Generate SIS Connect files for multiple plant species

The aim of LCr is to speed up the process of adding Least Concern (LC) species to the IUCN Red List. We’ll start with a list of plant species and end up with a zip file of CSV files that contain the minimal required information to support a Least Concern assessment. The zip file can then be uploaded into the IUCN Species Informaiton System (SIS) via SIS Connect (requires registration) where the draft assessments can be edited, reviewed and hopefully published on the IUCN Red List in due course. Spatial data to support the assessment is also generated by LCr.

Before you start, make sure you have the rWCVP package installed along with the associated data package rWCVPdata. For more information see the Getting Started guide.

Load the LCr library:

library(LCr)
library(dplyr)
library(rWCVP)
remotes::install_github('matildabrown/rWCVPdata')

Get name keys from a species list

The first step is to determine the list of LC species that you want to document. A study predicted extinction risk for all species of flowering plants (Angiosperms). You can filter on species that are confidently predicted to be LC, or manually generate a data frame from a list as shown below.

Note that predictions can be wrong so please verify that your selected species are genuine LC species. A useful resource to help determine whether your species is LC is to check whether it has previously been assessed. The ThreatSearch resource maintained by BGCI contains evidence-based plant conservation assessments compiled from digital resources including national/regional Red Lists.

lc_species <-
  data.frame(sp = c(
    "Crabbea acaulis", "Crabbea cirsioides", "Crabbea nana", "Crabbea velutina"
  ))
print(lc_species)
#>                   sp
#> 1    Crabbea acaulis
#> 2 Crabbea cirsioides
#> 3       Crabbea nana
#> 4   Crabbea velutina

Next we can reconcile the names against other data sources using existing packages such as rWCVP and rgbif. This will provide us with name keys that can be used to search for data on those species.

In this case we want to enforce a single matching name for every name in our list so we set the match parameter to ‘single’, but you can set this to ‘multiple’ if you wish to allow multiple matches. Similarly, we want to focus only on accepted species, so we set the tax_status parameter to ‘accepted’, but you can set this to ‘any’ if you wish to return a different taxonomic status.

lc_keys <-
  get_name_keys(
    df = lc_species,
    name_column = "sp",
    tax_status = "accepted",
    match = "single"
  )

# take a look at the resulting table
glimpse(lc_keys)

The name matching went well. I’ve checked the authors and I’m happy that the names match to the correct concept for these taxa. Note that it is worth spending time with the name matching step to ensure you are using a consistent concept for the taxa you are working with across GBIF and WCVP. Adjust the tax_status and match parameters for more fuzzy searching.

We now have some useful fields to help us find more data for these species. The GBIF_usageKey is an identifier for species according to the GBIF name backbone and the wcvp_ipni_id identifies the species according to the WCVP name backbone.

Generate an LC point file

Use the make_LC_points function to kick off a download from GBIF, clean the downloaded occurences, and reformat them to IUCN spatial standards. Name information is required for the standard point file. Set the range_check parameter to TRUE if you want to exclude points outside the native range according to WCVP, otherwise standard cleaning protocols are applied using the CoordinateCleaner package.

Note that we will use the rGBIF package to obtain the occurrence data. You will need to set up your GBIF credentials to obtain the downloads. After you have set up an account at GBIF you need to register your credentials in the r environment - see this post for an explanation.

The IUCN spatial point standards require some fields relating to the data compiler and associated institution. These can be manually entered as below.

lc_sis_occs <-
  make_LC_points(
    keys_df = lc_keys,
    first_name = "Steven",
    second_name = "Bachman",
    institution = "Royal Botanic Gardens, Kew",
    range_check = TRUE
  )

# save the file - you'll need to submit this to IUCN
library(readr)
write_csv(lc_points$points, "lc_points_fungi.csv")

Some occurrence records were removed as they did not pass the cleaning tests or were outside the native range. Note that the output from make_LC_points will likely be a large list with two or three objects: a data frame of occurrences called points, a data frame called citation that holds the citation details for this download, and if you set range_check = TRUE it will also return the native ranges as a data frame. The GBIF citation is important to retain as we need to cite the use of this data in the Red List assessment and we’ll add this to the reference file later on.

Generate LC CSV files

We can now generate the LC csv files. We can use the wcvp_ipni_id field as a unique identifier. We need to add the assessor details again as this is registered in the credits csv file, note that the email address is also required. Now we can add our GBIF citation back in so that it goes in the references csv file. Finally, the native ranges we generated earlier can be used to help define the list of countries that our species are native to.

lc_sis_files <- make_sis_csvs(unique_id = lc_keys$wcvp_ipni_id,
                              wcvp_ipni_id = lc_keys$wcvp_ipni_id,
                              first_name = "Steven",
                              second_name = "Bachman",
                              email = "s.bachman@kew.org",
                              institution = "Royal Botanic Gardens, Kew",
                              gbif_ref = lc_sis_occs$citation,
                              native_ranges = lc_sis_occs$native_ranges
                              )

The final step is to make a ZIP file as this is needed to import the data into SIS Connect. With the lc_sis_files list object we just created we run the make_zip function that saves the zip file to your current working directory.

All done! Send those LC species to the Red list and start working on the next batch.

make_zip(lc_sis_files)