create_data_input {SharkDemography}

Create empty template for demography life history data

Description

The demography functions in this package require a large amount of detailed life history information. This is provided to these functions as a multi-level list of various life history parameters with the class 'Demography.inputs'. This function creates the template for this input data which can be filled in after it is created.

Usage

create_data_input(maturity.type, t0 = FALSE)

Arguments

maturity.type

The type of maturity estimates from the source life history study. must be one of 'logistic - int/slope', 'logistic - a50/a95', 'normal' or 'uniform'.

t0

Logical argument regarding whether the growth models included "t0" or "L0" as as a parameter. Default is 'FALSE'

Value

A multi-level list of the class 'Demography.inputs'

Examples

######-----------
# Example code for Silky sharks
######-----------

silky_data <- create_data_input("logistic - int/slope", t0 = FALSE)
# Add growth data
silky_data$`growth`$model.type <- "logistic"
silky_data$growth$pars$Linf <- 268
silky_data$growth$pars$k <- 0.14
silky_data$growth$pars$L0 <- 82.7
silky_data$growth$se$Linf.se <- 5.8
silky_data$growth$se$k.se <- 0.006
silky_data$growth$se$L0.se <- 1.6
silky_data$growth$corr.matrix <- matrix(ncol = 3, nrow = 3,
                                        dimnames = list(c("Linf", "k", "L0"),c("Linf", "k", "L0")),
                                        data = c(1.0000000, -0.907188, 0.6233407,
                                                -0.9071881,  1.0000000, -0.8572509,
                                                0.6233407,-0.857250, 1.0000000))
# Add maturity data
silky_data$maturity$pars$intercept <- -15.90
silky_data$maturity$pars$slope <- 1.14
silky_data$maturity$se$intercept.se <- 2.78258
silky_data$maturity$se$slope.se <- 0.1971363
silky_data$maturity$corr.matrix <- matrix(ncol = 2, nrow = 2,
                                          dimnames = list(c("Intercept", "slope")
                                          ,c("Intercept", "slope")),
                                          data = c(1.0000000, -0.9922574,
                                                   -0.9922574, 1.0000000))
# max age lower bound
silky_data$max.age$min <- 28

# Add fecundity info
silky_data$litter.size$mean <- 10
silky_data$litter.size$se <- 3
silky_data$gest.period <- 1
silky_data$repro.cycle <- 2

# Add TL conversions (if available and required)
silky_data$Lt.type <- "TL"
silky_data$Lt.to.Wt$model.type <- "PCL"
silky_data$Lt.to.Wt$pars$a <- 2.73e-5
silky_data$Lt.to.Wt$pars$b <- 2.86

silky_data$convert.TL$model.type <- "PCL"
silky_data$convert.TL$pars$a <- 2.08
silky_data$convert.TL$pars$b <- 1.32