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Rapid LC - fungi workflow

Source: vignettes/SIS_connect_fungi_workflow.Rmd
SIS_connect_fungi_workflow.Rmd

The following workflow demonstrates how to search and clean open data on fungi species in order to test whether they are likely to be evaluated as Least Concern. With selected LC species, you can then generate CSV files with the minimal required information to support IUCN Red List Least Concern assessments.

The LCr and rCAT packages are not on CRAN, but can be installed via the github repository:

# install LCr and rCAT2 from github
remotes::install_github("stevenpbachman/LCr")

# load relevant libraries 
library(LCr)
library(dplyr)
library(rCAT)

Load in a list of names

Start by providing a dataframe with your taxon list.

# dataframe of species you want to run through LCr
lc_species <-
  data.frame(sp = c(
    "Morchella americana Clowez & Matherly", 
    "Hericium americanum Ginns", 
    "Lactarius badiosanguineus Kühner & Romagn"
  ))

Match names against GBIF to get identifiers

Run the get_name_keys function using your dataframe of names as input. This will check the names against the GBIF names backbone. The name_column is the column that contains the taxa to be assessed. This can either contain a binomial or a binomial and author combined. Including the author with the binomial will help achieve a better name match.

By default the GBIF matching will select the match with highest confidence, but you can also set this to fuzzy match using match = "any", which may provide multiple matches.

# get the GBIF identification key
lc_keys <-
  LCr::get_name_keys(
    df = lc_species,
    name_column = "sp",
    match = "single", 
    kingdom = "fungi"
  )

Let’s take a look at the output:

dplyr::glimpse(lc_keys)

Clean keys

LCr is designed to work at species level and where taxonomic status is accepted. In this case, all names matched the GBIF backbone and were accepted, but we can run clean_keys anyway.

# remove any problematic records
lc_keys_clean <- LCr::clean_keys(lc_keys)

Get GBIF occurrence data

Now with a clean set of names and identification keys we can query GBIF to obtain occurrence data.

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. You may need to restart R after updating the R environment.

The get_gbif_occs function has two modes - search or download. Search mode uses rgbif::occ_search and provides a fast, exploratory search. Use the download mode when you want to generate a citable DOI for your assesment, accessed viagbif_occs$citation.

# get the raw GBIF occs - with timer
start_time <- Sys.time()
gbif_occs <- LCr::get_gbif_occs(lc_keys_clean, mode = "search")
end_time <- Sys.time()
check <-  end_time - start_time
print(check)

Data quality checks

We can now run some tests, mostly based on the geospatial information, and flag records that are potentially problematic and should be removed. As we don’t currently have a dataset for native ranges for fungi we ignore the native_ranges and buffer parameters of the check_occs function. The output will be two objects, the flagged data and a summary of the flagged records.

# run the occurrence quality checks
checked_occs <- LCr::check_occs(gbif_occs$points)

# flagged occurrences and summary table
flagged_occs <- checked_occs$checked_data
flagged_sum <- checked_occs$summary

Now we can clean the data using the clean_occs function with the default settings, meaning all flagged occurrences are removed. We then create objects containing valid data (cleaned) and problem data (flagged as problematic). You can save these data for your records.

# Clean using all available flags
cleaned_result <- LCr::clean_occs(flagged_occs)

# Create objects for clean and problem records
valid_data <- cleaned_result$clean_occs
problem_data <- cleaned_result$problem_occs

# For reference you may want to save these down as csv files
write.csv(valid_data, file = "valid_occs.csv", row.names = FALSE)
write.csv(problem_data, file = "problem_occs.csv", row.names = FALSE)

Run the Least Concern tests

Now we have a clean dataset that is ready for the Least Concern test. Depending on the data available, either 4 or 5 metrics (Number of WGSRPD regions for plants only) are generated and checked against the following default thresholds:

  • Extent of occurrence (EOO) > 30000
  • Area of occupancy > 3000
  • Number of cleaned records>75
  • Number of WGSRPD regions >5
  • Number of recent (<30 years) occurrence records >50

The core parameters (EOO and AOO) must both be above the thresholds and at least 2 of 3 remaining parameters (number of cleaned points, regions, recent records) must also be above the thresholds.

