BK {DLMtool} | R Documentation |
Family of management procedures that sets the TAC by approximation of Fmax based on the length at first capture relative to asymptotic length and the von Bertalanffy growth parameter K.
BK(x, Data, reps = 100, plot = FALSE) BK_CC(x, Data, reps = 100, plot = FALSE, Fmin = 0.005) BK_ML(x, Data, reps = 100, plot = FALSE)
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? |
Fmin |
The minimum fishing mortality rate that is derived from the catch-curve (interval censor). |
The TAC is calculated as:
\textrm{TAC} = A F_{\textrm{max}}
where A is (vulnerable) stock abundance, and F_{\textrm{max}} is calculated as:
F_{\textrm{max}} = \frac{0.6K}{0.67-L_c/L_∞}
where K is the von Bertalanffy growth coefficient, L_c is the length at first capture, and L_∞ is the von Bertalanffy asymptotic length
Abundance (A) is either assumed known (BK
) or estimated (BK_CC
and BK_ML
):
A = \frac{\bar{C}}{≤ft(1-e^{-F}\right)}
where \bar{C} is the mean catch, and F is estimated. See Functions section below for the estimation of F.
An object of class Rec-class
with the TAC
slot populated with a numeric vector of length reps
BK
: Assumes that abundance is known, i.e. Data@Abun
and Data@CV_abun
contain values
BK_CC
: Abundance is estimated using an age-based catch curve
to estimate Z and F, and abundance estimated from recent catches and F.
BK_ML
: Abundance is estimated using mean length
to estimate Z and F, and abundance estimated from recent catches and F.
See Data-class
for information on the Data
object
BK
: Abun, LFC, vbK, vbLinf
BK_CC
: CAA, Cat, LFC, vbK, vbLinf
BK_ML
: CAL, Cat, LFC, Lbar, Lc, Mort, vbK, vbLinf
See Online Documentation for correctly rendered equations
Note that the Beddington-Kirkwood method is designed to estimate F_\textrm{max}, that is, the fishing mortality that produces the maximum yield assuming constant recruitment independent of spawning biomass.
Beddington and Kirkwood (2005) recommend estimating F using other methods (e.g., a catch curve) and comparing the estimated F to the estimated F_\textrm{max} and adjusting exploitation accordingly. These MPs have not been implemented that way.
T. Carruthers.
Beddington, J.R., Kirkwood, G.P., 2005. The estimation of potential yield and stock status using life history parameters. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 163-170.
## Not run: BK(1, MSEtool::SimulatedData, reps=1000, plot=TRUE) ## End(Not run) ## Not run: BK_CC(1, MSEtool::SimulatedData, reps=1000, plot=TRUE) ## End(Not run) ## Not run: BK_ML(1, MSEtool::SimulatedData, reps=100, plot=TRUE) ## End(Not run)