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NLMR

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📦 R package to simulate neutral landscape models 🏔

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# NLMR <img src="man/figures/logo.png" align="right" width="150" />

**NLMR** is an `R` package for simulating **n**eutral **l**andscape
**m**odels (NLM). Designed to be a generic framework like
[NLMpy](https://pypi.org/project/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

NLMR is not available on CRAN at the moment (see \#95). The only way to
install NLMR at the moment is:

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

## Example

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

``` r
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):

<thead>

<tr>

Function
</th>

Description
</th>

Crossreference
</th>

Reference
</th>

</tr>

</thead>

<tbody>

<tr>

nlm_curds
</td>

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
</td>

Figure 1a,p
</td>

O’Neill, Gardner, and Turner (1992); Keitt (2000)
</td>

</tr>

<tr>

nlm_distancegradient
</td>

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)
</td>

Figure 1b
</td>

Etherington, Holland, and O’Sullivan (2015)
</td>

</tr>

<tr>

nlm_edgegradient
</td>

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
</td>

Figure 1c
</td>

Travis and Dytham (2004); Schlather et al. (2015)
</td>

</tr>

<tr>

nlm_fbm
</td>

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
</td>

Figure 1d
</td>

Schlather et al. (2015)
</td>

</tr>

<tr>

nlm_gaussianfield
</td>

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

Figure 1e
</td>

Schlather et al. (2015)
</td>

</tr>

<tr>

nlm_mosaicfield
</td>

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
</td>

Figure 1f
</td>

Schlather et al. (2015)
</td>

</tr>

<tr>

nlm_neigh
</td>

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
</td>

Figure 1g
</td>

Scherer et al. (2016)
</td>

</tr>

<tr>

nlm_percolation
</td>

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
</td>

Figure 1h
</td>

Gardner et al. (1989)
</td>

</tr>

<tr>

nlm_planargradient
</td>

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

Figure 1i
</td>

Palmer (1992)
</td>

</tr>

<tr>

nlm_mosaictess
</td>

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
</td>

Figure 1k
</td>

Gaucherel (2008), Method 1
</td>

</tr>

<tr>

nlm_mosaicgibbs
</td>

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)
</td>

Figure 1l
</td>

Gaucherel (2008), Method 2
</td>

</tr>

<tr>

nlm_random
</td>

Simulates a spatially random neutral landscape model with values drawn a
uniform distribution
</td>

Figure 1m
</td>

With and Crist (1995)
</td>

</tr>

<tr>

nlm_randomcluster
</td>

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
</td>

Figure 1n
</td>

Saura and Martínez-Millán (2000)
</td>

</tr>

<tr>

nlm_mpd
</td>

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

Figure 1n
</td>

Peitgen and Saupe (1988)
</td>

</tr>

<tr>

nlm_randomrectangularcluster
</td>

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

Figure 1o
</td>

Gustafson and Parker (1992)
</td>

</tr>

</tbody>

</table>

## Meta

- Please [report any issues or
  bugs](https://github.com/ropensci/NLMR/issues/new/).
- 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](CONTRIBUTING.md).
  - Please note that this project is released with a [Contributor Code
    of Conduct](CONDUCT.md). By participating in this project you agree
    to abide by its terms.

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NLMR

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