species.richness.nonweighted {sperich} | R Documentation |
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, origin, resolution=1,
upperbound, all.species=-1, silent=TRUE,
do.parallel=FALSE)
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. |
origin |
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. |
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).
Value
This function returns a grid which contains the species richness information for a given distance.
Author(s)
Maximilian Lange, Sven Lautenbach
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)
origin <- getOrigin(dataset.all.species)
##create landwatermask
landwatermask.nocoast <- createLandwatermask(dataset.landwater,
dimension, origin, resolution=1)
##estimate species richness
species.richness.noweight <- species.richness.nonweighted(dataset.all.species,
landwatermask.nocoast, distance=5, dimension,
origin, resolution=1, upperbound=5, all.species=1:2)