California Barred Sand Bass
CDFW Project 2016
Adrian Hordyk, University of British Columbia.
June 2017
Introduction
This OM was built as part of a project with the California Department of Fish and Wildlife to evaluate DLMtool for use in California state-managed marine fisheries.
The goal of the project was to demonstrate an efficient and transparent framework for improving the science for data-limited fisheries and for prioritizing future data collection.
Through close collaboration with CDFW biologists, the project has conducted case studies with four stocks: barred sand bass, Southern California halibut, red sea urchin, and warty sea cucumber. A current project with CDFW, which commenced in February 2018, will update the OM for these cases studies, as well as build OM for several more data-limited fisheries in California.
This document describes the OM for the barred sand bass. Several features have been added to DLMtool (e.g., historical MPAs, retention curves and discard mortality) since this OM was built. Default assumptions for these parameters have been made in this OM, and the OM will be updated as part of the 2018 project.
Operating Model
The OM rdata file can be downloaded from here
Download and import into R using myOM <- readRDS('OM.rdata')
Species Information
Species: Paralabrax nebulifer
Common Name: Barred Sand Bass
Management Agency: CDFW
Region: California, USA
Sponsor: Resource Legacy Fund
Latitude: 34.42083
Longitude: -119.69819
OM Parameters
OM Name: Name of the operating model: BSB_CA
nsim: The number of simulations: 500
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
See full report for details
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): 35
See M.
R0: The magnitude of unfished recruitment. Single value. Positive real number
Specified Value(s): 1e+05
Fixed at an arbitrary 100,000.
M: Natural mortality rate. Uniform distribution lower and upper bounds. Positive real number
Specified Value(s): 0.15, 0.21
Jarvis et al. (2014) estimated natural mortality to be 0.218 using Pauly (1980) method. This corresponds to an approximate maximum age of about 21 years which appears to be too low given that individuals above this age have been observed.
The upper bound for M was set to 0.21, and the lower bound at 0.15, which corresponds to maximum age of approximately 30 years. The oldest observed age in recent years is 24, but the species has a long history of exploitation and this is unlikely to represent the longevity of the species. The maximum age parameter was set to 35, which corresponds to an upper limit for longevity that corresponds to the lower bound on M.
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
Natural mortality was assumed to be constant at age.
Msd: Inter-annual variability in natural mortality rate expressed as a coefficient of variation. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0, 0.05
No justification provided.
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, 0.9
No information exists on steepness of stock-recruitment relationship for this species, and a range of 0.6 - 0.9 was used, based on the meta-analysis of Myers et al.(2002).
SRrel: Type of stock-recruit relationship. Single value, switch (1) Beverton-Holt (2) Ricker. Integer
Specified Value(s): 1
A Beverton-Holt stock-recruitment was used.
Perr: Process error, the CV of lognormal recruitment deviations. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0.2, 0.5
Inter-annual variability in recruitment appears to be low, but recruitment error appears auto-correlated and driven by environmental conditions (Jarvis et al. 2014).
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): 0.5, 0.9
See Perr
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): 560, 763
Love et al. (1996) provide the only available estimates of the von Bertalanffy growth parameters for the barred sand bass. The ranges for the growth parameters were set at the mean values reported by Love et al. (1996) ± 2 standard deviations.
K: von Bertalanffy growth parameter k. Uniform distribution lower and upper bounds. Positive real numbers
Specified Value(s): 0.05, 0.11
See Linf.
t0: von Bertalanffy theoretical age at length zero. Uniform distribution lower and upper bounds. Non-positive real numbers
Specified Value(s): -3.89, -1.37
See Linf.
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
No information was available, default values were used.
Ksd: Inter-annual variability in growth parameter k expressed as coefficient of variation. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0, 0.02
No justification provided.
Linfsd: Inter-annual variability in maximum length expressed as a coefficient of variation. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0, 0.02
No justification provided.
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): 219, 239
Love et al. (1996) estimated maturity-at-length for both male and females. The range used in this study represents the range of estimates for females and males.
L50_95: Length increment from 50 percent to 95 percent maturity. Uniform distribution lower and upper bounds. Positive real numbers
Specified Value(s): 10, 15
There appears to be a rapid transition from immature to mature state (Love et al. 1996).
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): 0.2, 0.6
No reliable information exists for current depletion for the barred sand bass in southern California. Recent declines in catch rates suggests that stock may be at lower levels. A recent publication suggests that hyper-stability in the CPUE index may have masked a decline in abundance in recent years (Erisman et al. 2011), however scientists at CDFW dispute this interpretation based on the use of inappropriate catch data (Jarvis et al. 2014).
