NLMR

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 compatibility to common GIS tasks and a flexible and simple usage. Furthermore, it simulates NLMs within a self-contained, reproducible framework.

Installation

Install the release version from CRAN:

install.packages("NLMR")

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

# install.packages("devtools")
devtools::install_github("ropensci/NLMR")

Example

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)

Overview

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

Function	Description	Crossreference	Reference
nlm\_curds	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)
nlm\_distancegradient	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)
nlm\_edgegradient	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)
nlm\_fbm	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)
nlm\_gaussianfield	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)
nlm\_mosaicfield	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 infinite to TRUE, the algorithm approaches a fractal pattern	Figure 1f	Schlather et al. (2015)
nlm\_neigh	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)
nlm\_percolation	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)
nlm\_planargradient	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)
nlm\_mosaictess	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
nlm\_mosaicgibbs	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
nlm\_random	Simulates a spatially random neutral landscape model with values drawn a uniform distribution	Figure 1m	With and Crist (1995)
nlm\_randomcluster	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)
nlm\_mpd	Simulates a midpoint displacement neutral landscape model where the parameter roughness controls the level of spatial autocorrelation	Figure 1n	Peitgen and Saupe (1988)
nlm\_randomrectangularcluster	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)

j-johanness/NLMR

NLMR

Installation

Example

Overview

See also

Meta