popbio (version 2.4.4)

multiresultm: Incorporate demographic stochasticity into population projections

Description

This function generates multinomial random numbers for state transitions and lognormal or binomial (for clutch size=1) random numbers for fertilities and returns a vector of the number of individuals per stage class at t+1.

Usage

multiresultm(n, T, F, varF=NULL)

Arguments

n

the vector of numbers of individuals per class at t

T

a transition T matrix

F

a fertility F matrix

varF

a matrix of inter-individual variance in fertilities, default is NULL for simulating population where clutch size = 1, so that fertilities give the probabilities of birth.

Value

The function returns a vector of the number of individuals per class at t+1.

References

Caswell, H. 2001. Matrix population models. Construction, Analysis and interpretation. 2nd ed. Sinauer, Sunderland, Massachusetts.

Morris, W. F., and D. F. Doak. 2002. Quantitative conservation biology: Theory and practice of population viability analysis. Sinauer, Sunderland, Massachusetts, USA.

Examples

# NOT RUN {
data(whale)
x<-splitA(whale)
whaleT<-x$T
whaleF<-x$F

multiresultm(c(1,9,9,9),whaleT, whaleF)
multiresultm(c(1,9,9,9),whaleT, whaleF)

## create graph similar to Fig 15.3 a
reps <- 10    # number of trajectories
tmax <- 200   # length of the trajectories
totalpop <- matrix(0,tmax,reps)  # initializes totalpop matrix to store trajectories
nzero <- c(1,1,1,1) # starting population size
for (j in 1:reps) 
{
   n <- nzero
   for (i in 1:tmax) 
   {
      n <- multiresultm(n,whaleT,whaleF)
      totalpop[i,j] <- sum(n)
   } 
} 
matplot(totalpop, type = 'l', log="y",
        xlab = 'Time (years)', ylab = 'Total population')
# }