sim.coarse: 2-species coexistence modeling based on the matrix listing out all combinations of parameter setting

Description

all combinations of parameter setting can be input as in the argument item "prange"

Usage

sim.coarse(island=10,scale = 2, dispersalscale = 51, allee = 1, T = 1000, prange, type, initp, path = NULL)

Arguments

island

number of patches

scale

controlling the parameter's low value=parameter original value/scale; while parameter's high value=parameter original value*scale. Specifically used in the spatial types.

dispersalscale

the scale value for reducing the connectivity probability between the neighbouring patches, allowing the simulation slow down or speed up.

allee

allee effect for the species, the minimum viable population in a local patch, default=1, indicating that if the population size in a patch for a species is less than 1, then the species was removed from that patch

simulation time/steps

prange

the matrix listing out all combinations of parameter setting

initp

initial population size for a species that will be released at the source patch at each time step

type

a model configuration vector describing the spatial patterns of each of the parameters, for example, a vector like this, c("decrease","decrease","decrease","increase","increase","increase","mosaiclow") can be used as the input. There are 5 simple spatial types currently for the package: "decrease",the parameter will have one-time decreasing transition from source patch to other sink patches in the middle of the patches "increase",the parameter will have one-time increasing transition from source patch to other sink patches in the middle of the patches "constant",the parameter will keep in a constant value across the patches during the simulation "mosaiclow",the parameter will switch from a low value to a high value one-by-one from the source patch to sink patches, so source patch the parameter for the species there will be assigned a low value "mosaichigh",the parameter will switch from a high value to a low value one-by-one from the source patch to sink patches,so source patch the parameter for the species there will be assigned a high value

path

local disk file name or folder to save the simulated outputs

Value

a list of output, each list member has a parameter combination vector, a matrix of species-site abundance

References

Chen YH (2012) coexist: an R package for performing species coexistence modeling and analysis under asymmetric dispersal and fluctuating source-sink dynamics. http://code.google.com/p/coexist.

Examples

Run this code

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (scale = 2, dispersalscale = 51, allee = 1, T = 1000, 
    prange = parspace, type = typevector, path = NULL) 
{
    parnum = 5
    parlen <- length(prange)
    outcome <- list()
    length(outcome) <- parlen^parnum
    outindex <- matrix(0, nrow = parlen^parnum, ncol = parnum)
    colnames(outindex) <- c("r1", "r2", "dis", "c11", "c22")
    habitat1 <- parsetting(island, good, scale, type[1])
    habitat2 <- parsetting(island, good, scale, type[2])
    resource <- rbind(habitat1, habitat2)
    count = 0
    outcomefile = filename.check(path)
    for (i1 in 1:parlen) {
        for (i2 in 1:parlen) {
            for (i3 in 1:parlen) {
                for (i4 in 1:parlen) {
                  for (i5 in 1:parlen) {
                    grow1 <- parsetting(island, rate = prange[i1], 
                      scale, type[3])
                    grow2 <- parsetting(island, rate = prange[i2], 
                      scale, type[4])
                    grow <- rbind(grow1, grow2)
                    dismat <- dispvar(island, rate = prange[i3]/dispersalscale, 
                      scale, type[5])
                    comp1 <- compvar(island, rate = prange[i4], 
                      scale, type[6])
                    comp2 <- compvar(island, rate = prange[i5], 
                      scale, type[7])
                    spvector <- rbind(spabundance(island, 1000), 
                      spabundance(island, 1000))
                    count = count + 1
                    outindex[count, ] <- c(prange[i1], prange[i2], 
                      prange[i3], prange[i4], prange[i5])
                    for (j in 1:T) {
                      spvector <- competition(spvector, resource, 
                        comp1, comp2, grow, allee)
                      spvector <- dispersal(spvector, dismat, 
                        allee)
                    }
                    outcome[[count]] <- spvector
                    write.table(outcome[[count]], file = outcomefile, 
                      sep = "\t", append = TRUE)
                    outcome[[count]] <- list(abund = outcome[[count]], 
                      pars = outindex[count, ])
                  }
                }
            }
        }
    }
    return(outcome)
  }