bootspat_str {SESraster}

Spatially structured sample

Description

Randomizes a raster stack with fixed richness. Randomizations are based on frequencies (given or calculated from x) and, optionally, a probability raster stack. Both, frequencies and probability raster stack, control the probability that a given species is sampled in each cell raster. Frequency controls the probability of each species being sampled compared to all others. Probability raster stack controls the probability that each species is sampled in a given raster cell.

Usage

bootspat_str(
  x,
  rprob = NULL,
  rich = NULL,
  fr_prob = NULL,
  cores = 1,
  filename = "",
  memory = NULL,
  overwrite = FALSE,
  ...
)

Arguments

x

SpatRaster. A presence-absence SpatRaster.

rprob

SpatRaster. Stack of probability values. Structures the spatial pattern of each randomized species.

rich

SpatRaster. Richness pattern structuring the sample size of each cell randomization. Calculated if not provided.

fr_prob

Either frequency of pixels or probability that a species is observed across the whole layer.

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)

filename

character. Output filename

memory

logical. Checks if there is enough available RAM memory. Calculated if NULL

overwrite

logical. If TRUE, filename is overwritten

...

additional parameters for terra::app

Value

SpatRaster object

Author(s)

Neander Marcel Heming

See Also

bootspat_naive, bootspat_ff, SESraster, algorithm_metrics

Examples

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

# applying the function
rand.str <- bootspat_str(r)
plot(rand.str)

# With null probability raster
rprobnull <- terra::app(r,
                       function(x){
                       ifelse(is.na(x), NA, 1)
                       })
rand.str2 <- bootspat_str(r, rprob = rprobnull)


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)

rprobnull <- terra::app(r10,
                       function(x){
                       ifelse(is.na(x), NA, 1)
                       })

# bootstrapping once
randr10 <- bootspat_str(r10, rprobnull)
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]))