Carrying Capacity Map Simulator — K

Generates multiple carrying capacity maps based on spatially autocorrelated Gaussian Random Fields (GRFs), with optional correlation between layers.

Usage

K_sim(n, id, range, cor_mat = NULL, qfun = qnorm, ...)

Arguments

n: Integer. Number of maps to generate.
id: A SpatRaster object used as a geographic template.
range: Numeric. Spatial autocorrelation parameter passed to the grf function.
cor_mat: Optional correlation matrix. If NULL, maps are generated independently.
qfun: Quantile function to apply to the generated GRFs (default: qnorm).
...: Additional arguments passed to the quantile function qfun.

Value

A SpatRaster object with n layers, each representing a carrying capacity map.

Examples

library(terra)
#> terra 1.9.46
library(FieldSimR)

# Community parameters
nspec <- 4
nrows <- ncols <- 10
xmin <- 250000; xmax <- xmin + nrows * 1000
ymin <- 600000; ymax <- ymin + ncols * 1000
id <- rast(nrows = nrows, ncols = ncols, xmin = xmin, xmax = xmax,
                                         ymin = ymin, ymax = ymax)
crs(id) <- "epsg:2180"

# Correlation matrix of carrying capacities
cor_mat <- FieldSimR::rand_cor_mat(nspec, -0.5, 0.5, pos.def = TRUE)
#> 'cor_mat' is already positive (semi)-definite, matrix was not altered
cor_mat
#>            1          2          3          4
#> 1  1.0000000 -0.4192499  0.3343330  0.1007609
#> 2 -0.4192499  1.0000000 -0.3427916 -0.4926006
#> 3  0.3343330 -0.3427916  1.0000000 -0.0336065
#> 4  0.1007609 -0.4926006 -0.0336065  1.0000000

# Generate and define the distributions and parameters
# of correlated carrying capacity maps
K_map <- K_sim(nspec, id, range = 20000, cor_mat = cor_mat, qfun = qlnorm,
               meanlog = 2, sdlog = 0.5)
K_map
#> class       : SpatRaster
#> size        : 10, 10, 4  (nrow, ncol, nlyr)
#> resolution  : 1000, 1000  (x, y)
#> extent      : 250000, 260000, 600000, 610000  (xmin, xmax, ymin, ymax)
#> coord. ref. : ETRF2000-PL / CS92 (EPSG:2180)
#> source(s)   : memory
#> names       :         1,         2,         3,         4
#> min values  :  1.991756,  1.919727,  2.519688,  1.981645
#> max values  : 26.002093, 21.939949, 18.626261, 27.204118
hist(K_map)

plot(K_map)