Operating model for Haddock (Melanogrammus aeglefinus)
A case-study based on the Faroe Islands stock to investigate increase in catchability
A. Hordyk ([email protected])
May 2018
Introduction
The operating model is based on the Faroe Island haddock fishery in 1996. After 1996 the fishery switched to effort controls. Eigaard et al (2011) report an average annual increase in catchability of 9% after 1996.
The base-case OM assumes that future catchability is variable but stable.
The first alternative OM assumes half of the reported annual increase in catchability - 4.5%.
The second alternative OM assumes the the reported annual increase in catchability - 9%.
Operating Model
The OM rdata file can be downloaded from here
Download and import into R using myOM <- readRDS('OM.rdata')
Species Information
Species: Melanogrammus aeglefinus
Common Name: Haddock
Management Agency:
Region: Faroe Islands
OM Parameters
OM Name: Name of the operating model: Haddock_Faroe_Islands
nsim: The number of simulations: 200
proyears: The number of projected years: 50
interval: The assessment interval - how often would you like to update the management system? 4
pstar: The percentile of the sample of the management recommendation for each method: 0.5
maxF: Maximum instantaneous fishing mortality rate that may be simulated for any given age class: 0.8
reps: Number of samples of the management recommendation for each method. Note that when this is set to 1, the mean value of the data inputs is used. 1
Source: A reference to a website or article from which parameters were taken to define the operating model
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
Maximum age was set a little higher than the maximum observed age of 20 years.
R0: The magnitude of unfished recruitment. Single value. Positive real number
Specified Value(s): 1000
Scaling parameter set at arbitrary value.
M: Natural mortality rate. Uniform distribution lower and upper bounds. Positive real number
Specified Value(s): 0.2, 0.2
Natural mortality was fixed at the rate used in the ICES 2013 assessement.
M2: (Optional) Natural mortality rate at age. Vector of length maxage . Positive real number
Slot not used.
Mexp: Exponent of the Lorenzen function assuming an inverse relationship between M and weight. Uniform distribution lower and upper bounds. Real numbers <= 0.
Specified Value(s): 0, 0
We assumed 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 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): 0.64, 0.82
Bounds for steepness were based on the 20th and 80th percentiles estimated by Myers et al (1999) for haddock.
SRrel: Type of stock-recruit relationship. Single value, switch (1) Beverton-Holt (2) Ricker. Integer
Specified Value(s): 1
We assumed a Beverton-Holt stock-recruitment relationship.
Perr: Process error, the CV of lognormal recruitment deviations. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0.6, 0.9
No estimates for recruitment variability exist, but recruitment is highly variable for this stock.
Bounds for this parameter were set to reflect highly variable recruitment.
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): 0.1, 0.6
A wide range of values was used for auto-correlation in recruitment to reflect the uncertainty in this parameter.
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): 59.85, 66.15
Magnussen (2007) reported Linf of 63 cm. We assumed an arbitrary 5% CV to account for some uncertainty in Linf.
K: von Bertalanffy growth parameter k. Uniform distribution lower and upper bounds. Positive real numbers
Specified Value(s): 0.43, 0.47
Magnussen (2007) reported K of 0.452 cm. We assumed an arbitrary 5% CV to account for some uncertainty in K
t0: von Bertalanffy theoretical age at length zero. Uniform distribution lower and upper bounds. Non-positive real numbers
Specified Value(s): 0, 0
Magnussen (2007) did not report estimates of t0. It was assumed that fish at age 0 are length 0.
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
Default values were used for the variability in length-at-age.
Ksd: Inter-annual variability in growth parameter k expressed as coefficient of variation. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0, 0.02
A small amount of inter-annual variability in K was assumed.
Linfsd: Inter-annual variability in maximum length expressed as a coefficient of variation. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0, 0.02
A small amount of inter-annual variability in Linf was assumed.
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): 43.7, 48.3
Magnussen (2007) reported at length-of-maturity of 46 cm for haddock. We assumed an arbitrary 5% CV to account for some uncertainty in L50.
L50_95: Length increment from 50 percent to 95 percent maturity. Uniform distribution lower and upper bounds. Positive real numbers
Specified Value(s): 4, 5
The difference between length at 50% and 95% maturity was assumed to be small (about 10% of L50).
