knitr::opts_chunk$set( collapse = TRUE, comment = "#>", warning = FALSE, message = FALSE, eval = FALSE, fig.width = 8, fig.height = 6 )
One way of organizing biodiversity data is by using presence-absence matrices (PAMs), where a one represents the presence of species j in cell i, and a zero indicates absence. From a PAM, we can estimate a variety of metrics related to biodiversity patterns, including richness, range size, and composition. For a comprehensive list of biodiversity metrics, refer to the PAM_indices function in the biosurvey package.
Before you begin, use the load_faunabr function to load the data. For more detailed information on obtaining and loading the data, please refer to 1. Getting started with faunabr
my_dir <- file.path(file.path(tempdir(), "faunabr")) dir.create(my_dir) get_faunabr(output_dir = my_dir, #directory to save the data data_version = "latest", #get the most recent version available overwrite = T) #Overwrite data, if it exists
library(faunabr) library(terra) #Folder where you stored the data with the function get_faunabr() #Load data bf <- load_faunabr(data_dir = my_dir, data_version = "latest", type = "short") #short version #> Loading version 1.3
The fauna_pam() function facilitates the utilization of species distribution information in Fauna do Brazil to generate a PAM. Each site represents a brazilian state or a country. In addition to the PAM, the function also provides a summary and a SpatVector containing the number of species in each site.
As an example, lets obtain a PAM consisting of all mammal species natives to Brazil:
#Select native species of mammals with confirmed occurrence in Brazil br_mammals <- select_fauna(data = bf, include_subspecies = FALSE, phylum = "all", class = "Mammalia", order = "all", family = "all", genus = "all", lifeForm = "all", filter_lifeForm = "in", habitat = "all", filter_habitat = "in", states = "all", filter_states = "in", country = "BR", filter_country = "in", origin = "all", taxonomicStatus = "valid") #Get presence-absence matrix in states and countries pam_mammals <- fauna_pam(data = br_mammals, by_state = TRUE, by_country = FALSE, remove_empty_sites = TRUE, return_richness_summary = TRUE, return_spatial_richness = TRUE, return_plot = TRUE) #Visualize (as tibble) the PAM for the first 5 species and 7 sites tibble::tibble(pam_mammals$PAM[1:7, 1:5]) #> # A tibble: 7 × 5 #> states `Platyrrhinus aurarius` `Kannabateomys amblyonyx` `Callicebus lucifer` `Cerradomys maracajuensis` #> <fct> <dbl> <dbl> <dbl> <dbl> #> 1 AM 1 0 1 0 #> 2 ES 0 1 0 0 #> 3 MG 0 1 0 1 #> 4 PR 0 1 0 0 #> 5 RJ 0 1 0 0 #> 6 RS 0 1 0 0 #> 7 SC 0 1 0 0
Since return_richness_summary is set to TRUE, the function also returns a data frame containing the number of species per site.
#Visualize (as tibble) the richness summary table tibble::tibble(pam_mammals$Richness_summary[1:7,]) #> # A tibble: 7 × 3 #> states richness #> <fct> <dbl> #> 1 AM 225 #> 2 ES 120 #> 3 MG 188 #> 4 PR 116 #> 5 RJ 133 #> 6 RS 105 #> 7 SC 101
If return_spatial_richness is set to TRUE, the function will return a SpatVector containing the number of species per site. Additionally, when return_plot is also set to TRUE, the function returns a plot.
knitr::include_graphics("vignettes_img/IMG05.png")
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