📦 R package to simulate neutral landscape models 🏔
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Build StatusBuild status codecov CRAN_Status_Badge lifecycle DOI:10.1111/2041-210X.13076


NLMR is an R package for simulating neutral landscape models (NLM). Designed to be a generic framework like NLMpy, it leverages the ability to simulate the most common NLM that are described in the ecological literature. NLMR builds on the advantages of the raster package and returns all simulation as RasterLayer objects, thus ensuring a direct compability to common GIS tasks and a flexible and simple usage. Furthermore, it simulates NLMs within a self-contained, reproducible framework.


Install the release version from CRAN:


To install the developmental version of NLMR, use the following R code:

# install.packages("devtools")


Each neutral landscape models is simulated with a single function (all starting with nlm_) in NLMR, e.g.:

random_cluster <- NLMR::nlm_randomcluster(nrow = 100,
                                      ncol = 100,
                                      p    = 0.5,
                                      ai   = c(0.3, 0.6, 0.1),
                                      rescale = FALSE)

random_curdling <- NLMR::nlm_curds(curds = c(0.5, 0.3, 0.6),
                              recursion_steps = c(32, 6, 2))

midpoint_displacememt <- NLMR::nlm_mpd(ncol = 100,
                                 nrow = 100,
                                 roughness = 0.61)


NLMR supplies 15 NLM algorithms, with several options to simulate derivates of them. The algorithms differ from each other in spatial auto-correlation, from no auto-correlation (random NLM) to a constant gradient (planar gradients):






Simulates a randomly curdled or wheyed neutral landscape model. Random curdling recursively subdivides the landscape into blocks. At each level of the recursion, a fraction of these blocks is declared as habitat while the remaining stays matrix. When option q is set, it simulates a wheyed curdling model, where previously selected cells that were declared matrix during recursion, can now contain a proportion of habitat cells

Figure 1a,p

O’Neill, Gardner, and Turner (1992); Keitt (2000)


Simulates a distance gradient neutral landscape model. The gradient is always measured from a rectangle that one has to specify in the function (parameter origin)

Figure 1b

Etherington, Holland, and O’Sullivan (2015)


Simulates a linear gradient orientated neutral model. The gradient has a specified or random direction that has a central peak, which runs perpendicular to the gradient direction

Figure 1c

Travis and Dytham (2004); Schlather et al. (2015)


Simulates neutral landscapes using fractional Brownian motion (fBm). fBm is an extension of Brownian motion in which the amount of spatial autocorrelation between steps is controlled by the Hurst coefficient H

Figure 1d

Schlather et al. (2015)


Simulates a spatially correlated random fields (Gaussian random fields) model, where one can control the distance and magnitude of spatial autocorrelation

Figure 1e

Schlather et al. (2015)


Simulates a mosaic random field neutral landscape model. The algorithm imitates fault lines by repeatedly bisecting the landscape and lowering the values of cells in one half and increasing the values in the other half. If one sets the parameter infinit to TRUE, the algorithm approaches a fractal pattern

Figure 1f

Schlather et al. (2015)


Simulates a neutral landscape model with land cover classes and clustering based on neighbourhood characteristics. The cluster are based on the surrounding cells. If there is a neighbouring cell of the current value/type, the target cell will more likely turned into a cell of that type/value

Figure 1g

Scherer et al. (2016)


Simulates a binary neutral landscape model based on percolation theory. The probability for a cell to be assigned habitat is drawn from a uniform distribution

Figure 1h

Gardner et al. (1989)


Simulates a planar gradient neutral landscape model. The gradient is sloping in a specified or (by default) random direction between 0 and 360 degree

Figure 1i

Palmer (1992)


Simulates a patchy mosaic neutral landscape model based on the tessellation of a random point process. The algorithm randomly places points (parameter germs) in the landscape, which are used as the centroid points for a voronoi tessellation. A higher number of points therefore leads to a more fragmented landscape

Figure 1k

Gaucherel (2008), Method 1


Simulates a patchy mosaic neutral landscape model based on the tessellation of an inhibition point process. This inhibition point process starts with a given number of points and uses a minimisation approach to fit a point pattern with a given interaction parameter (0 ‐ hardcore process; 1 ‐ Poisson process) and interaction radius (distance of points/germs being apart)

Figure 1l

Gaucherel (2008), Method 2


Simulates a spatially random neutral landscape model with values drawn a uniform distribution

Figure 1m

With and Crist (1995)


Simulates a random cluster nearest‐neighbour neutral landscape. The parameter ai controls for the number and abundance of land cover classes and p controls for proportion of elements randomly selected to form clusters

Figure 1n

Saura and Martínez-Millán (2000)


Simulates a midpoint displacement neutral landscape model where the parameter roughness controls the level of spatial autocorrelation

Figure 1n

Peitgen and Saupe (1988)


Simulates a random rectangular cluster neutral landscape model. The algorithm randomly distributes overlapping rectangles until the landscape is filled

Figure 1o

Gustafson and Parker (1992)

See also

NLMR was split during its development process - to have a minimal dependency version for simulating neutral landscape models and an utility toolbox to facilate workflows with raster data. If you are interested in merging, visualizing or further handling neutral landscape models have a look at the landscapetools package.