Species Distribution Models as a Function of Biotic, Abiotic and Movement Factors (BAM)
Convert distribution polygons to a presence-absence matrix (PAM)
Predict method of the package bamm.
sdm_sim: Simulate single species dispersal dynamics using the BAM fram...
Class for the A (Abiotic) Set of BAM Diagram
Class for the M set of the bamm diagram
shape2Grid: Function to create a grid given a spatial polygon
Show information in setA class bamm.
sim2Animation: Animate BAM simulation object.
sim2Raster: Convert a BAM simulation object to RasterStack
adj_mat: Function to compute the adjacency matrix of an area.
bam_clusters: Function to estimate the connectivity of suitable areas
bam_sim: Simulate dispersal dynamics using the set B of the BAM framew...
bam_ssim: Simulate dispersal dynamics using the set B of the BAM frame...
Class bam digram
Class bioindex_sparse
Class bioindex
Class community_sim digram
community_bam: Community bamm
csd_estimate: Estimate the connectivity suitability and dispersal plot
Class csd
csim2pam: Converts community simulation to a Presence Absence Matrix (...
range_diversity_analysis: diversity analysis
Class diversity_range
eigen_bam: Compute the Eigen system of two bam objects
S4 Class Hierarchy for BAM (Biotic-Abiotic-Movement) Modeling
jaccard: Estimates the Jaccard index for comparing two binary maps
Class leaflet leaflet
model2sparse: Converts a niche model into a diagonal sparse matrix
models2pam: Converts binary rasters to a PAM
null_dispersion_field_distribution: Null distribution of the dispersio...
occs2sparse: Converts occurrence data into a sparse matrix object
Class pam Presence-Absence Matrix
pam2bioindex: PAM to biodiversity index
pam2richness: Converts Presence Absence Matrix (pam object) to richnes...
permute_pam: Function to permute a Presence-Absence-Matrix.
Plot method for objects of class diversity_range bamm.
Species Distribution Modeling (SDM) is a practical methodology that aims to estimate the area of distribution of a species. However, most of the work has focused on estimating static expressions of the correlation between environmental variables. The outputs of correlative species distribution models can be interpreted as maps of the suitable environment for a species but not generally as maps of its actual distribution. Soberón and Peterson (2005) <doi:10.17161/bi.v2i0.4> presented the BAM scheme, a heuristic framework that states that the occupied area of a species occurs on sites that have been accessible through dispersal (M) and have both favorable biotic (B) and abiotic conditions (A). The 'bamm' package implements classes and functions to operate on each element of the BAM and by using a cellular automata model where the occupied area of a species at time t is estimated by the multiplication of three binary matrices: one matrix represents movements (M), another abiotic -niche- tolerances (A), and a third, biotic interactions (B). The theoretical background of the package can be found in Soberón and Osorio-Olvera (2023) <doi:10.1111/jbi.14587>.