Accessing AFLIBER data

Download AFLIBER data

All the data versions are openly accessible here: Dryad Digital Repository

Different versions include at least 4 separate files, namely:

  • Species list file. Incorporates accepted list of taxa, their taxonomic classification, information about ther endemic status and more useful information. -
  • Distributions file. Occurrence records in a “long” format. The “References” column includes numeric references for the data sources citing the row’s species (in Taxon column) at the given grid cell (in the UTM10x10 one). Numeric references match those in the Data sources table.
  • Data sources file. Includes origins of distribution data, including a numeric reference (matching Distributions’ file), a text reference and a proper citation. May include also the number of occurrences incorporated to the dataset.
  • Eliminations file. Shows a record of distribution records included in the original data sources which have been identified as erroneous (manually or automatically).

Access AFLIBER information via Dryad API

library(rdryad)
library(tidyverse)

afliber_dryad <- rdryad::dryad_download("10.5061/dryad.gmsbcc2kv")# specify AFLIBER's DOI
downloadpath <- dirname(unlist(afliber_dryad)[1]) #Local download folder
list.files(downloadpath) # See downloaded files
[1] "AFLIBER_Distributions.csv"                       
[2] "AFLIBER_Species_list.csv"                        
[3] "AFLIBER_Supplement_3_Disregarded_occurrences.csv"
[4] "AFLIBER_Supplement_4b-AFLIBER_DataSources.csv"   
[5] "README.txt"                                      
checklist <- read.csv(unlist(afliber_dryad)[2])
distributions <- read.csv(unlist(afliber_dryad)[1])

message("Species list structure:")
Species list structure:
glimpse(checklist)
Rows: 6,456
Columns: 11
$ Taxon           <chr> "Abies alba", "Abies pinsapo", "Acer campestre", "Acer…
$ Scientific_Name <chr> "Abies alba Mill.", "Abies pinsapo Boiss.", "Acer camp…
$ Endemic         <lgl> FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,…
$ Genus           <chr> "Abies", "Abies", "Acer", "Acer", "Acer", "Acer", "Ace…
$ Species         <chr> "alba", "pinsapo", "campestre", "monspessulanum", "opa…
$ Subspecies      <chr> "", "", "", "monspessulanum", "granatense", "opalus", …
$ Class           <chr> "Pinopsida", "Pinopsida", "Magnoliopsida", "Magnoliops…
$ Order           <chr> "Pinales", "Pinales", "Sapindales", "Sapindales", "Sap…
$ Family          <chr> "Pinaceae", "Pinaceae", "Sapindaceae", "Sapindaceae", …
$ GBIF_id         <chr> "2685484", "2685464", "3189863", "7344624", "8013467",…
$ POW_Name        <chr> "Abies alba", "Abies pinsapo", "Acer campestre", "Acer…
message("Distribution file structure:")
Distribution file structure:
glimpse(distributions)
Rows: 1,824,549
Columns: 3
$ Taxon      <chr> "Abies alba", "Abies alba", "Abies alba", "Abies alba", "Ab…
$ UTM.cell   <chr> "30TXM89", "30TXM99", "30TXN27", "30TXN32", "30TXN33", "30T…
$ References <chr> "999", "999", "6", "999", "999", "114_999", "114", "6_12_11…

Querying AFLIBER data

Filter datasets by taxonomic group

As an example, here we will filter the dataset just to include species of the genus Nepeta.

checklist <- read_csv(unlist(afliber_dryad)[2], show_col_types = F)
distributions <- read_csv(unlist(afliber_dryad)[1], show_col_types = F)

taxa <- checklist |> 
  filter(Genus == "Nepeta") |> 
  pull(Taxon) 

Nepeta_AFLIBER <- distributions |> 
  filter(Taxon %in% taxa)

glimpse(Nepeta_AFLIBER)
Rows: 1,403
Columns: 3
$ Taxon      <chr> "Nepeta apuleji", "Nepeta apuleji", "Nepeta apuleji", "Nepe…
$ UTM.cell   <chr> "29SQA44", "29SQA52", "29SQA61", "30STF44", "30STF45", "30S…
$ References <chr> "114", "999", "999", "114_999", "999", "999", "999", "114",…

Get AFLIBER subset citation bibliography

Using the Nepeta subset, we can retrieve the data sources where our data come from

sources <- read_csv(unlist(afliber_dryad)[4], show_col_types = F,locale = locale(encoding = "Latin1"))

bibliography <- Nepeta_AFLIBER |> 
  separate_longer_delim(References, delim ="_") |> 
  mutate(References = as.numeric(References)) |> 
  left_join(sources, join_by(References == `NUMERIC REFERENCE`)) |> 
  arrange(CITATION) |> 
  pull(CITATION) |> 
  unique()

message(paste0(head(bibliography), collapse = "\n\n")) #Here we plot just the first ones!!
Alonso Felpete, J.I., Gonza?lez Robinson, S., Ferna?ndez Rodri?guez, A., Sanzo Rodri?guez, I., Mora Cabello de Alba, A., Bueno Sa?nchez, A. & Di?az Gonza?lez, T.E. (2011). Cata?logo flori?stico del Parque Nacional Picos de Europa. Jardi?n Bota?nico Atla?ntico de Gijo?n, Ayuntamiento de Gijo?n. ISBN 978-84-615-5846-9.

Atlas de la Flora de Aragón, Instituto Pirenaico de Ecología y Gobierno de Aragón (Departamento de Medio Ambiente) http://floragon.ipe.csic.es/. Last accessed June, 2020.

Banco de Datos de Biodiversidad Conselleria D’agricultura, Desenvolupament Rural, Emergència Climàtica i Transició Ecològica, Comunidad Valenciana. http://www.bdb.gva.es/. Last accessed October, 2019.

Charco, J., Alcaraz, F., Carrillo, F.A. & Rivera, D. (2015). Árboles y arbustos autóctonos de la Región de Murcia. Centro de Investigaciones Ambientales del Mediterráneo, Ciudad Real.

Flora-On: Flora de Portugal Interactiva. Sociedade Portuguesa de Botânica. www.flora-on.pt. Last accessed April, 2020.

IberBal-Flora. Database of the Iberian and Balearic vascular flora. Department of Biology, Autonomous University of Madrid.
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