The fishery has been managed as a recreational only fishery since the early 1900s, with a minimum legal length at or above the size of maturity since 1956. Furthermore, the size limit was increased in 2013 as a management response to the perceived decline in the stock. The bounds for the depletion parameter were set to 0.20 - 0.60 which capture the uncertainty of a stock at low levels (around half of BMSY), to one that has been exploited but is still above BMSY.
Fdisc: Fraction of discarded fish that die. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0, 0
Discard mortality was not included in DLMtool at the time this OM was built. Recent versions of DLMtool now include 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
The length-weight parameters were sourced from Miller et al. (2008).
b: Length-weight parameter beta. Single value. Positive real number
Specified Value(s): 2.98
See above.
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): 0.15, 0.25
The Size_area_1 slot was not included in DLMtool at the time of this analyis. For this OM this Size_area_1
has been set equal to Frac_area_1 which assumes equal density in Area 1 and Area 2.
Frac_area_1: The fraction of the unfished biomass in stock 1. Uniform distribution lower and upper bounds. Positive real numbers
Specified Value(s): 0.15, 0.25
The fraction of the stock in area 1 (Frac_area_1) was based on the assumption that MPAs cover about 15 - 25% of the area in California waters.
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): 0.1, 0.1
The species forms large spawning aggregations that are targeted by recreational fishers, both on private boats and commercial passenger fishing vessels. MPAs are not believed to offer protection to this species because of high movement, and the probability of staying in Area 1 between years set at 0.095 – 0.105.
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): 117
The fishery for barred sand bass in southern California began in the early 1900s, and a historical period of 117 years (1900 – 2016) was used in the model.
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
Targeting was assumed proportional to biomass.
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
Hill and Schneider (1999) present the trends in fishing effort from the commercial passenger fishing vessel (CPFV) fleet from 1936 - 1996. Barred sand bass did not contribute a significant amount to the CPFV catch until the mid-1900s. It was assumed that fishing mortality was low from 1900 to 1950, then generally increased from 1950 - 1980. Fishing effort in recent decades appears to have been slightly decreasing since the early 1960s, and declined more steeply in recent years (Erisman et al. 2011).
EffLower: Lower bound on relative effort corresponding to EffYears. Vector. Non-negative real numbers
See EffYears
EffUpper: Upper bound on relative effort corresponding to EffYears. Vector. Non-negative real numbers
See EffYears
| EffYears | EffLower | EffUpper |
|---|---|---|
| 1900 | 0.00 | 0.00 |
| 1911 | 0.10 | 0.20 |
| 1921 | 0.10 | 0.20 |
| 1932 | 0.00 | 0.00 |
| 1942 | 0.00 | 0.00 |
| 1953 | 0.35 | 0.65 |
| 1963 | 0.65 | 0.95 |
| 1974 | 0.65 | 0.95 |
| 1984 | 0.65 | 0.75 |
| 1995 | 0.85 | 1.00 |
| 2005 | 0.50 | 1.00 |
| 2016 | 0.30 | 0.40 |
Esd: Additional inter-annual variability in fishing mortality rate. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0.1, 0.4
Default values from DLMtool were used for the inter-annual variability in fishing mortality.
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, 2
Default values from DLMtool were used for the annual increase in catchability.
qcv: Inter-annual variability in fishing efficiency (applicable only to forward projection and input controls). Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0.1, 0.3
Default values from DLMtool were used for the annual variability in catchability.
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
Not used. See SelYears below.
LFS: Shortest length that is fully vulnerable to fishing. Uniform distribution lower and upper bounds. Positive real numbers
Specified Value(s): 0, 0
Not used. See SelYears below.
Vmaxlen: The vulnerability of fish at Stock@Linf . Uniform distribution lower and upper bounds. Fraction
Specified Value(s): 0, 0
Not used. See SelYears below.
isRel: Selectivity parameters in units of size-of-maturity (or absolute eg cm). Single value. Boolean.
Specified Value(s): FALSE
Selectivity was specified in absolute units.
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
This slot was not included in DLMtool at the time the OM was built. The retention curve is assumed to be equivalent to the selectivity curve, which may not be a correct assumption for this fishery. See SelYears for more details.
LFR: Shortest length that is fully retained. Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 0, 0
See LR5
Rmaxlen: The retention of fish at Stock@Linf . Uniform distribution lower and upper bounds. Non-negative real numbers
Specified Value(s): 1, 1
See LR5
DR: Discard rate - the fraction of caught fish that are discarded. Uniform distribution lower and upper bounds. Fraction
Slot not used.
