sdm (version 1.2-55)

background: Generate background (pseudo-absence) records

Description

The function uses different methods to genrates background or Pseudo-absence records over an study area which is assumed to be the non-NA cells in the input Raster layer(s) in x.

Usage

background(x,n,method,bias,sp,setting)

Value

a data.frame with spatial coordinates of background locations and the values of predictor variables extracted over the locations.

Arguments

x

an spatRaster or RasterStack or RasterBrick object with explanatory (predictor) variables that will be used to fit SDMs

n

size of background sample

method

a character, specifies the method of background generation; can be either of gRandom,eRandom,gDist,eDist

bias

optional, a Raster object (SpatRaster or RasterLayer) with a single layer that specifies bias map which can ONLY be used by the method gRandom

sp

species presence locations (either as a SpatVector/SpatialPoints or a data.frame/matrix object); this argument is needed if the method is either gDist or eDist

setting

optional, a list contains additional settings required by different methods (see details)

Author

Babak Naimi

https://www.r-gis.net/

https://www.biogeoinformatics.org/

Details

The following methods are available:

- gRandom (random selection over geographical space): this method randomly select the non-missing pixels over the study area. Same weights are given to each pixel through the random selection of points unless the bias layer is introduced by a user which is a single raster layer that specifys a weighting scheme for background generation. A pixel with a greater value in the bias layer would have a higher chance to be selected as a background record. It has been shown by some studies that if the same bias in collecting the presence records (e.g., locations that are close to roads and residential areas have higher chance to be visited for recording species presence) is used to generate background records, it can improve performance of SDMs.

- eRandom (random selection over environmental spece): this method tries to collect a uniform (i.e., evenly distributed) distribution of records over environmental gradiants by sampling in environmental space.

- gDist (random sampling weighted by geographic distnce): This method uses a random selection of locations over geographical space but gives more weights to locations with larger distance to species presence locations.

- eDist (random sampling weighted by environmental distance): This method uses a random selection of locations over geographical space but gives more weights to locations with environmental conditions that are more dissimilar to the locations where species are observed.

References

Naimi, B., Araujo, M.B. (2016) sdm: a reproducible and extensible R platform for species distribution modelling, Ecography, 39:368-375, DOI: 10.1111/ecog.01881

Examples

if (FALSE) {
  #########

# Let's read raster dataset containing predictor variables for this study area:

file <- system.file("external/predictors.tif", package="sdm") # path to a raster object

r <- rast(file)

r # a SpatRaster object including 2 rasters (covariates)

plot(r)
#----

file <- system.file("external/po_spatial_points.shp", package="sdm") # path to a shapefile

po <- vect(file) # spatial points with presence-only records


head(po) # it contains data for one species (sp4) and the dataset has only presence records!




b1 <- background(r,n=20,method = 'gRandom') # you may specify the bias file (a raster object)

head(b1) # background records generated using gRandom

b2 <- background(r,n=20,method = 'eRandom')

head(b2) # background records generated using eRandom

b3 <- background(r,n=20,method = 'eDist',sp=po)

head(b3) # background records generated using eDist


b4 <- background(r,n=20,method = 'gDist')

head(b4) # background records generated using gDist


}