`SPmod.Rd`

An MP that makes incremental adjustments to TAC recommendations based on the apparent trend in surplus production. Based on the theory of Mark Maunder (IATTC)

SPmod(x, Data, reps = 100, plot = FALSE, alp = c(0.8, 1.2), bet = c(0.8, 1.2))

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? |

alp | Condition for modifying the TAC (bounds on change in abundance) |

bet | Limits for how much the TAC can change among years |

An object of class `Rec-class`

with the `TAC`

slot populated with a numeric vector of length `reps`

A numeric vector of TAC recommendations

Note that this isn't exactly what Mark has previously suggested and is stochastic in this implementation.

The TAC is calculated as:
$$\textrm{TAC}_y =
\left\{\begin{array}{ll}
C_{y-1} \textrm{bet}_1 & \textrm{if } r < \alpha_1 \\
C_{y-1} & \textrm{if } \alpha_1 < r < \alpha_2 \\
\textrm{bet}_2 (b_2 - b_1 + C_{y-2} ) & \textrm{if } r > \alpha_2 \\
\end{array}\right.
$$
where \(\textrm{bet}_1\) and \(\textrm{bet}_2\) are elements in `bet`

,
\(r\) is the ratio of the index in the most recent two years, \(C_{y-1}\)
is catch in the previous year, \(b_1\) and \(b_2\) are ratio of index
in \(y-2\) and \(y-1\) over the estimate of catchability \(\left(\frac{I}{A}\right)\),
and \(\alpha_1\), \(\alpha_2\), and \(\alpha_3\) are specified in argument
`alp`

.

See `Data-class`

for information on the `Data`

object

`SPmod`

: Cat, Ind

See Online Documentation for correctly rendered equations

http://www.iattc.org/Meetings/Meetings2014/MAYSAC/PDFs/SAC-05-10b-Management-Strategy-Evaluation.pdf

T. Carruthers

#> TAC (median) #> 16.30994