Prepare data

library(impIndicator)

library(b3gbi) # General biodiversity indicators for data cubes
library(dplyr) # Data wrangling
library(knitr) # Nice tables

Process GBIF data from the R environment

Import GBIF data using read.csv(), readr::read_csv(), or readxl::read_excel() based on the data set format.

Here is an example a GBIF occurrences data with the minimum required columns.decimalLatitude, decimalLongitude, species, speciesKey, coordinateUncertaintyInMeters and year

decimalLatitude	decimalLongitude	species	speciesKey	coordinateUncertaintyInMeters	year
-33.47209	26.25137	Acacia mearnsii	2979775	25	2024
-32.34151	19.02159	Acacia mearnsii	2979775	8	2024
-34.56317	19.79653	Acacia longifolia	2978730	5	2024
-34.66322	19.80716	Acacia cyclops	2980425	NA	2024
-34.38089	19.22371	Acacia longifolia	2978730	15	2024
-33.01186	18.36404	Acacia saligna	2978552	4	2024
-33.68421	18.70806	Acacia saligna	2978552	15	2024
-34.33013	18.99397	Acacia longifolia	2978730	4	2024
-26.19055	28.11916	Acacia mearnsii	2979775	9	2024
-34.42525	19.86027	Acacia cyclops	2980425	15	2024

The region of the study has to be given as a shapefile of the study area or a character representing the country of study area. An example is:

southAfrica_sf
#> Simple feature collection with 1 feature and 0 fields
#> Geometry type: MULTIPOLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 16.48333 ymin: -34.822 xmax: 32.89043 ymax: -22.13639
#> Geodetic CRS:  WGS 84
#>                         geometry
#> 1 MULTIPOLYGON (((31.2975 -22...

acacia_cube <- taxa_cube(
  taxa = taxa_Acacia,
  region = southAfrica_sf,
  first_year = 2010,
  last_year = 2023
)

The cube is a sim_cube object. Below is an example of the acacia taxa in South Africa:

# View processed cube
acacia_cube
#>
#> Simulated data cube for calculating biodiversity indicators
#>
#> Date Range: 2010 - 2023
#> Number of cells: 385
#> Grid reference system: custom
#> Coordinate range:
#>      xmin      xmax      ymin      ymax
#>  16.60833  31.35833 -34.69700 -22.94701
#>
#> Total number of observations: 5663
#> Number of species represented: 28
#> Number of families represented: Data not present
#>
#> Kingdoms represented: Data not present
#>
#> First 10 rows of data (use n = to show more):

Download from GBIF website

The cube can be generated by downloading the GBIF with rgbif::occ_data()

Cube with standard grid

impIndicator works other cubes with standard grid cell, such as, eea and eqdgc which are processed from b3gbi::process_cube(). An example is the mammal_cube in the b3gbi package.

# Load GBIF data cube
cube_name <- system.file("extdata", "denmark_mammals_cube_eqdgc.csv",
                         package = "b3gbi")

# Prepare cube
mammal_cube <- b3gbi::process_cube(cube_name, first_year = 2000)

# View cube
mammal_cube
#>
#> Processed data cube for calculating biodiversity indicators
#>
#> Date Range: 2000 - 2024
#> Single-resolution cube with cell size 0.25degrees
#> Number of cells: 265
#> Grid reference system: eqdgc
#> Coordinate range:
#>   xmin   xmax   ymin   ymax
#>  3.375 15.125 54.375 58.125
#>
#> Total number of observations: 191676
#> Number of species represented: 97
#> Number of families represented: 31
#>
#> Kingdoms represented: Animalia
#>
#> First 10 rows of data (use n = to show more):