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): 0.48, 0.66
ICES 2013 reported an estimate of BMSY of 35,000 tonnes. We assumed that BMSY/B0 is ~0.4 based on the steepness values used for this stock, and calculated an unfished spawning biomass of 87,500 tonnes. ICES 2013 report spawning biomass in 1996 of 50,682 tonnes. Based on these values, we calculate depletion in 1996 as 0.57 and assumed a CV of 15% to account for uncertainty.
Fdisc: Fraction of discarded fish that die. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0, 0
The base-case model assumed no discarding and no discard mortality.
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
Length-weight parameters were based on average values reported in FishBase.
b: Length-weight parameter beta. Single value. Positive real number
Specified Value(s): 3.13
Length-weight parameters were based on average values reported in FishBase.
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): 77
The OM is based on the haddock stock in 1996. The first data reported in ICES 2013 is from 1996, although the stock was already commercially fished before this time. We assumed that fishing commenced in 1920, and assumed a linear increase in fishing effort from 1920 to 1961.
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
See nyears above.
EffLower: Lower bound on relative effort corresponding to EffYears. Vector. Non-negative real numbers
Fishing effort from 1961 to 1996 was based on the estimated trend in F reported in ICES 2013. We assumed a 10% CV to account for variability in historical fishing effort. See nyears for more details.
EffUpper: Upper bound on relative effort corresponding to EffYears. Vector. Non-negative real numbers
See EffLower.
| EffYears | EffLower | EffUpper |
|---|---|---|
| 1920 | 0.0000 | 0.0000 |
| 1921 | 0.0119 | 0.0146 |
| 1922 | 0.0238 | 0.0291 |
| 1923 | 0.0358 | 0.0437 |
| 1924 | 0.0477 | 0.0583 |
| 1925 | 0.0596 | 0.0728 |
| 1926 | 0.0715 | 0.0874 |
| 1927 | 0.0834 | 0.1020 |
| 1928 | 0.0954 | 0.1170 |
| 1929 | 0.1070 | 0.1310 |
| 1930 | 0.1190 | 0.1460 |
| 1931 | 0.1310 | 0.1600 |
| 1932 | 0.1430 | 0.1750 |
| 1933 | 0.1550 | 0.1890 |
| 1934 | 0.1670 | 0.2040 |
| 1935 | 0.1790 | 0.2190 |
| 1936 | 0.1910 | 0.2330 |
| 1937 | 0.2030 | 0.2480 |
| 1938 | 0.2150 | 0.2620 |
| 1939 | 0.2260 | 0.2770 |
| 1940 | 0.2380 | 0.2910 |
| 1941 | 0.2500 | 0.3060 |
| 1942 | 0.2620 | 0.3200 |
| 1943 | 0.2740 | 0.3350 |
| 1944 | 0.2860 | 0.3500 |
| 1945 | 0.2980 | 0.3640 |
| 1946 | 0.3100 | 0.3790 |
| 1947 | 0.3220 | 0.3930 |
| 1948 | 0.3340 | 0.4080 |
| 1949 | 0.3460 | 0.4220 |
| 1950 | 0.3580 | 0.4370 |
| 1951 | 0.3690 | 0.4520 |
| 1952 | 0.3810 | 0.4660 |
| 1953 | 0.3930 | 0.4810 |
| 1954 | 0.4050 | 0.4950 |
| 1955 | 0.4170 | 0.5100 |
| 1956 | 0.4290 | 0.5240 |
| 1957 | 0.4410 | 0.5390 |
| 1958 | 0.5640 | 0.6900 |
| 1959 | 0.5130 | 0.6270 |
| 1960 | 0.6390 | 0.7810 |
| 1961 | 0.5060 | 0.6190 |
| 1962 | 0.5860 | 0.7160 |
| 1963 | 0.6300 | 0.7700 |
| 1964 | 0.4280 | 0.5230 |
| 1965 | 0.4730 | 0.5790 |
| 1966 | 0.4760 | 0.5820 |
| 1967 | 0.3630 | 0.4430 |
| 1968 | 0.3940 | 0.4810 |
| 1969 | 0.4370 | 0.5340 |
| 1970 | 0.4290 | 0.5240 |
| 1971 | 0.4110 | 0.5020 |
| 1972 | 0.3570 | 0.4360 |
| 1973 | 0.2610 | 0.3190 |
| 1974 | 0.1990 | 0.2430 |
| 1975 | 0.1620 | 0.1980 |
| 1976 | 0.2230 | 0.2720 |
| 1977 | 0.3490 | 0.4260 |
| 1978 | 0.2500 | 0.3060 |
| 1979 | 0.1400 | 0.1710 |
| 1980 | 0.1600 | 0.1960 |
| 1981 | 0.1630 | 0.2000 |
| 1982 | 0.2980 | 0.3640 |
| 1983 | 0.2390 | 0.2920 |
| 1984 | 0.2060 | 0.2510 |
| 1985 | 0.2480 | 0.3040 |
| 1986 | 0.2010 | 0.2460 |
| 1987 | 0.2380 | 0.2910 |
| 1988 | 0.1810 | 0.2210 |
| 1989 | 0.2570 | 0.3140 |
| 1990 | 0.2460 | 0.3000 |
| 1991 | 0.2480 | 0.3020 |
| 1992 | 0.1900 | 0.2320 |
| 1993 | 0.1690 | 0.2060 |
| 1994 | 0.1860 | 0.2270 |
| 1995 | 0.2040 | 0.2490 |
| 1996 | 0.2880 | 0.3510 |
Esd: Additional inter-annual variability in fishing mortality rate. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0.1, 0.3
Default values were used to allow for additional varability in the historical effort trend.
