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.
Loading data
Before you begin, use the load_florabr function to load
the data. For more detailed information on obtaining and loading the
data, please refer to 1. Getting started
with florabr
library(florabr)
library(terra)
#Folder where you stored the data with the function get_florabr()
#Load data
bf <- load_florabr(data_dir = my_dir,
data_version = "Latest_available",
type = "short") #short version
#> Loading version 393.401Getting a presence-absence matrix
The get_pam() function facilitates the utilization of
species distribution information in Brazil to generate a PAM. Each site
represents a Biome, a State, a vegetation type, or any combination of
these. 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, let’s obtain a PAM consisting of all tree species native and endemic to the Amazon biome:
#Load Flora e Funga do Brasil data
data("bf_data")
#Select endemic and native species of trees with occurrence only in Amazon
am_trees <- select_species(data = bf_data,
include_subspecies = FALSE,
include_variety = FALSE,
kingdom = "Plantae",
group = "All", subgroup = "All",
family = "All", genus = "All",
lifeForm = "Tree", filter_lifeForm = "only",
habitat = "All", filter_habitat = "in",
biome = "Amazon",
filter_biome = "only",
state = "All", filter_state = "and",
vegetation = "All",
filter_vegetation = "in",
endemism = "Endemic", origin = "Native",
taxonomicStatus = "Accepted",
nomenclaturalStatus = "All")
#Get presence-absence matrix
pam_am <- get_pam(data = am_trees, by_biome = TRUE, by_state = TRUE,
by_vegetation = 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_am$PAM[1:7, 1:5])
# # A tibble: 7 × 5
# Biome States `Euxylophora paraensis` `Galipea congestiflora` `Hortia longifolia`
# <fct> <fct> <dbl> <dbl> <dbl>
# 1 Amazon AM 1 0 1
# 2 Amazon MA 1 1 0
# 3 Amazon PA 1 1 1
# 4 Amazon TO 1 1 0
# 5 Amazon MT 0 0 1
# 6 Amazon RR 0 0 1
# 7 Amazon AC 0 0 0Since return_richness_summary is set to TRUE, the function also returns a data frame containing the number of species per site.
#Visualize (as tibble) the richness summary table
tibble::tibble(pam_am$Richness_summary[1:7,])
# # A tibble: 7 × 3
# Biome States Richness
# <fct> <fct> <dbl>
# 1 Amazon AM 606
# 2 Amazon MA 34
# 3 Amazon PA 296
# 4 Amazon TO 7
# 5 Amazon MT 63
# 6 Amazon RR 45
# 7 Amazon AC 107If 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. 