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 terra package and returns all simulations as SpatRaster objects, thus ensuring direct compatibility with common GIS tasks and a flexible and simple usage. Furthermore, it simulates NLMs within a self-contained, reproducible framework.

Installation

NLMR is currently not available on CRAN. The only way to install NLMR at the moment is:

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

Example

Each neutral landscape model 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	reference
nlm_percolation	Binary landscapes from thresholded random draws.	Gardner et al. (1989)
nlm_neigh	Categorical landscapes shaped by neighbourhood effects.	Scherer et al. (2016)
nlm_randomcluster	Nearest-neighbour random clusters.	Saura and Martinez-Millan (2000)
nlm_randomrectangularcluster	Overlapping rectangular clusters.	Gustafson and Parker (1992)
nlm_gaussianfield	Spatially correlated Gaussian random fields.	Schlather et al. (2015)
nlm_curds	Recursive curdling with optional wheying.	O’Neill, Gardner, and Turner (1992); Keitt (2000)
nlm_fbm	Fractional Brownian motion surfaces.	Schlather et al. (2015)
nlm_mpd	Midpoint displacement surfaces.	Peitgen and Saupe (1988)
nlm_distancegradient	Distance gradients measured from a rectangular origin.	Etherington, Holland, and O’Sullivan (2015)
nlm_edgegradient	Directional gradients with a central peak.	Travis and Dytham (2004); Schlather et al. (2015)
nlm_planargradient	Linear gradients in a specified or random direction.	Palmer (1992)
nlm_random	Independent random values drawn for each cell.	With and Crist (1995)
nlm_mosaicfield	Mosaic random fields generated by repeated bisection.	Schlather et al. (2015)
nlm_mosaicgibbs	Inhibited point-pattern tessellations.	Gaucherel (2008), Method 2
nlm_mosaictess	Voronoi tessellations from random seed points.	Gaucherel (2008), Method 1

Algorithm examples

Example outputs for the algorithms implemented in NLMR.

Meta

• Please report any issues or bugs.
• License: GPL3
• Get citation information for NLMR in R doing citation(package = 'NLMR')
• We are very open to contributions - if you are interested check out our Contributor Guidelines.
  • Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.