Cox et al. 2016 state D was 0.08 in 2013, my (WB) calculations for 2017 are 0.12. Try range of 0.08 to 0.25 (approx. 3 fold)

Figure from Cox et al. 2016

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

Specified Value(s): 0.23, 0.23

Taken from study of Atlantic halibut trawl and longline discard mortality by Neilson et al. 1989

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): 0.01

Taken from Cox et al. 2016

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

Specified Value(s): 3.12

Taken from Cox et al. 2016

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

Set equal to area 2 - mixed stock assumption. Tagging data supports some long distance movement

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

Set equal to area 2 - mixed stock assumption. Tagging data supports some long distance movement

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): 0.6, 0.9

High to moderate mixing - mixed stock assumption. Tagging data supports some long distance movement

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): 44

The assessment on which the operating model is based, runs from 1970 - 2013, a total of 44 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

No reason to assume that fishers do not fish in relation to CPUE

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

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

The fishing mortality rate trend presented in Cox 2016 is used here.

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

No uncertainty in F trend is assumed (variability in magnitude in F originates from the varying depletion and other parameters)

EffYears EffLower EffUpper
1970 0.405 0.405
1971 0.474 0.474
1972 0.473 0.473
1973 0.470 0.470
1974 0.398 0.398
1975 0.298 0.298
1976 0.228 0.228
1977 0.199 0.199
1978 0.170 0.170
1979 0.155 0.155
1980 0.135 0.135
1981 0.113 0.113
1982 0.136 0.136
1983 0.134 0.134
1984 0.181 0.181
1985 0.260 0.260
1986 0.261 0.261
1987 0.242 0.242
1988 0.323 0.323
1989 0.335 0.335
1990 0.427 0.427
1991 0.497 0.497
1992 0.473 0.473
1993 0.490 0.490
1994 0.445 0.445
1995 0.302 0.302
1996 0.252 0.252
1997 0.247 0.247
1998 0.199 0.199
1999 0.184 0.184
2000 0.156 0.156
2001 0.207 0.207
2002 0.205 0.205
2003 0.212 0.212
2004 0.198 0.198
2005 0.180 0.180
2006 0.182 0.182
2007 0.191 0.191
2008 0.168 0.168
2009 0.190 0.190
2010 0.146 0.146
2011 0.117 0.117
2012 0.108 0.108
2013 0.104 0.104

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

Specified Value(s): 0, 0

Since individual annual effort is prescribed above (e.g. EffLower) there is no need to inpose addition interannual variability.

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): 0.05, 0.1

No reason to suspect major changes in fishing efficiency.

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): 0.05, 0.1

No reason to suspect high variability. Longline fishery.

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): 55, 60

Estimated from Fig. 2 of Cox et al. 2016, and the mean length at age from Armsworthy and Campana 2010

Fig. 2 of Cox et al. 2016

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

Specified Value(s): 65, 70

Estimated from Fig. 2 of Cox et al. 2016, and the mean length at age from Armsworthy and Campana 2010

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

Specified Value(s): 1, 1

Asymptotic - selectivity modelled as knife-edged

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

Specified Value(s): FALSE

Selectivity and retention are modelled in absolute units and are not relative to length at 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): 81, 81

Fishery has 81 cm minimum length

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

Specified Value(s): 81, 81

Fishery has 81 cm minimum length

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

Specified Value(s): 1, 1

Assume all fish >=81 cm retained

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

Specified Value(s): 0, 0

Due to value of fish, all fish above legal length are retained.

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): 2013

Last assessment year.

Existing Spatial Closures: MPA

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

Slot not used.

Obs Parameters

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

Relatively constrained catch over/under reporting - taken from Capelin example

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): 0.01

Expected to be low given size and value of fishery

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

Specified Value(s): 200, 225

Detailed otolith based CAA sampling has not been done lately. Took mean +/- 25 from Armsworthy and Campana 2010

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): 20, 25

Sampling in Armsworthy and Campana 2010 is unclear. Assume 10% of total samples taken in each independent sampling event.

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

Specified Value(s): 200, 225

No justification provided.

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): 20, 25

Same as CAA_nsamp: sampling in Armsworthy and Campana 2010 is unclear. Assume 10% of total samples taken in each independent sampling event.

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): 0.2, 0.5

Based on Cal Iobs, 95% CI appears large; Capelin example calls 0.2-0.5 high for Btobs

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): 0.1

Capelin example says this is unused

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

Based on Cal Btobs, 95% CI appears large; Capelin example calls 0.2-0.5 high for Btobs

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

Taken from Capelin example

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): 0.33, 1

Taken from Capelin example

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): 0.05

Taken from Capelin example

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): 0.1

Natural mortality may be wrong, but these are a long-lived fish.

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): 0.05

Taken from Capelin example

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

Taken from Capelin example

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

Taken from Capelin example

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): 0.1

Taken from Capelin example

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): 0.1

Taken from Capelin example

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): 0.3

Taken from Capelin example

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

Taken from Capelin example

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): 0.05

Taken from Capelin example

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

Taken from Capelin example

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

Taken from Capelin example

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

Specified Value(s): 0.4

Taken from Capelin example

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

Taken from Capelin example

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

Taken from Capelin example

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): 0.3

Taken from Capelin example

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, 0.2

Taken from Capelin example

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): 0.9, 1.1

Take about the TAC each year since 1998

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, 0.1

Fish within about 2-10% of TAC since 1998

Effort Control Implementation Error: TAEFrac, TAESD

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

Specified Value(s): 0.9, 1.1

Year round fishery with TAC, effort invariant.Not applicable: year round fishery with TAC, effort is not used by management (the same values as TAC management are assumed however).

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, 0.1

Not applicable: year round fishery with TAC, effort is not used by management (the same values as TAC management are assumed however).

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, 1

The minimum legal size is 81 cm, this is enforced

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): 0.01, 0.02

The minimul legal size is 81 cm, this is enforced

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