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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