Rramas (version 0.1-8)

decline: Compute Declination and Explosion Probabilities

Description

Compute declination (or explosion) probabilities for a stage-structured population. From a vector of initial stage abundances and a transition matrix, decline and explosion compute respectively the probability that the population size falls below or surpasses some abundance thresholds during a given time interval.

Usage

decline(rmas, bootsp = 1000)
explosion(rmas, bootsp = 1000)
# S3 method for rmas.risk
summary(object, q = c(0.025, 0.975),...)
# S3 method for summary.rmas.risk
plot(x, ylim = NULL, col = NULL, xlab = NULL, ylab = NULL, main = NULL, ...)

Value

Both decline and explosion return an object of class rmas.rsik, basically a list with the following elements

cf.obs

a data.frame with the evaluated thresholds and their probabilities.

cf.boot

a list of data.frames similar to cf.obs for each bootstraped sample.

abminbot

a list of bootstraped minimum (or maximum for explosion) abundances for each replica in the rmas object.

main

Text to appear as title when plotting the summary.

The methods summary and plot.summary summarize the results and print and plot probabilities and bootstraped confidence interval of attainning a particular population threshold.

Arguments

rmas

An object of class rmas,i.e., resulting from projectn.

bootsp

number of botstraped samples.

object

An object of class rmas.rsik, i.e. resulting from declineor explosion.

x

An object of class summary.rmas.rsik.

q

vector of quantiles to compute bootstraped confidence intervals.

ylim

Vector with max and min values of the y (abundances) axis.

col

Color or vector of colors to draw the trajectories.

xlab

Label for the x-axis.

ylab

Label for the y-axis.

main

Text to appear as title.

...

Other parameters passed to plot and other methods.

Author

Marcelino de la Cruz Rot

Details

Both decline and explosion require that some stochastic simulations for a given time interval had been previously constructed (using projectn). Using those simulations decline computes the probability of falling bellow some population threshold (and explosion the probability of surpassing it) as the ratio between the number of times that these threshold has been attained and the number of replications. The set of abundances in each time interval (in all the simulations) are bootstraped (i.e. sampled with replacement) to build a confidence interval.

References

Akcakaya, H. R., Burgman, M. A. and Ginzburg L.R. 1999. Applied Population Ecology. Sinauer.

Caswell, H. 2003. Matrix Population Models: Construction, Analysis, and Interpretation . Sinauer.

Examples

  
     data(coryphanthaA)
     coryphanthaA <- as.tmatrix(coryphanthaA)
     #initial abundances:
     v0 <- c(100,0,0)
       
     # run 500 simulations of 20 years with  demographic stochasticity:
     simu20.ds <- projectn(v0=v0, mat=coryphanthaA, time = 20, estdem=TRUE, nrep=500)
     
     # compute declination probabilities
     simu20.ds.dec <- decline(simu20.ds)
     
     summary(simu20.ds.dec)