BiodiversityR (version 2.10-1)

ensemble.spatialThin: Spatial thinning of presence point locations using the highly accurate geodesic estimates from the geosphere package

Description

The function creates a randomly selected subset of point locations where the shortest distance (geodesic) is above a predefined minimum. The geodesic is calculated more accurately (via distGeo) than in the spThin or red packages.

Usage

ensemble.spatialThin(x, thin.km=0.1, 
    runs=100, verbose=FALSE, return.matrix=FALSE)

Arguments

x

Point locations provided in 2-column (lon, lat) format.

thin.km

Threshold for minimum distance (km) in final point location data set.

runs

Number of runs to maximize the retained number of point locations.

verbose

Provide some details on each run.

return.matrix

Include the distance matrix (calculated via distGeo)) in the results.

Value

The function returns a spatially thinned point location data set.

Details

Locations with distances smaller than the threshold distance are randomly removed from the data set until no distance is smaller than the threshold. The function uses a similar algorithm as functions in the spThin or red packages, but the geodesic is more accurately calculated via distGeo.

With several runs (default of 100 as in the red package or some spThin examples), the (first) data set with the maximum number of records is retained.

References

Aiello-Lammens ME, Boria RA, Radosavljevic A, Vilela B and Anderson RP. 2015. spThin: an R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38: 541-545

See Also

ensemble.batch

Examples

# NOT RUN {
# get predictor variables, only needed for plotting
library(dismo)
predictor.files <- list.files(path=paste(system.file(package="dismo"), '/ex', sep=''),
    pattern='grd', full.names=TRUE)
predictors <- stack(predictor.files)
# subset based on Variance Inflation Factors
predictors <- subset(predictors, subset=c("bio5", "bio6", 
    "bio16", "bio17", "biome"))
predictors
predictors@title <- "base"

# presence points
presence_file <- paste(system.file(package="dismo"), '/ex/bradypus.csv', sep='')
pres <- read.table(presence_file, header=TRUE, sep=',')[, -1]

# number of locations
nrow(pres)

par.old <- graphics::par(no.readonly=T)
par(mfrow=c(2,2))

pres.thin1 <- ensemble.spatialThin(pres, thin.km=100, runs=10, verbose=T)
plot(predictors[[1]], main="10 runs", ext=extent(SpatialPoints(pres.thin1)))
points(pres.thin1, pch=20, col="red")

pres.thin2 <- ensemble.spatialThin(pres, thin.km=100, runs=10, verbose=T)
plot(predictors[[1]], main="10 runs", ext=extent(SpatialPoints(pres.thin2)))
points(pres.thin2, pch=20, col="red")

pres.thin3 <- ensemble.spatialThin(pres, thin.km=100, runs=100, verbose=T)
plot(predictors[[1]], main="100 runs", ext=extent(SpatialPoints(pres.thin3)))
points(pres.thin3, pch=20, col="red")

pres.thin4 <- ensemble.spatialThin(pres, thin.km=100, runs=100, verbose=T)
plot(predictors[[1]], main="100 runs", ext=extent(SpatialPoints(pres.thin4)))
points(pres.thin4, pch=20, col="red")

graphics::par(par.old)

# }
# NOT RUN {
# }