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Last data update: 2014.03.03

R: The spatial and temporal model of succession in the Swiss...

SNPsm

R Documentation

The spatial and temporal model of succession in the Swiss National Park

Description

A dynamic model of succession on alp Stabelchod in the Swiss Nationl Park using differential equations and numerial integration. 6 species guilds are considered. Space is conceived as a grid of 30 times 40 cells. Typical simulation time is around 500yr.

Usage

SNPsm(trange,tsl,diff,r6,...)
SNPsm2(trange=100,tsl=5.0,diff=0.001,r6=NULL)

## Default S3 method:
SNPsm(trange, tsl, diff, r6, ...)
## S3 method for class 'SNPsm'
plot(x, ...,out.seq=1,col=FALSE)

Arguments

`trange`	Time range of simulation in yr
`tsl`	Time range of simulation in yr
`out.seq`	Time interval (yr) at which maps of the state are printed
`diff`	A diffusion coefficient driving random spatial propagation
`r6`	Growth rates of 6 guilds involved, increase in cover percentage per yr
`...`	Parameter out.seq, the plotting interval
`x`	An object of class "SNPsm"
`col`	A logical variable to suppress color printing

Value

An object of class "SNPsm" with at least the following items:

`n.time.steps`	Number of time steps used for numerical integration
`imax`	Vertical grid count
`jmax`	Horizontal grid count
`time.step.length`	The time step length in yr
`veg.types`	The names of the vegetation types, i.e., the species
`vegdef`	A nspecies x nspecies matrix defining composition of vegetation types
`growth.rates`	The growth rates given upon input
`sim.data`	Simulated scores of all species (guilds) during simulation time
`tmap`	The 30 x 40 grid map of types used as initial condition
`igmap`	The same as tmap
`frame`	A 30 x 40 grid showing initial forest edges, used for printing

Author(s)

Otto Wildi

References

Wildi, O. 2002. Modeling succession from pasture to forest in time and space. Community Ecology 3: 181–189.

Wildi, O. 2013. Data Analysis in Vegetation Ecology. 2nd ed. Wiley-Blackwell, Chichester.

Examples

r6=NULL           # imposes default growth rates
o.stSNP<- SNPsm(trange=100,tsl=10.0,diff=0.001,r6)
plot(o.stSNP,out.seq=50)