prepare_base_PAM {biosurvey} R Documentation

## Presence-absence matrix (PAM) linked to a spatial grid

### Description

Prepares a presence-absence matrix (PAM) in which all sites of interest (rows) will have a value for presence or absence of a species of interest (columns). Initial points of interest will be represented by an ID, and longitude and latitude coordinates. The PAM will be linked to a spatial grid.

### Usage

prepare_base_PAM(data, format = NULL, master_matrix, cell_size,
complete_cover = TRUE, clip_grid = FALSE,
indices = "basic", parallel = FALSE, n_cores = NULL,
verbose = TRUE)


### Arguments

 data species geographic ranges to be used to create a presence-absence matrix (PAM). This argument can be: character, data.frame, RasterStack, RasterBrick, list, SpatialPolygonsDataFrame, or SpatialPointsDataFrame. See details for a description of the characteristics of data for each option. format (character) if data is of class character, available options are: "shp", "gpkg", "geojson", "GTiff", and "ascii". master_matrix object of class "master_matrix" or "master_selection". See details. cell_size (numeric) resolution for grid (single number or vector of two numbers) in kilometers (km). complete_cover (logical) whether or not to include cells of grid partially overlapped with the geographic region of interest contained in master_matrix. Default = TRUE. clip_grid (logical) whether to clip the spatial grid using the region of interest. Clipping improves visualization but depending on how complex the region of interest is it could take time to perform this task. indices (character) code for indices to be calculated. Basic indices are calculated all the time, other indices need to be specified. Options are: "all", "basic, "AB", "BW", "BL", "SCSC", "SCSR", "DF", "CC", "WRN", "SRC", "CMSC", and "CMSR". Default = "basic". See details. parallel (logical) whether to perform analyses in parallel. Default = FALSE. Not used if data is of class data.frame, RasterStack, or RasterBrick. n_cores (numeric) number of cores to be used when parallel = TRUE. The default, NULL, uses available cores - 1. verbose (logical) whether or not to print messages about the process. Default = TRUE.

### Details

Objects of class "master_matrix" or "master_selection" can be obtained from functions prepare_master_matrix, random_selection, uniformG_selection, uniformE_selection, or uniformEG_selection. The element region or mask if this last is not NULL is used to prepare the spatial grid.

Geographic projection of objects or coordinates involved must be WGS84 (EPSG:4326).

Description of objects to be used as data:

• character.- name of directory containing raster, shapefiles, geopackage, or geojson files representing species geographic ranges. Each file must be named as the species that it represents. All files must be in the same format. If files are in raster format, "GTiff" and "ascii" are acceptable extensions; values in each layer must be 1 (presence) and 0 (absence).

• data.frame.- a table containing three columns. Columns must be in the following order: Longitude, Latitude, Species.

• RasterStack or RasterBrick.- Each layer must be named as the species which range it represents, and values in each layer must be 1 (presence) and 0 (absence).

• list.- a list of RasterLayers that cannot be stacked because of extent or resolution differences. Each element of the list must be named as the species which range it represents, and values in each RasterLayer must be 1 (presence) and 0 (absence).

• SpatialPolygonsDataFrame.- object representing species' geographic ranges. The data.frame associated with the object must contain a column named "Species" to distinguish among features representing each species range.

• SpatialPointsDataFrame.- object of spatial points where each record of a species must be a point. The associated data.frame must contain the following columns (in that order): Longitude, Latitude, Species.

A list of codes and indices that can be calculated is described below. For further details on the way calculations are performed and the meaning of the indices see Soberon and Cavner (2015) doi: 10.17161/bi.v10i0.4801.

 Code Index Calculation RI Richness Basic RA Range Basic RIN Richness normalized Basic RAN Range normalized Basic AB Additive Beta Needs to be defined BW Beta Whittaker Needs to be defined BL Beta Legendre Needs to be defined and DF SCSC Schluter covariance sites-composition Needs to be defined and CMSC SCSR Schluter covariance species-ranges Needs to be defined and CMSR DF Dispersion field Needs to be defined SCC Shared community composition Needs to be defined WRN Wright-Reeves nestedness Needs to be defined, BW, and DF SRC Stone-Roberts C-score Needs to be defined and DF CMSC Covariance matrix sites-composition Needs to be defined, DF, and BW CMSR Covariance matrix species-ranges Needs to be defined, SCC, and BW MCC Mean composition covariance Calculated with CMSC MRC Mean range covariance Calculated with CMSR

### Value

A presence-absence matrix (PAM) of class base_PAM for the region of interest associated with a SpatialPolygonsDataFrame, as in a grid of cell_size resolution. Each grid cell is related to a specific ID and longitude and latitude coordinates. Presence (1) and absence (0) values for each species in every cell of the PAM are included as apart of the data.frame of the SpatialPolygonsDataFrame. PAM indices is returned with the basic indices of biodiversity as default, but can be changed using the argument indices.

PAM_indices

### Examples

# Data
data("m_matrix", package = "biosurvey")
data("species_data", package = "biosurvey")

# Create base_PAM
b_pam <- prepare_base_PAM(data = species_data, master_matrix = m_matrix,
cell_size = 100)
sp::plot(b_pam$PAM) summary(b_pam$PAM@data[, 1:6])


[Package biosurvey version 0.1.1 Index]