Description Usage Arguments Details Value Author(s) References Examples
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.
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rmas |
An object of class |
bootsp |
number of botstraped samples. |
object |
An object of class |
x |
An object of class |
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. |
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.
Both decline
and explosion
return an object of class rmas.rsik
, basically a list with the following elements
cf.obs |
a |
cf.boot |
a list of data.frames similar to |
abminbot |
a list of bootstraped minimum (or maximum for |
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.
Marcelino de la Cruz Rot
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.
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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)
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