Randomize a set of rasters according to the observed frequency.

Randomize a set of rasters according to the observed frequency using the methods: sites (by cells), species (by layer) or both (layers and cells). The randomization not assign values to cells with nodata.

Usage

bootspat_naive(
  x,
  random = c("site", "species", "both"),
  filename = "",
  memory = NULL,
  cores = 1,
  ...
)

Arguments

x: SpatRaster. A presence-absence SpatRaster.
random: character. Character indicating the type of randomization to be used. The available types are by "site", "specie" or "both". The first method (site) keeps species richness constant within each site (cell)pixel by randomizing the position (presence/absence) of the species within each cell of the stack.
filename: character. Output filename
memory: logical.
cores: positive integer. If cores > 1, a 'parallel' package cluster with that many cores is created and used. You can also supply a cluster object. Ignored for functions that are implemented by terra in C++ (see under fun)
...: additional arguments to be passed passed down from a calling function.

Value

SpatRaster object

Details

The first method (site) is performed within each site (cell) by randomizing the position (presence/absence) of the species within each cell of the stack. This method keeps species richness constant at each cell but the size of the species distribution might change. The second method (species) is performed at each layer (species) of the stack by randomizing the position of species presences in space. This method changes the species richness at each cell but the size of the species distribution is held constant (except if randomization is performed by frequency). The third method (both) combines randomization by site and species at the same time. This method will shuffle all presences across cells and layers, changing site richness and species distribution sizes and location at the same time.

Author

Neander Marcel Heming and Gabriela Alves-Ferreira

Examples

library(terra)
# load random species distributions
r <- load_ext_data()
plot(r)


# randomize pres/abs data by site
rn <- bootspat_naive(r, "site")
plot(rn)



library(SESraster)
library(terra)
# creating random species distributions
f <- system.file("ex/elev.tif", package="terra")
r <- rast(f)
set.seed(510)
r10 <- rast(lapply(1:18,
                function(i, r, mn, mx){
                  app(r, function(x, t){
                    sapply(x, function(x, t){
                       x<max(t) & x>min(t)
                    }, t=t)
                  }, t=sample(seq(mn, mx), 2))
                }, r=r, mn=minmax(r)[1]+10, mx=minmax(r)[2]-10))

names(r10) <- paste("sp", 1:nlyr(r10))
plot(r10)


# bootstrapping once
randr10 <- bootspat_naive(r10, "site")
plot(randr10)


plot(c(sum(r10), sum(randr10)), main=c("observed", "randomized"))

cbind(observed=sapply(r10, function(x)freq(x)[2,3]),
      randomized=sapply(randr10, function(x)freq(x)[2,3]))
#>       observed randomized
#>  [1,]      767       1813
#>  [2,]     3443       1809
#>  [3,]     1175       1771
#>  [4,]      889       1789
#>  [5,]      908       1814
#>  [6,]     2160       1784
#>  [7,]      548       1789
#>  [8,]      133       1857
#>  [9,]      122       1830
#> [10,]     4174       1780
#> [11,]     2565       1753
#> [12,]     3031       1828
#> [13,]       36       1781
#> [14,]     4387       1816
#> [15,]     3270       1820
#> [16,]     2198       1793
#> [17,]     2427       1815
#> [18,]      235       1826