nicheSynthese {sdmvspecies}

nicheSynthese

Description

niche synthese method

Usage

nicheSynthese(env.stack, config, stack = FALSE, random.error = FALSE)

Arguments

env.stack

a rasterStack object that contain the environment variable

config

config is a list or matrix or data.frame that contain config info, details see details part

stack

stack is an option that if you want not compose them togethor (result return as a rasterStack). Default is FALSE

random.error

add random error on cell or not. Default is FALSE

Details

This method mainly implement niche synthese method, for more details see references

You can write several paragraphs.

Value

rasterLayer or rasterStack if stack is set to TRUE

References

Hirzel, A. H., Helfer, V., & Metral, F. (2001). Assessing habitat-suitability models with a virtual species. Ecological modelling, 145(2), 111-121.

Examples

# load the sdmvspecies library
library("sdmvspecies")
library("raster")
# find package's location
package.dir <- system.file(package="sdmvspecies")
# let see where is our sdmvspecies is installed in
package.dir
# find env dir under the package's location
env.dir <- paste(package.dir, "/external/env/", sep="")
# let see env dir
env.dir
# get the environment raster file
env.files <- list.files(env.dir, pattern="*.bil$", full.names=TRUE)
# see the file list
env.files
# put the environment file in a raster stack,
# which require all the environment should have same resolution and extend
env.stack <- stack(env.files)
# let see the env.stack var
env.stack
# here let's configure the environment response function and weight
config <- list(
    c("bio1","1",2),
    c("bio14", "2", 2),
    c("bio5", "3", 1),
    c("bio11", "4", 2),
    c("bio16", "5", 1)
)
# call the niche synthsis method
species.raster <- nicheSynthese(env.stack, config)
# let see the result raster,
# you should noticed that it's continue value map not distributin map
species.raster

# write the map to file, so you can use it latter in GIS software
# or further analysis.
#
#writeRaster(species.raster, "synthese.img", "HFA", overwrite=TRUE)

# to make binary distribution map, you should chosee a threshold to make map
# see the map then to decide the threshold to binary
plot(species.raster)
# choice threshold, here we choice 4
threshold <- 14
# make binary map
distribution.map <- species.raster > threshold
# plot the map out
plot(distribution.map)