sperich (version 1.5-8)

species.richness.nonweighted: Species richness estimation without weighting

Description

This function estimates the species richness for a given distance based on given species occurrences without using weighting factor.

Usage

species.richness.nonweighted(dataset.all.species, landwatermask, 
	distance=10, dimension, shift, resolution=1,
	upperbound, all.species=-1, silent=TRUE,
	do.parallel=FALSE)

Value

This function returns a grid which contains the species richness information for a given distance.

Arguments

dataset.all.species

A dataset containing the species with their ID (named: speciesID)and the longitude (named: long) and latitude (named: lat) of their occurrence location.

landwatermask

A grid containing the land-water-information of the observed area. If a grid cell containes no land, the value of the cell in the landwatermask is -1, otherwise it is 0. Additional, height-informations could be added for land surfaces. In this case, take care of the 'upperbound' value.

distance

The distance which will be used for species range estimation.

dimension

The dimension of the processed grid.

shift

The geographic coordinates of the origin of the grid.

resolution

The resolution of the grid in (geographical) degree.

upperbound

This value determines the height (based on values in 'landwatermask') which is considered to be a barrier for species distribution.

all.species

The vector with the numbers of the species which should be mentioned. If the first value is -1, all species in the database will be used for species richness estimation.

silent

A boolean flag that determines wether the report of status messages should be suppressed or not.

do.parallel

A boolean flag determining wether the function uses the 'foreach'-package to estimate species richness via parallel processing.

Author

Maximilian Lange, Sven Lautenbach

Details

This routine estimates the species richness for a given distance (without weighted summation) based on given species occurrences through a geometric interpolation model (details in Raedig et al. 2010).

References

Raedig, C., Dorman, C.F., Hildebrandt, A. and Lautenbach, S. (2010). Reassessing Neotropical angiosperm distribution patterns based on monographic data: a geometric interpolation approach. Biodivers Conserv, 19, 1523-1546.

Examples

##load data
data(dataset.all.species)
data(dataset.landwater)

##create grid parameters
dimension <- getDimension(dataset.all.species, resolution=1)
shift <- getShift(dataset.all.species)

##create landwatermask
landwatermask.nocoast <- createLandwatermask(dataset.landwater, 
				dimension, shift, resolution=1)

##estimate species richness
species.richness.noweight <- species.richness.nonweighted(dataset.all.species, 
		landwatermask.nocoast, distance=5, dimension, 
		shift, resolution=1, upperbound=5, all.species=1:2)