Simulate_AAFC {SharkDemography}

Monte Carlo simulations of Leslie matrix models under varying levels of F and AAFC

Description

This is a wrapper function for 'Calculate_demography' which runs this function the specified number of times using a range of F and Age-at-first-capture (AAFC) values

Usage

Simulate_AAFC(n = 1000, .data, M.estimators = NULL, max.AAFC = 15, n_cores = 1)

Arguments

n

The number of simulations to be run. 1000 is recomended but smaller numbers should be run when testing to avoid long run times.

.data

A multi-level list of the class 'Demography.inputs' produced from the 'create_data_input' function and then manually completed.

M.estimators

Any specific natural mortality estimators to be included in the analysis. Only one will be used in each run which is randomly selected. Must be a single estimator or a vector of estimators. These can include: "Pet.Wro","Jensen.mat","Chen.Yuan", "Then_hoenig","Then_pauly", "Jensen.mat","Charnov" or "Chen.Want". If none are specified then all applicable estimators could be chosen.

max.AAFC

The maximum Age class to be run in the analyses. This does not need to be the maximum age for the population and keeping this number reasonable reduces run-time.

n_cores

The number of cores to be used for parallel processing. It should be 1 core less than the maximum number available.

Value

A list with two data.frames. The first provides Fcritical values for each age class. This is the value of F where the population growth rate is stable (lambda = 1). The second dataframe is the mean lambda produced for each combination of AAFC and F

Examples

# load Silky shark data produced by create_data_input()
# Type `?create_data_input()` for details
data("Silky_data")

# Run function to get conduct an age-at-first-capture (AAFC) analysis using
# Monte Carlo Simulations using for all available natural mortality estimators.
# Set n = at least 1000 for full analysis but use n = 10 for testing given long run times

Simulate_AAFC(n = 10, Silky_data, n_cores = 1)