sperich (version 1.3-1)

species.richness: Species richness estimation

Description

This function estimates the species richness based on given species occurences.

Usage

species.richness(dataset.all.species, landwatermask, 
	distances=1:10, weight=0.5, dimension, shift, resolution=1,
	upperbound, narrow.endemic=FALSE, narrow.endemic.limit=5,
	all.species=-1, silent=TRUE)

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 occurence 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

distances

The distances which will be used for species range estimation.

weight

The tuning parameter of the weighting procedure (details in Raedig et al. 2010).

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.

narrow.endemic

A boolean flag that determines if only narrow endemic species should be considered in species richness estimation.

narrow.endemic.limit

This value determines the limit of points up to which a species is considered as narrow endemic species.

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.

Value

• This function returns a grid which contains the weighted species richness information.

Details

This routine estimates the species richness based on given species occurences 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.weighted <- species.richness(dataset.all.species, 
		landwatermask.nocoast, weight=0.5, dimension, shift, resolution=1,
		upperbound=5, narrow.endemic=FALSE, narrow.endemic.limit=5, 
		all.species=1:20)