LstepCC1 {DLMtool} R Documentation

## Step-wise Constant Catch

### Description

A management procedure that incrementally adjusts the TAC according to the mean length of recent catches.

### Usage

```LstepCC1(
x,
Data,
reps = 100,
plot = FALSE,
yrsmth = 5,
xx = 0,
stepsz = 0.05,
llim = c(0.96, 0.98, 1.05)
)

LstepCC2(
x,
Data,
reps = 100,
plot = FALSE,
yrsmth = 5,
xx = 0.1,
stepsz = 0.05,
llim = c(0.96, 0.98, 1.05)
)

LstepCC3(
x,
Data,
reps = 100,
plot = FALSE,
yrsmth = 5,
xx = 0.2,
stepsz = 0.05,
llim = c(0.96, 0.98, 1.05)
)

LstepCC4(
x,
Data,
reps = 100,
plot = FALSE,
yrsmth = 5,
xx = 0.3,
stepsz = 0.05,
llim = c(0.96, 0.98, 1.05)
)
```

### 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? `yrsmth` Years over which to calculate mean length. `xx` Parameter controlling the fraction of mean catch to start using in first year `stepsz` Parameter controlling the size of update increment in TAC or effort. `llim` A vector of length reference points that determine the conditions for increasing, maintaining or reducing the TAC or effort.

### Details

The TAC is calculated as:

where \textrm{TAC}^* is (1-`xx`) times average catch in the first year, and previous catch in all projection years, S is step-size determined by `stepsz`, and r is the ratio of L_\textrm{recent} and L_\textrm{ave} which are mean length over the most recent `yrsmth` years and 2 x `yrsmth` historical years respectively.

The conditions are specified in the `llim` argument to the function.

### Value

An object of class `Rec-class` with the `TAC` slot populated with a numeric vector of length `reps`

### Functions

• `LstepCC1`: The least biologically precautionary TAC-based MP.

• `LstepCC2`: More biologically precautionary than `LstepCC1` (`xx` = 0.1)

• `LstepCC3`: More biologically precautionary than `LstepCC2` (`xx` = 0.2)

• `LstepCC4`: The most precautionary TAC-based MP.

### Required Data

See `Data-class` for information on the `Data` object

`LstepCC1`: Cat, LHYear, ML, Year

`LstepCC2`: Cat, LHYear, ML, Year

`LstepCC3`: Cat, LHYear, ML, Year

`LstepCC4`: Cat, LHYear, ML, Year

### Rendered Equations

See Online Documentation for correctly rendered equations

T. Carruthers

### References

Carruthers et al. 2015. Performance evaluation of simple management procedures. ICES J. Mar Sci. 73, 464-482.

Geromont, H.F., Butterworth, D.S. 2014. Generic management procedures for data-poor fisheries; forecasting with few data. ICES J. Mar. Sci. doi:10.1093/icesjms/fst232

### Examples

```LstepCC1(1, Data=MSEtool::SimulatedData, plot=TRUE)

LstepCC2(1, Data=MSEtool::SimulatedData, plot=TRUE)
LstepCC3(1, Data=MSEtool::SimulatedData, plot=TRUE)
LstepCC4(1, Data=MSEtool::SimulatedData, plot=TRUE)
```

[Package DLMtool version 6.0.2 Index]