diversity_range_analysis {bamm}

range_diversity_analysis: diversity analysis

Description

diversity_range_analysis biodiversity indices related to diversity-range plots

Usage

diversity_range_analysis(
  pam,
  xy_mat = NULL,
  lower_interval = 0.05,
  upper_interval = 0.95,
  raster_templete = NULL,
  niter = 100,
  return_null_dfield = FALSE,
  randal = "indep_swap",
  parallel = TRUE,
  n_cores = 2
)

Arguments

pam

A Presence-Absence-Matrix of matrix class or sparse matrix.

xy_mat

A two dimensional matrix with longitude and latitude data.

lower_interval

Lower interval.

upper_interval

Upper interval.

raster_templete

A raster template.

niter

Number of iterations to obtain the distribution.

return_null_dfield

If TRUE the null distribution of dispersal field will be returned.

randal

Randomization algorithm applied to the PAM. Possible choices "curveball","fastball" and "indep_swap".

parallel

If TRUE the computations will be performed in parallel.

n_cores

Number of cores for the parallel computation.

Details

For more information about the biodiversity indices see Soberon and Cavner (2015). For detail about the diversity range analysis see Soberon et al. (2022). To plot diversity range results use plot method for objects of class diversity_range.

For details about randomization algorithms applied to the PAM see null_dispersion_field_distribution.

Value

An object of class diversity_range. The main result is the diversity range analysis which shows jointly two indices describing the community composition of every cell in the grid: (1) the relative number of species, and (2) the mean dispersion field (see plot method for plot (Soberon et al. 2022). The contains 12 slots with different measurements of biodiversity such as alpha diversity (species richness in each site or pixel), omega (size of the area of distribution of each species), dispersion field (the standardized size of the area of distribution of all species occurring in each pixel).

Author(s)

Luis Osorio-Olvera & Jorge Soberón

References

Soberón J, Cobos ME, Nuñez-Penichet C (2021). “Visualizing species richness and site similarity from presence-absence matrices.” Biodiversity Informatics, 16(1), 20–27. doi:10.17161/bi.v16i1.14782, https://journals.ku.edu/jbi/article/view/14782..

Soberon J, Cavner J (2015). “Indices of Biodiversity Pattern Based on Presence-Absence Matrices: A GIS Implementation.” Biodiversity Informatics, 10, 22–34..

Examples

set.seed(111)
pam <- matrix(rbinom(10000,1,0.5),nrow = 100,ncol = 1000)
rdivan <- bamm::diversity_range_analysis(pam=pam,
                                         parallel = FALSE,
                                         niter = 10,
                                         return_null_dfield=TRUE)
bamm::plot(rdivan,plot_type="diversity_range")
# Lagomorphos

lagos_path <- system.file("extdata/conejos",
                          package = "bamm")
enm_path <- list.files(lagos_path,
                       pattern = ".tif",
                       full.names = TRUE)
en_models <- raster::stack(enm_path) >0.01
nonas <- which(!is.na(en_models[[1]][]))
xy_mat <- sp::coordinates(en_models[[1]])[ nonas,]
pam <- bamm::models2pam(en_models,sparse=FALSE)

rdivan <- bamm::diversity_range_analysis(pam=pam,
                                         xy_mat=xy_mat,
                                         raster_templete = en_models[[1]],
                                         parallel=TRUE,
                                         n_cores=2,
                                         return_null_dfield=TRUE)
bamm::plot(rdivan,plot_type="diversity_range")
bamm::plot(rdivan,plot_type="diversity_range_map")
if(require(plotly) && require(crosstalk)){
#bamm::plot(rdivan,plot_type="diversity_range_interactive")
}