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
Specified Value(s): 1, 57, 58, 59, 61, 114
A minimum legal length of 266 mm was regulated in 1956. The size limit was increased by 13 mm each year until 1959, when it was set to 304 mm. In 2013 the size limit was increased to 355 mm. The size at selection has been above the size at maturity for most of the history of the fishery. Barred sand bass are targeted by hook and line fishery on spawning aggregations, and selectivity-at-length is assumed to be asymptotic.
The DLMtool now includes a retention curve. The minimum legal length should correspond to the size of retention, and size of selection may in fact be lower if undersize fish are caught and released.
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
Specified Value(s): 243, 259, 272, 288, 298, 348
See SelYears
L5Upper: (Optional) Upper bound of L5 (use ChooseSelect function to set these). Vector. Non-negative real numbers
Specified Value(s): 247, 263, 276, 291, 301, 352
See SelYears
LFSLower: (Optional) Lower bound of LFS (use ChooseSelect function to set these). Vector. Non-negative real numbers
Specified Value(s): 248, 264, 277, 292, 302, 353
See SelYears
LFSUpper: (Optional) Upper bound of LFS (use ChooseSelect function to set these). Vector. Non-negative real numbers
Specified Value(s): 252, 268, 281, 296, 306, 357
See SelYears
VmaxLower: (Optional) Lower bound of Vmaxlen (use ChooseSelect function to set these). Vector. Fraction
Specified Value(s): 0.85, 0.85, 0.85, 0.85, 0.85, 0.85
See SelYears
VmaxUpper: (Optional) Upper bound of Vmaxlen (use ChooseSelect function to set these). Vector. Fraction
Specified Value(s): 1, 1, 1, 1, 1, 1
See SelYears
Current Year: CurrentYr
CurrentYr: The current calendar year (final year) of the historical simulations (eg 2011). Single value. Positive integer.
Specified Value(s): 2016
The OM was populated on data from 2016
Existing Spatial Closures: MPA
MPA: (Optional) Matrix specifying spatial closures for historical years.
Slot not used.
Obs Parameters
Except where information was found to suggest alternative values, the parameters used for the observation model were based on the values presented in Carruthers et al. (2014) and are found in the ‘Generic_Obs’ observation object in the 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): 400, 600
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): 150, 300
Catch-at-length effect sample size (CAL_ESS) was increased to reflect availability of length data for this species.
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): 0.33, 3
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): 0.2, 0.5
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): 0.45, 1
The fishery for the barred sand bass predominantly targets spawning aggregations, and it is possible that the CPUE index is affected by hyper-stability.
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): 0.05
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): 0.05
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): 0.05
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
See Kbiascv
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
Implementation error was not included in DLMtool at the time this OM was built. Here it is assumed that management is implemented perfectly. These values may be updated when the OM is revised in 2018.
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
Erisman, B.E., Allen, L.G., Claisse, J.T., Pondella II, D.J., Miller, E.F., and Murray, J.H. 2011. The illusion of plenty: hyperstability masks collapses in two recreational fisheries that target fish spawning aggregations. Can. J. Fish. Aquat. Sci. 68: 1705–1716. doi: 10.1139/F2011-090.
Hill, K.T., and Schneider, N. 1999. Historical Logbook Databases from California’s Commercial Passenger Fishing Vessel (Party boat) Fishery, 1936-1997. Scripps Inst. Oceanogr.: 1936–1997.
Jarvis, E.T., Gliniak, H.L., and Valle, C.F. 2014. Effects of fishing and the environment on the long-term sustainability of the recreational saltwater bass fishery in southern California. Calif. Fish Game 100: 234–259.
Love, M.S., Brooks, A., Busatto, D., Stephens, J., and Gregory, P.A. 1996. Aspects of life histories of kelp bass, Paralabrax clathratus, and the barred sand bass, P. nebulifer, from the southern California Bight. Fish. Bull. 94: 472–481.
Miller, E.F., Beck, D.S., and Dossett, W. 2008. Length-Weight Relationships of Select Common Nearshore Southern California Marine Fishes. Bull. South. Calif. Acad. Sci. 107: 183–186. doi:10.3160/0038-3872-107.3.183.
Myers, R.A., Barrowman, N.J., Hilborn, R., and Kehler, D.G. 2002. Inferring Bayesian Priors with Limited Direct Data: Applications to Risk Analysis. North Am. J. Fish. Manag. 22: 351–364.
Pauly, D. 1980. On the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks. J Cons Int. Explor Mer. 39: 175–192.