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
The base-case model assumed that catchability was stable and did not change in the future. The alternative OMs examine the increase in catchability that was observed in this fishery from 1996 onwards.
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
We assume some variability in fishing efficiency among years.
Future Catchability
Sampled Parameters
Histograms of 48 simulations of inter-annual variability in fishing efficiency (qcv) and average annual change in fishing efficiency (qinc), with vertical colored lines indicating 3 randomly drawn values used in the time-series plot:
Time-Series
Time-series plot showing 3 trends in future fishing efficiency (catchability): 
Fishery gear length selectivity: L5, LFS, Vmaxlen, isRel
L5: Shortest length corresponding to 5 percent vulnerability. Uniform distribution lower and upper bounds. Positive real numbers
Specified Value(s): 28, 33
Selectivity-at-length was based on the catch-at-age data reported in the 2008 ICES assessment and the von Bertalanffy
growth parameters reported above.
LFS: Shortest length that is fully vulnerable to fishing. Uniform distribution lower and upper bounds. Positive real numbers
Specified Value(s): 47, 54
See L5.
Vmaxlen: The vulnerability of fish at Stock@Linf . Uniform distribution lower and upper bounds. Fraction
Specified Value(s): 1, 1
Selectivity was assumed to be asymptotic.
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 all fish in the catch are retained.
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 all fish in the catch are retained.
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 all fish in the catch are retained.
DR: Discard rate - the fraction of caught fish that are discarded. Uniform distribution lower and upper bounds. Fraction
Specified Value(s): 0, 0
We assume that all fish in the catch 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): 1996
The OM was based on the fishery in 1996.
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.
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
We assumed that management was implemented perfectly.
Output Control Implementation Error: TACFrac, TACSD
TACFrac: Mean fraction of TAC taken. Uniform distribution lower and upper bounds. Positive real number.
Specified Value(s): 1, 1
Borrowed from: Perfect_Imp
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, 0
Borrowed from: Perfect_Imp
Effort Control Implementation Error: TAEFrac, TAESD
TAEFrac: Mean fraction of TAE taken. Uniform distribution lower and upper bounds. Positive real number.
Specified Value(s): 1, 1
Borrowed from: Perfect_Imp
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, 0
Borrowed from: Perfect_Imp
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
Borrowed from: Perfect_Imp
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, 0
Borrowed from: Perfect_Imp
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
Eigaard, Ole Ritzau, Bjarti Thomsen, Holger Hovgaard, Anders Nielsen, and Adriaan D. Rijnsdorp. 2011. ‘Fishing Power Increases from Technological Development in the Faroe Islands Longline Fishery.’ Canadian Journal of Fisheries and Aquatic Sciences. Journal Canadien Des Sciences Halieutiques et Aquatiques 68 (11). NRC Research Press: 1970-82.
Magnussen, E. 2007. ‘Interpopulation Comparison of Growth Patterns of 14 Fish Species on Faroe Bank: Are All Fishes on the Bank Fast-Growing?’ Journal of Fish Biology 71 (2): 453-75.
Myers, Ransom A., Keith G. Bowen, and Nicholas J. Barrowman. 1999. ‘Maximum Reproductive Rate of Fish at Low Population Sizes.’ Canadian Journal of Fisheries and Aquatic Sciences. Journal Canadien Des Sciences Halieutiques et Aquatiques 56 (12). NRC Research Press: 2404-19.