GBIF biodiversity data from R – more functions

October 8, 2012

(This article was first published on Recology - R, and kindly contributed to R-bloggers)

We have been working on an R package to get GBIF data from R, with the stable version available through CRAN here, and the development version available on GitHub here.

We had a Google Summer of code stuent work on the package this summer – you can see his work on the package over at his GitHub page here. We have added some new functionality since his work, and would like to show it off.

Lets install rgbif first.

# install_github('rgbif', 'ropensci') # uncomment if not already installed

Get taxonomic information on a specific taxon or taxa in GBIF by their taxon concept keys.

(keys <- taxonsearch(scientificname = "Puma concolor"))  # many matches to this search
 [1] "51780668" "51758018" "50010499" "51773067" "51078815" "51798065"
 [7] "51088007" "50410780" "50305290" "51791438"
taxonget(keys[[1]])  # let's get the first one - the first row in the data.frame is the one we searched for (51780668)
                    sciname gbifkeys       rank
1             Puma concolor 51780668    species
2                      Puma 51780667      genus
3                   Felidae 51780651     family
4                 Carnivora 51780613      order
5                  Mammalia 51780547      class
6                  Chordata 51775774     phylum
7                  Animalia 51775773    kingdom
8 Puma concolor californica 51780669 subspecies
9   Puma concolor improcera 51780670 subspecies

The occurrencedensity function was renamed to densitylist because it is in the density API service, not the occurrence API service. You can use densitylist to get a data.frame of total occurrence counts by one-degree cell for a single taxon, country, dataset, data publisher or data network. Just a quick reminder of what the function can do:

head(densitylist(originisocountrycode = "CA"))
  cellid minLatitude maxLatitude minLongitude maxLongitude count
1  46913          40          41          -67          -66    44
2  46914          40          41          -66          -65   907
3  46915          40          41          -65          -64   510
4  46916          40          41          -64          -63   645
5  46917          40          41          -63          -62    56
6  46918          40          41          -62          -61   143

Using a related function, density_spplist, you can get a species list by one-degree cell as well.

# Get a species list by cell, choosing one at random
density_spplist(originisocountrycode = "CO", spplist = "random")[1:10]
 [1] "Globigerina ciperoensis angustiumbilicata"
 [2] "Globigerina ciperoensis ciperoensis"      
 [3] "Globigerina fringa"                       
 [4] "Globigerina nepenthes"                    
 [5] "Globigerina rohri"                        
 [6] "Globigerina selli"                        
 [7] "Globigerina triloculinoides"              
 [8] "Globigerina venezuelana"                  
 [9] "Globigerinatella insueta"                 
[10] "Globigerinita dissimilis"                 
# density_spplist(originisocountrycode = 'CO', spplist = 'r') # can
# abbreviate the `spplist` argument

# Get a species list by cell, choosing the one with the greatest no. of
# records
density_spplist(originisocountrycode = "CO", spplist = "great")[1:10]  # great is abbreviated from `greatest`
 [1] "Acanthaceae Juss."                
 [2] "Accipitridae sp."                 
 [3] "Accipitriformes/Falconiformes sp."
 [4] "Apodidae sp."                     
 [5] "Apodidae sp. (large swift sp.)"   
 [6] "Apodidae sp. (small swift sp.)"   
 [7] "Arctiinae"                        
 [8] "Asteraceae Bercht. & J. Presl"    
 [9] "Asteraceae sp. 1"                 
[10] "Asteraceae sp. 6"                 
# Can also get a data.frame with counts instead of the species list
density_spplist(originisocountrycode = "CO", spplist = "great", listcount = "counts")[1:10, 
                              names_ count
1                  Acanthaceae Juss.     2
2                   Accipitridae sp.     6
3  Accipitriformes/Falconiformes sp.     2
4                       Apodidae sp.     5
5     Apodidae sp. (large swift sp.)     8
6     Apodidae sp. (small swift sp.)     5
7                          Arctiinae     7
8      Asteraceae Bercht. & J. Presl     2
9                   Asteraceae sp. 1     6
10                  Asteraceae sp. 6    10

You can now map point results, from fxns occurrencelist and those from densitylist, which plots them as points or as tiles, respectively. Point map, using output from occurrencelist.

out <- occurrencelist(scientificname = "Puma concolor", coordinatestatus = TRUE, 
    maxresults = 100, latlongdf = T)
gbifmap(input = out)  # make a map, plotting on world map


Point map, using output from occurrencelist, with many species plotted as different colors

splist <- c("Accipiter erythronemius", "Junco hyemalis", "Aix sponsa", "Buteo regalis")
out <- lapply(splist, function(x) occurrencelist(x, coordinatestatus = T, maxresults = 100, 
    latlongdf = T))


Tile map, using output from densitylist, using results in Canada only.

out2 <- densitylist(originisocountrycode = "CA")  # data for Canada
gbifmap(out2)  # on world map


gbifmap(out2, "Canada")  # on Canada map


Get the .Rmd file used to create this post at my github account – or .md file.

Written in Markdown, with help from knitr.

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