library(impIndicator)
library(b3gbi)
#> Error in get(paste0(generic, ".", class), envir = get_method_env()) :
#> object 'type_sum.accel' not found
library(sf)
#> Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.4.0; sf_use_s2() is TRUEProcess GBIF data from the R environment
- import GBIF data using
read.csv(),readr::read_csv(), orreadxl::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.
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,
res = 0.25,
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: 372
#> Grid reference system: custom
#> Coordinate range:
#> [1] 16.60833
#>
#> Total number of observations: 5491
#> 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):
#>
#> # A tibble: 5,491 × 8
#> scientificName taxonKey minCoordinateUncerta…¹ year cellCode xcoord ycoord
#> <chr> <dbl> <dbl> <dbl> <chr> <dbl> <dbl>
#> 1 Acacia mearnsii 2979775 8 2010 1376 30.4 -29.7
#> 2 Acacia saligna 2978552 1 2010 206 18.4 -33.9
#> 3 Acacia implexa 2979232 1 2010 206 18.4 -33.9
#> 4 Acacia pycnantha 2978604 1 2010 206 18.4 -33.9
#> 5 Acacia cyclops 2980425 122 2010 668 18.4 -32.2
#> 6 Acacia mearnsii 2979775 1 2010 215 20.6 -33.9
#> 7 Acacia mearnsii 2979775 110 2010 215 20.6 -33.9
#> 8 Acacia saligna 2978552 1 2011 206 18.4 -33.9
#> 9 Acacia saligna 2978552 1 2011 144 19.4 -34.2
#> 10 Acacia melanoxy… 2979000 1 2011 206 18.4 -33.9
#> # ℹ 5,481 more rows
#> # ℹ abbreviated name: ¹minCoordinateUncertaintyInMeters
#> # ℹ 1 more variable: obs <dbl>Download from GBIF website
The cube can be generated by downloading the GBIF with
rgbif::occ_data()
Cube with stardard 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 <- 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 ^2
#> 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):
#>
#> # A tibble: 28,155 × 15
#> year cellCode kingdomKey kingdom familyKey family taxonKey scientificName
#> <dbl> <chr> <dbl> <chr> <dbl> <chr> <dbl> <chr>
#> 1 2000 E006N56DB 1 Animalia 9680 Odoben… 5218819 Odobenus rosm…
#> 2 2000 E008N54BA 1 Animalia 5310 Phocid… 2434793 Phoca vitulina
#> 3 2000 E008N55AA 1 Animalia 5310 Phocid… 2434793 Phoca vitulina
#> 4 2000 E008N55AB 1 Animalia 5307 Mustel… 2433753 Lutra lutra
#> 5 2000 E008N55AB 1 Animalia 5510 Muridae 7429082 Mus musculus
#> 6 2000 E008N55AC 1 Animalia 5307 Mustel… 2433753 Lutra lutra
#> 7 2000 E008N55AC 1 Animalia 5310 Phocid… 2434793 Phoca vitulina
#> 8 2000 E008N55AC 1 Animalia 5310 Phocid… 2434806 Halichoerus g…
#> 9 2000 E008N55AC 1 Animalia 9701 Canidae 5219243 Vulpes vulpes
#> 10 2000 E008N55AC 1 Animalia 9379 Lepori… 7952072 Lepus europae…
#> # ℹ 28,145 more rows
#> # ℹ 7 more variables: obs <dbl>, minCoordinateUncertaintyInMeters <dbl>,
#> # minTemporalUncertainty <dbl>, familyCount <dbl>, xcoord <dbl>,
#> # ycoord <dbl>, resolution <chr>