# check EOO, AOO, number of records and number of recent records
lc_test <- LCr::make_metrics(valid_data, keys = lc_keys_clean)

Now we can see which species are estimated to be LC using the leastconcern column and can filter on all those estimated to be LC. However, it should be noted that the user has full control over which species they want to designate as Least Concern. Other predictive methods to estimate LC such as Bachman et al. (2024) can be used instead of this simple test.

# filter on LC species
lc_final <- lc_test %>% dplyr::filter(leastconcern == "TRUE")

Generate the SIS CSV files

Now we have the final set of species and can proceed with generating the IUCN standard point file and the SIS Connect CSV files.

Note: Remember that the valid occurrence data was generated and cleaned based on the full species list, but we have now reduced that list to only the species we think are LC. When we generate the IUCN standard point file we need to make sure that we filter the occurrence data by the chosen LC species.

You will also need to define some parameters for the point file and CSV files:

# define parameters - the person who is assessing the species
first_name <- "Steven"
second_name <- "Bachman"
email = "s.bachman@kew.org"
institution = "Royal Botanic Gardens, Kew"

# filter the valid occurrence data on the LC species
valid_data_LC <- valid_data %>%
  filter(taxonKey %in% lc_final$GBIF_usageKey)

# generate the point file
sis_point_file <- LCr::make_sis_occs(valid_data_LC,
                                     first_name = first_name,
                                     second_name = second_name,
                                     institution = institution)

# write down the SIS point file
write.csv(sis_point_file, 
          file = "SIS_points.csv", 
          row.names = FALSE, 
          fileEncoding = "UTF-8",
          na = "")

The make_sis_occs function includes some validation checks so may report issues with the file that will need to be resolved. In this case, write down the file, fix errors, import the file and rerun the make_sis_occs function. Note that when writing the final SIS point file we specify UTF-8 encoding and convert NA values to blanks (na = "") in order to ensure the point file is compliant with IUCN standards.

We can now run the make_sis_csvs function to automatically generate the CSV files

One final step is to generate ‘native ranges’. We don’t have native ranges for fungi in the same we have for plants where species’ ranges are recorded using the World Geographic Scheme for Recording Plant Distributions (WGSRPD). However, we can generate the same WGSRPD regions based on where our cleaned occurrence records are found. We can then use this as the basis for making the list of IUCN land regions as used in the countries.csv file.

# get WGSRPD regions based on points
native_ranges <- get_occs_range(sis_point_file)

# get SIS files
lc_sis_files <- LCr::make_sis_csvs(unique_id = lc_final$GBIF_usageKey,
                                   first_name = "Steven",
                                   second_name = "Bachman",
                                   email = "s.bachman@kew.org",
                                   institution = "Royal Botanic Gardens, Kew",
                                   family = lc_final$GBIF_family,
                                   genus = lc_final$GBIF_genus,
                                   species = lc_final$GBIF_species,
                                   gbif_ref = gbif_occs$citation,
                                   taxonomicAuthority = lc_final$GBIF_authorship,
                                   occs = sis_point_file,
                                   native_ranges = native_ranges,
                                   kingdom = "fungi"
)

And finally, we zip them up ready to be sent to SIS Connect

# final step - make the zip file
make_zip(lc_sis_files)

Register for SIS Connect and then log in: https://connect.iucnredlist.org/

Upload the ZIP file and wait for the automated validation to complete. This will take a few minutes depending on the number of species in your file. You should receive an email from noreply@iucnredlist.org alerting you when the validation is complete.

Open the working set in SIS and check for any issues. You can manually fix issues by opening up the CSV files and editing them directly and saving them as a zip file again. When you are happy that the files are ready you can select the option to submit to the Red List unit. This should be followed up with an email to Craig Hilton-Taylor.

References

Bachman, S.P., Brown, M.J.M., Leão, T.C.C., Nic Lughadha, E. & Walker, B.E. (2024). Extinction risk predictions for the world’s flowering plants to support their conservation. New Phytologist. https://doi.org/10.1111/nph.19592