| GB_target {DLMtool} | R Documentation |
Geromont and Butterworth target CPUE and catch MP
Description
An MP similar to SBT2 that modifies a time-series of catch recommendations and aims for target catch rate and catch level based on BMSY/B0 and MSY, respectively.
Usage
GB_target(x, Data, reps = 100, plot = FALSE, w = 0.5)
Arguments
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? |
w |
A gain parameter |
Details
The TAC is calculated as:
If I_\textrm{recent} \geq I_0:
\textrm{TAC}= C_\textrm{ref} \left(w + (1-w)\frac{I_\textrm{rec}-I_0}{I_\textrm{target}-I_0} \right)
else:
\textrm{TAC}= wC_\textrm{ref} \frac{I_\textrm{rec}}{I_0}^2
where C_\textrm{ref} is a reference catch assumed to be a proxy for MSY (Data@Cref),
w is a gain parameter,
I_\textrm{rec} is the average index over the last 4 years,
I_\textrm{target} is the target Index (Data@Iref), and
I_0 is 0.2 x the average index over the past 5 years.
In the MSE C_\textrm{ref} is the calculated MSY subject to observation error
defined in Obs@CV_Cref, and I_\textrm{target} is assumed to be the index at MSY subject
to observation error (Obs@CV_Iref). Consequently, the performance of this method in the MSE
is strongly determined by the specified uncertainty for these parameters.
The TAC is subject to the following conditions:
if next TAC > 1.2 last catch, then TAC = 1.2 last catch
if next TAC < 0.8 last catch, then TAC = 0.8 last catch
Value
An object of class Rec-class with the TAC slot populated with a numeric vector of length reps
Required Data
See Data-class for information on the Data object
GB_target: Cref, Ind, Iref
Rendered Equations
See Online Documentation for correctly rendered equations
Author(s)
T. Carruthers
References
Geromont, H.F. and Butterworth, D.S. 2014. Complex assessment or simple management procedures for efficient fisheries management: a comparative study. ICES J. Mar. Sci. doi:10.1093/icesjms/fsu017
See Also
Other Index methods:
GB_slope(),
Gcontrol(),
ICI(),
Iratio(),
Islope1(),
Itarget1_MPA(),
Itarget1(),
ItargetE1()
Examples
GB_target(1, MSEtool::SimulatedData, plot=TRUE)