Spatially explicit landscape on a square grid

Sites on a side x side grid, optionally wrapped as a torus so that no site lies on an edge. The environment is spatially autocorrelated: independent normal deviates are smoothed by a Gaussian kernel with a bandwidth of env_bandwidth cells, and then given a uniform marginal distribution on env_range by ranks. Emigrants reach the other sites with probabilities from dispersal_kernel().

Usage

landscape_grid(
  side = 10,
  env_bandwidth = 2,
  disp_scale = 2,
  torus = TRUE,
  env_range = c(0, 100)
)

Arguments

side

Number of cells per side.

env_bandwidth

Bandwidth (in cells) of the Gaussian kernel that smooths the environment. Larger values give smoother gradients.

disp_scale

Scale (in cells) of the exponential dispersal kernel.

torus

Wrap the grid as a torus?

env_range

Range of the uniform marginal distribution of the environment.

Value

An mc_landscape object: a list with env, coords (data frame with columns x and y), dist (distances among sites, toroidal if torus = TRUE) and dispersal (row-stochastic matrix with a zero diagonal).

See also

torus_permutations() for site permutations that keep the spatial structure of a torus.

Examples

set.seed(1)
ls <- landscape_grid(side = 8)
image(matrix(ls$env, 8, 8), asp = 1)