The bamm
package is an R package designed to create and
operate on large (tens of millions of cells) matrices regarding to each
element of the \(\textbf{BAM}\) scheme,
for example, the adjacency matrix (connectivity matrix), and the niche
suitability matrices.
The following vignette presents the basic functions and methods of
the bamm
package. The package has three main
functionalities related to each element of the BAM
scheme.
The package uses sparse matrices to represent those objects which
allows it to optimize computers memory and make important speed ups in
computation times by eliminating operations on zero elements. The basic
function of the package is model2sparse
which converts a
raster model to an setA class
model_path <- system.file("extdata/Lepus_californicus_cont.tif",
package = "bamm")
model <- raster::raster(model_path)
# binary model
model_bin <- model > 0.7
sparse_mod <- bamm::model2sparse(model = model_bin)
The slots of the object are
sparse_mod
#> Set A of the BAM digram it contains 8 slots
#>
#> @niche_model: a niche model:
#>
#> class : RasterLayer
#> dimensions : 91, 159, 14469 (nrow, ncol, ncell)
#> resolution : 0.2, 0.2 (x, y)
#> extent : -118.4042, -86.60417, 14.51846, 32.71846 (xmin, xmax, ymin, ymax)
#> crs : +proj=longlat +datum=WGS84 +no_defs
#> source : memory
#> names : layer
#> values : 0, 1 (min, max)
#>
#> @suit_threshold: Threshold value used to binarize model@cellIDs: ids of the cells that have values (4281 pixels)
#>
#> @suit_values: Suitability values of the continuous model
#>
#> @sparse_model: A sparse square matrix of 4281 x 4281 entries
#>
#> @coordinates: Pixel centroid coordinates of the model
The function to estimate connectivity between pixels of a given
raster is adj_mat
, (generally is the M area but can be any
matrix); it uses sparse adjacency matrices.
# Adjacency matrix from a niche model
adj_mod <- adj_mat(sparse_mod,ngbs=1,eigen_sys = T)
adj_mod
#> Set M of the BAM digram it contains 7 slots
#>
#> @coordinates: A matrix with longitude and latitude values of each cell of the raster area
#>
#> x y
#> [1,] -115.9042 32.61846
#> [2,] -115.7042 32.61846
#> [3,] -115.5042 32.61846
#> [4,] -115.3042 32.61846
#> [5,] -115.1042 32.61846
#> [6,] -114.9042 32.61846
#> @eigen_val: Eigen values of the connectivity matrix M
#>
#> [1] 7.975459
#> @eigen_vec: Eigen vector of the connectivity matrix M
#>
#> [,1]
#> [1,] -8.640620e-11
#> [2,] -1.555028e-10
#> [3,] -2.564191e-10
#> [4,] -4.169027e-10
#> [5,] -6.556495e-10
#> [6,] -8.733471e-10
The map of the connectivity matrix adj_mod
is
These are objects that combine SetA and SetM .
Estimates the connectivity suitability and dispersal diagram. It shows the number of geographic clusters as a function of dispersal and suitability.
The function returns the following slots
clustersin
#> Object of class csd it contains 3 slots:
#>
#> @connections: Geographic clusters data.frame
#>
#> x y clusterID cluster_size
#> 1 -116.3042 32.41846 1 37
#> 2 -116.7042 32.21846 1 37
#> 3 -116.5042 32.21846 1 37
#> 4 -116.3042 32.21846 1 37
#> @interactive_map: A leaflet map showing the geographic clusters
#>
#> @raster_map: A raster map of the clusters
Lets see the geographic clusters as function of suitability and dispersal
The CSD diagram is a simple but useful tool to estimate the number of dispersal steps that a species needs to travel per unit time in order to occupy its potential area of distribution. In this example the approximate number of dispersal steps to archive all suitable patches is 30 (not shown here).
csd_plot <- bamm::csd_estimate(sparse_mod,
dispersal_steps=c(2,4,8))
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