A management procedure that incrementally adjusts the TAE to reach a target mean length in catches.

LtargetE1(x, Data, reps = 100, plot = FALSE, yrsmth = 5, xL = 1.05)

LtargetE4(x, Data, reps = 100, plot = FALSE, yrsmth = 5, xL = 1.15)



A position in the data object


A data object


The number of stochastic samples of the MP recommendation(s)


Logical. Show the plot?


Years over which to calculate mean length


Parameter controlling the magnitude of the target mean length of catches relative to average length in catches.


An object of class Rec-class with the TAE slot(s) populated


Four target length MPs proposed by Geromont and Butterworth 2014. Tested by Carruthers et al. 2015.

The TAE is calculated as:

If \(L_\textrm{recent} \geq L_0\): $$\textrm{TAE} = 0.5 \textrm{TAE}^* \left[1+\left(\frac{L_\textrm{recent}-L_0}{L_\textrm{target}-L_0}\right)\right] $$

else: $$\textrm{TAE} = 0.5 \textrm{TAE}^* \left[\frac{L_\textrm{recent}}{L_0}^2\right] $$

where \(\textrm{TAE}^*\) is the effort in the previous year, \(L_\textrm{recent}\) is mean length in last yrmsth years, \(L_0\) is (except for L95target) 0.9 average catch in the last 2 x yrsmth historical (pre-projection years) (\(L_\textrm{ave}\)), and \(L_\textrm{target}\) is (except for L95target) xL \(L_\textrm{ave}\).


  • LtargetE1: The least biologically precautionary TAE-based MP.

  • LtargetE4: The xL argument is increased to 1.15.

Required Data

See Data-class for information on the Data object

LtargetE1: LHYear, ML, MPeff, Year

Rendered Equations

See Online Documentation for correctly rendered equations


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

See also

Other Length target MPs: Lratio_BHI(), Ltarget1()


T. Carruthers


LtargetE1(1, Data=MSEtool::SimulatedData, plot=TRUE)

#> Effort 
#>   0.85 
LtargetE4(1, Data=MSEtool::SimulatedData, plot=TRUE)

#> Effort 
#>   0.85