pop (version 0.1)

simulation: Stochastic Simulation

Description

Simulate a population dynamic model in discrete time, recording the number of individuals in each state at each time point.

Usage

simulation(dynamic, population, timesteps = 1, replicates = 1, ncores = NULL)
is.simulation(x)
"plot"(x, states = NULL, patches = 1, ...)

Arguments

dynamic

a population dynamic model of class dynamic

population

a dataframe or named vector of positive integers, giving the number of individuals in each state of dynamic. If a dataframe, it should have only one row (as in the examples below), or as many rows as patches in the metapopulation if a multi-patch landscape has been defined for dynamic (using landscape). If a multi-patch landscape has been defined for dynamic, but population has only one row or is a vector, this population will be duplicated for all patches in the landscape.

timesteps

a positive integer giving the number of time steps (iterations) over which to simulate the model

replicates

a positive integer giving the number of independent time series to simulate

ncores

an optional positive integer giving the number of cpu cores to use when running simulations. By default (when ncores = NULL) all cores are used (or as many as parallel::detectCores can find). This argument is ignored is replicates = 1

x

a simulation object, or an object to be tested as a simulation

states

a character vector naming the states in the dynamic object used to run the simulation that should be plotted. By default all of them are.

patches

vector of positive integers identifying the patches for which to plot the simulations. By default only projections for the first patch are plotted.

...

further arguments passed to or from other methods.

Value

an object of class simulation

Details

The order of the dynamics in the simulation is defined by the order in which the transitions were passed to dynamic. I.e. if the stasis probability of a life stage (e.g. fraction surviving and remaining in the stage) was specified before the reproduction rate, then only those staying in the state will reproduce. Conversely, if reproduction was given first, individuals will reproduce before the stasis probability is applied.

Examples

# set up a three-stage model
stasis_egg <- tr(egg ~ egg, p(0.6))
stasis_larva <- tr(larva ~ larva, p(0.4))
stasis_adult <- tr(adult ~ adult, p(0.9))
hatching <- tr(larva ~ egg, p(0.35))
fecundity <- tr(egg ~ adult, r(20))
pupation <- tr(adult ~ larva, p(0.2))

pd <- dynamic(stasis_egg,
              stasis_larva,
              stasis_adult,
              hatching,
              pupation,
              fecundity)

population <- data.frame(egg = 1200, larva = 250, adult = 50)

# simulate for 50 timesteps, 30 times
sim <- simulation(dynamic = pd,
                  population = population,
                  timesteps = 50,
                  replicates = 30,
                  ncores = 1)

is.simulation(sim)
par(mfrow = c(3, 1))
plot(sim)