nlm_gaussianfield

Simulates a spatially correlated random fields (Gaussian random fields) neutral landscape model.

Usage

nlm_gaussianfield(
  ncol,
  nrow,
  resolution = 1,
  autocorr_range = 10,
  mag_var = 5,
  nug = 0.2,
  mean = 0.5,
  user_seed = NULL,
  rescale = TRUE
)

Arguments

ncol

[numerical(1)]
Number of columns forming the raster.

nrow

[numerical(1)]
Number of rows forming the raster.

resolution

[numerical(1)]
Resolution of the raster.

autocorr_range

[numerical(1)]
Maximum range (raster units) of spatial autocorrelation.

mag_var

[numerical(1)]
Magnitude of variation over the entire landscape.

nug

[numerical(1)]
Magnitude of variation in the scale of autocorr_range, smaller values lead to more homogeneous landscapes.

mean

[numerical(1)]
Mean value over the field.

user_seed

[numerical(1)]
Set random seed for the simulation

rescale

[numeric(1)]
If TRUE (default), the values are rescaled between 0-1.

Details

Gaussian random fields are a collection of random numbers on a spatially discrete set of coordinates (landscape raster). Natural sciences often apply them with spatial autocorrelation, meaning that objects which distant are more distinct from one another than they are to closer objects.

References

Kéry & Royle (2016) Applied Hierarchical Modeling in Ecology Chapter 20

Examples

# simulate random gaussian field
gaussian_field <- nlm_gaussianfield(ncol = 90, nrow = 90,
                                    autocorr_range = 60,
                                    mag_var = 8,
                                    nug = 5)

if (FALSE) {
# visualize the NLM
landscapetools::show_landscape(gaussian_field)
}