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 class 'rmas.risk'
summary(object, q = c(0.025, 0.975),...)
## S3 method for class 'summary.rmas.risk'
plot(x, ylim = NULL, col = NULL, xlab = NULL, ylab = NULL, main = NULL, ...)
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.
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.
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.
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
## Not run:
data(coryphanthaA)
coryphanthaA <- as.tmatrix(coryphanthaA)
#initial abundances:
v0 <- c(100,0,0)
# run 1000 simulations of 20 years with demographic stochasticity:
simu20.ds <- projectn(v0=v0, mat=coryphanthaA, time = 20, estdem=TRUE, nrep=1000)
# compute declination probabilities
simu20.ds.dec <- decline(simu20.ds)
summary(simu20.ds.dec)
## End(Not run)