PVA: Using imported population and control parameters, run a...

In mairindeith/PVAInvasR_RPackage: PVA to assess invasive species control methods.

Using imported population and control parameters, run a population viability analysis (PVA) on the target species.

Description

Using imported population and control parameters, run a population viability analysis (PVA) on the target species.

Usage

PVA(
  params,
  custom_inits = NULL,
  sens_percent = NULL,
  sens_params = NULL,
  create_plot = FALSE,
  set_plot_y = NULL,
  quiet = FALSE
)

Arguments

params	A list of initialized population and control parameters to inform the PVA. Parameters should be provided in the form of a named list. We suggest filling in a parameter template, which can be created and loaded using the pva_template() and load_pva_parameters() functions.
custom_inits	(Optional, invoked by the 'rank_uncertainty' function) A vector containing the names of which parameters, if any, should differ from the values provided in pvA_params. Should be a named list_ Can be be outputs of the init() function from PVAInvas.
sens_percent	(Optional, invoked by the 'rank_uncertainty' function) For the sake of sensitivity analysis, how much should population parameters (i.e. , , , , , , )
create_plot	(Optional) Should a ggplot heatmap object also be provided? Default: false. Will return a list with the final entry being the created plot.

Value

pva * A named list of PVA outputs, including calculated parameters (from init()) and outputs of the PVA_ * Calculated outputs from init(): - 'phie': unfished eggs per recruit at equilibrium, - 'R_A': stage-independent maximum survival (alpha parameter of Beverton-Holt recruitment), - 'R_B': stage-independent carrying capacity (beta parameter of Beverton-Holt recruitment), - 'A_s': stanza-specific maximum survival (alpha parameter of Beverton-Holt recruitment), - 'B_s': stanza-specifc carrying capacity (beta parameter of Beverton-Holt recruitment).

* Objects created from PVA simulations - 'Nt': 3-dimensional abundance array (dimensions: time-steps, ages, simulations), - 'Et': matrix of eggs for each timestep for each year in each simulation, - 'nest': !!!!!!!!!!!! estimated numbers for each year in each simulation, - 'Vfin': vector of abundance in the final year across simulations, - 'p_extinct': vector of proportion of erdicated in each time step, - 'p_extinct_50': proportion of simulations where the population is eradicated by the 50th time step, - 'p_extinct_100': proportion of simulations where the population is eradicated by the 100th time step, - 'p_extinct_200': proportion of simulations where the population is eradicated by the 200th time step, - 't_extinct': minimum number of timesteps needed for eradiction, - 'yext_seq': - 'cost_1': annual cost of sampling, - 'cost_T': total cost of sampling (up to the end of simulation or until 100 - 'NPV': net present value of sampling (taking into account intergenerational discounting), - 'E_NPV': expected mean present value (mean of NPV) - 'NT': abundance in the final time-step (reported as 5th percentile, mean, and 95 percentile of distributions), - 'runtime': time to execute the PVA, - 'plot': (Optional) if 'create_plot=TRUE', 'plot' is returned as a ggplot object with multiple components. Created by the function 'vwReg2.R'.

Examples

# Run a simple PVA, no custom values or sensitivity testing.
pva(pva_params = inputParameterList)

mairindeith/PVAInvasR_RPackage documentation built on Feb. 1, 2023, 3:17 p.m.