LBSPR {DLMtool} | R Documentation |
Length-Based SPR MPs
Description
The spawning potential ratio (SPR) is estimated using the LBSPR method and compared to a target of 0.4.
Usage
LBSPR(
x,
Data,
reps = 1,
plot = FALSE,
SPRtarg = 0.4,
theta1 = 0.3,
theta2 = 0.05,
maxchange = 0.3,
n = 5,
smoother = TRUE,
R = 0.2
)
LBSPR_MLL(
x,
Data,
reps = 1,
plot = FALSE,
SPRtarg = 0.4,
n = 5,
smoother = TRUE,
R = 0.2
)
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? |
SPRtarg |
The target SPR |
theta1 |
Control parameter for the harvest control rule |
theta2 |
Control parameter for the harvest control rule |
maxchange |
Maximum change in effort |
n |
Last number of years to run the model on. |
smoother |
Logical. Should the SPR estimates be smoothed? |
R |
variance of sampling noise for smoother |
Details
Effort is modified according to the harvest control rules described in Hordyk et al. (2015b):
Value
An object of class Rec-class
with the TAE slot populated
Functions
-
LBSPR_MLL
: Fishing retention-at-length is set equivalent to slightly higher than the maturity curve if SPR < 0.4
Required Data
See Data-class
for information on the Data
object
LBSPR
: CAL, CAL_bins, L50, L95, LHYear, MPeff, Mort, Year, vbK, vbLinf, wlb
Rendered Equations
See Online Documentation for correctly rendered equations
References
Hordyk, A., Ono, K., Valencia, S., Loneragan, N., and Prince J (2015a). A novel length-based empirical estimation method of spawning potential ratio (SPR), and tests of its performance, for small-scale, data-poor fisheries, ICES Journal of Marine Science, 72 (1), 217-231
Hordyk, A. R., Loneragan, N. R., & Prince, J. D. (2015b). An evaluation of an iterative harvest strategy for data-poor fisheries using the length-based spawning potential ratio assessment methodology. Fisheries Research, 171, 20-32. https://doi.org/10.1016/j.fishres.2014.12.018
Examples
LBSPR(1, Data=MSEtool::SimulatedData, plot=TRUE)
LBSPR_MLL(1, Data=MSEtool::SimulatedData, plot=FALSE)