Step 1: Assembling occurrence records and biome schemes

The four-step biomes workflow

biomes follows a four-step workflow, mirrored by these four vignettes and by Figure 1 of the companion paper:

Assembling occurrence records and biome schemes (this vignette).
Choosing a biome scheme.
Occurrences-to-biome classification.
Output and visualisation.

Throughout, we use the packaged example dataset biomes_example so you can run everything without a download.

Terms. A biome scheme is one of the 31 published classification systems. A biome class is a category within a scheme (e.g. savanna). A biome scheme number (1-31) identifies a scheme; it is the value you pass to the scheme argument of the classification and visualisation functions.

1. Occurrence records

Every downstream function works on a table of occurrence records, one row per record, with a longitude and a latitude column in decimal degrees, WGS84 (EPSG:4326). You may also pass an sf object or a terra::SpatVector.

Column	Required	Notes
longitude	yes	numeric, decimal degrees, WGS84. Default name `decimalLongitude`.
latitude	yes	numeric, decimal degrees, WGS84. Default name `decimalLatitude`.
`species`	for species counts	needed only to count species per biome class (Step 4).
anything else	no	carried through untouched.

If your columns are named differently, pass their names via lon and lat:

biomes_rank(occ, lon = "decimallongitude", lat = "decimallatitude")

The packaged example set:

data(biomes_example)
nrow(biomes_example)
#> [1] 29104
head(biomes_example)
#> # A tibble: 6 × 5
#>   genus    species          countryCode decimalLongitude decimalLatitude
#>   <chr>    <chr>            <chr>                  <dbl>           <dbl>
#> 1 Felis    Felis catus      US                     -74.6           40.6 
#> 2 Felis    Felis catus      US                     -74.6           40.6 
#> 3 Acinonyx Acinonyx jubatus KE                      35.5           -1.23
#> 4 Lynx     Lynx rufus       US                    -111.            32.3 
#> 5 Lynx     Lynx rufus       US                     -81.6           38.4 
#> 6 Panthera Panthera leo     KE                      35.4           -1.37

If you do not already have a dataset, biomes_occ() can download and clean one from GBIF for a taxon (needs the rgbif / CoordinateCleaner packages and a network connection):

occ <- biomes_occ(taxon = "Fagus sylvatica")

2. The 31 biome schemes

biomes_get() returns the packaged raster stack: 31 biome schemes at 10 × 10 km, globally.

schemes <- biomes_get()
schemes
#> class       : SpatRaster
#> size        : 1800, 3600, 31  (nrow, ncol, nlyr)
#> resolution  : 10000, 10000  (x, y)
#> extent      : -1.8e+07, 1.8e+07, -9000000, 9000000  (xmin, xmax, ymin, ymax)
#> coord. ref. : +proj=moll +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +units=m +no_defs
#> source      : Biomes_Inventory_RasterStack.tif
#> names       : Biome~er_01, Biome~er_02, Biome~er_03, Biome~er_04, Biome~er_05, Biome~er_06, ...
#> min values  :           1,           1,           1,           1,           1,           1, ...
#> max values  :          21,          98,          30,          20,          15,          14, ...

Each layer of the stack matches one row of biomes_information, in the same order. Use it (or the human-readable biomes_info()) to see which publication and methodology a scheme comes from:

data(biomes_information)
biomes_information[25, c("publication", "name_of_classification",
                         "scheme_type", "scheme_number")]
#> # A tibble: 1 × 4
#>   publication              name_of_classification      scheme_type scheme_number
#>   <chr>                    <chr>                       <chr>               <dbl>
#> 1 Ramankutty & Foley, 1999 Estimating historical chan… vegetation             25

biomes_info(25)   # readable summary for biome scheme no. 25
#> 
#> Name: Estimating historical changes in global land cover: croplands from 1700 to 1992 (Ramankutty & Foley, 1999)
#> 
#> Biome scheme number: 25
#> 
#> Criteria: Potential natural vegetation
#> 
#> Methodology: Informed classification of remotely sensed land cover
#> 
#> Description: Potential natural vegetation is derived by classifying DISCover land cover data following the Olson Global Ecosystems framework (Olson, 1994).
#> 
#> Number of biome classes: 12 (12/0)
#> 
#> Biome classes (raster value: name):
#>      1: Tropical evergreen woodland
#>      2: Tropical deciduous woodland
#>      3: Savanna
#>      4: Dense shrubland
#>      5: Desert and barren
#>      6: Open shrubland
#>      7: Grassland and steppe
#>      8: Temperate evergreen woodland
#>      9: Temperate deciduous woodland
#>     10: Mixed woodland
#>     11: Tundra
#>     12: Boreal woodland
#> 
#> -----

Scheme numbering. Biome scheme numbers follow the order of the biome inventory of Fischer et al. (2022), i.e. the alphabetical order of the 31 schemes’ original publications. Scheme no. 25, for example, is the vegetation scheme of Ramankutty & Foley (1999).

The class-level lookup (raster value → biome-class name, per scheme) lives in biomes_legend; the classification and visualisation functions use it internally.

You now have (a) occurrence records and (b) the 31 biome schemes and their metadata. Continue with Step 2: Choosing a biome scheme.

The four-step biomes workflow

1. Occurrence records

2. The 31 biome schemes

Next