Description Usage Arguments Details Value Functions Required Data Rendered Equations Author(s) References Examples
An MP that makes incremental adjustments to TAC recommendations based on the apparent trend in CPUE, a an MP that makes incremental adjustments to TAC recommendations based on index levels relative to target levels (BMSY/B0) and catch levels relative to target levels (MSY).
1 2 3 4 5 
x 
A position in the data object 
Data 
A data object 
reps 
The number of stochastic samples of the MP recommendation(s) 
plot 
Logical. Show the plot? 
yrsmth 
The number of years for evaluating trend in relative abundance indices 
k1 
Control parameter 
k2 
Control parameter 
gamma 
Control parameter 
epsR 
Control parameter 
tauR 
Control parameter 
For SBT1
the TAC is calculated as:
where λ is the slope of index over the last yrmsth
years, and
K_1, K_2, and γ are arguments to the MP.
For SBT2
the TAC is calculated as:
\textrm{TAC}_y = 0.5 (C_{y1} + C_\textrm{targ}δ)
where C_{y1} is catch in the previous year, C_{\textrm{targ}}
is a target catch ([email protected]
), and :
where \textrm{epsR} is a control parameter and:
R = \frac{\bar{r}}{φ}
where \bar{r} is mean recruitment over last tauR
years and φ
is mean recruitment over last 10 years.
This isn't exactly the same as the proposed methods and is stochastic in this implementation. The method doesn't tend to work too well under many circumstances possibly due to the lack of 'tuning' that occurs in the real SBT assessment environment. You could try asking Rich Hillary at CSIRO about this approach.
An object of class Rec
with the TAC
slot populated with a numeric vector of length reps
SBT1
: Simple SBT MP
SBT2
: Complex SBT MP
See Data for information on the Data
object
SBT1
: Cat, Ind, Year
SBT2
: Cat, Cref, Rec
See Online Documentation for correctly rendered equations
T. Carruthers
http://www.ccsbt.org/site/recent_assessment.php
1 2  SBT1(1, Data=DLMtool::SimulatedData, plot=TRUE)
SBT2(1, Data=DLMtool::SimulatedData, plot=TRUE)

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