Description Usage Arguments Details Value References Examples

Create a neutral landscape model with categories and clustering based on neighborhood characteristics.

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`ncol` |
[ |

`nrow` |
[ |

`resolution` |
[ |

`p_neigh` |
[ |

`p_empty` |
[ |

`categories` |
[ |

`neighbourhood` |
[ |

`proportions` |
[ |

`rescale` |
[ |

The algorithm draws a random cell and turns it into a given category based on
the probabilities `p_neigh`

and `p_empty`

, respectively. The decision is
based on the probability `p_neigh`

, if there is any cell in the Moore- (8 cells) or
Von-Neumann-neighborhood (4 cells), otherwise it is based on `p_empty`

. To create
clustered neutral landscape models, `p_empty`

should be (significantly) smaller than
`p_neigh`

. By default, the Von-Neumann-neighborhood is used to check adjacent
cells. The algorithm starts with the highest categorical value. If the
proportion of cells with this value is reached, the categorical value is
reduced by 1. By default, a uniform distribution of the categories is
applied.

RasterLayer

Scherer, Cédric, et al. "Merging trait-based and individual-based modelling:
An animal functional type approach to explore the responses of birds to
climatic and land use changes in semi-arid African savannas."
*Ecological Modelling* 326 (2016): 75-89.

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