Generate a MICE Rel object, with predict and simulate methods, for multiMSE. Currently implements intra-stock dynamics via density-dependent processes.

makeRel(type = "DDM", stock = 1, CV = 0, ...)

# S3 method for Rel
print(x, ...)

# S3 method for Rel
predict(object, newdata, ...)

# S3 method for Rel
simulate(object, nsim = 1, seed = 1, ...)

Arguments

type

String to indicate the type of stock interaction. "DDM" for density-dependent natural mortality.

stock

The index position of the stock in the MOM.

CV

Coefficient of variation of the predicted value for simulate. Used to pass values to the operating model.

...

Additional arguments depending on type. See details below.

x

For print.Rel, a Rel class object from make_Rel.

object

A Rel class object from make_Rel.

newdata

A data frame to provide values of predictor variables with which to calculate the response variable.

nsim

The number of simulations.

seed

Integer to specify the seed for the random number generator.

Value

A class "Rel" object to pass to MOM@Rel.

Density-dependent M ("DDM")

Natural mortality (M) is a linear function of stock depletion in terms to total biomass (B) in year y (Forrest et al. 2018): $$M_y = M_0 + (M_1 + M_0) (1 - B_y/B_0)$$ with a constraint that \(M_y = M_0\) if \(B_y > B_0\)

Provide the following arguments:

  • M0: Natural mortality as B approaches B0. Vector [nsim]

  • M1: Natural mortality as B approaches zero. Vector [nsim]

  • Optional B0: Unfished biomass. Calculated from stock-recruit alpha and beta and unfished biomass per recruit at M = M0. Vector [nsim]

References

Forrest, R., Holt, K., and Kronlund, A. 2018. Performance of alternative harvest control rules for two Pacific groundfish stocks with uncertain natural mortality: Bias, robustness and trade-offs. Fisheries Research 206: 259–286. doi:10.1016/j.fishres.2018.04.007

Author

Q. Huynh

Examples

# Depensatory natural mortality
Rel <- makeRel(type = "DDM", M0 = 0.8, M1 = 0.2, CV = 0.1)

# Predict M when B/B0 = 0.1
pred <- predict(Rel, newdata = data.frame(B_1 = 0.1, B0_1 = 1))

# Simulate values of M with CV = 0.1
Rel$fitted.values <- pred
simulate(Rel, nsim = 10, seed = 1)
#>       sim_1     sim_2     sim_3    sim_4     sim_5     sim_6     sim_7
#> 1 0.2430378 0.2634926 0.2380192 0.303335 0.2673545 0.2383794 0.2715994
#>       sim_8     sim_9    sim_10
#> 1 0.2784825 0.2740037 0.2509475

# Add Rel to MOM
MOM <- makeMOM(testOM)
MOM@Rel <- list(Rel)