LISA in predictors at species occurrence locations

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Description

Given a level of positional uncertainty (defined as a distance), this function calculates different statistics of local indicator of spatial association (LISA) in predictors (explanatory variables, defined as a raster object) at each species occurrence location (defined as a SpatialPoints object). According to Naimi et al. 2012, this can be used to understand whether positional uncertainty at which species locations are likely to affect predictive performance of species distribution models.

Usage

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speciesLisa(x, y, uncertainty, statistic="K1",weights) 

Arguments

x

explanatory variables (predictors), defined as a raster object (RasterLayer or RasterStack or RasterBrick)

y

species occurrence points, defined as a SpatialPoints or SpatialPointsDataFrame object

uncertainty

level of positional uncertainty, defined as a number (distance)

statistic

a character string specifying the LISA statistic that should be calculated. This can be one of "I", "c", "G", "G*", and "K1". Default is "K1"

weights

a numeric vector specifying the relative importance of explanatory variables in species distribution models (the first value in the weights, is the importance of the first variable in x, and ...). These values will be used as weights to aggregate the LISAs in predictors at each location and calculate a single measure. The length of weights should be equal to the number of raster layers in x

Details

This function calculates a LISA statistic for each explanatory variable at each species point. Although several statistics including local Moran's I ("I"), local Geary's c ("c"), local G and G* ("G" and "G*"), and local K1 statistics, can be calculated, according to Naimi et al. (2012), "K1" statistic (default) is recommended. This function returns a speciesLISA object, which includes species occurrence data, LISA statistic for each predictor at species locations, and an aggregated LISA statistic (a single LISA) at each species location, given the variable impotances. If weights in not specified, the equal weights (i.e. equal importance for explanatory variables) will be considered.

Value

speciesLISA

Author(s)

Babak Naimi naimi.b@gmail.com

http://r-gis.net

References

Naimi, B., Hamm, N.A.S., Groen, T.A., Skidmore, A.K., and Toxopeus, A.G. 2012. Where is positional uncertainty a problem for species distribution modelling, Ecography. Submitted.

See Also

lisa

Examples

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file <- system.file("external/predictors.grd", package="usdm")

r <- brick(file) # reading a RasterBrick object including 4 rasters in the Netherlands

r 

plot(r) # visualize the raster layers


#files.path <- system.file("external", package="usdm") # path to location of example files
#require(rgdal)
#sp <- readOGR(dsn=files.path,layer="species_nl") # reading species data (shapefile)

# alternatively, to read species data you can use:
#require(maptools)
#sp.file <- system.file("external/species_nl.shp", package="usdm")
#sp <- readShapePoints(sp.file)


#splisa <- speciesLisa(x=r,y=sp,uncertainty=15000,weights=c(0.22,0.2,0.38,0.2))

#splisa

#plot(splisa)

#bnd <- readOGR(dsn=files.path,layer="boundary") # reading the boundary map

#plot(splisa,bnd)