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Authors: Luis Osorio-Olvera & Jorge Soberon
The bamm package is an R package designed to estimate
dynamic models of species distributions using the concepts of the
BAM scheme. It allows to operate on large matrices
(tens of millions of cells) regarding to each element of the
BAM, for example, the adjacency matrix (connectivity
matrix), and the niche suitability matrices.
The dynamic model behind the package is the cellular automata
The main functions of the package are:
model2sparse: it is the basic function of
the package, it converts a binary niche model (in raster format) to
sparse matrix model (object of class setA).
adj_mat: the function returns the sparse
representation of the adjacency matrix of a given raster (generally is
the M area but can be any area) given a movement hypothesis. The user
can ask the function to return the eigen-analysis of the
matrix.
bam_clusters: function to estimate the
connectivity of suitable areas given an adjacency matrix. It returns
three objects: a) an dynamic map (open-street map) of connected areas or
clusters; b) the data.frame with coordinates the geographic cluster
membership; c) a raster object of with cluster IDs.
csd_estimate:This function is used to
estimate the CSD-plot. It gives you an idea about the dispersal distance
that a species needs to travel to fill its potential area of
distribution.
occs2sparse: Converts occurrence data into
a sparse matrix object. This object is used to declare the initial
conditions for modeling the invasion dynamics of a species.
sdm_sim: Simulate single species dispersal
dynamics using the cellular automaton of the area of
distribution.
install.packages("bamm")if (!require('devtools')) install.packages('devtools')
devtools::install_github('luismurao/bamm')
# If you want to build vignette, install pandoc before and then
devtools::install_github('luismurao/bamm',build_vignettes=TRUE)We are grateful to our many colleagues in the University of Kansas Niche Modeling Group for many vivacious and useful discussions on the topics of the paper. LOO acknowledges partially supported by Programa de Apoyo a Proyectos de Investigación e Innovación Tecnológica PAPIIT-IA202824 and Consejo Nacional de Ciencia y Tecnología (CONACyT; postdoctoral fellowship number 740751; CVU: 368747). LOO and JS acknowledges Blitzi Soberon for moral support.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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