This document describes how we map the checklist data to Darwin Core. The source file for this document can be found here.

1 Setup

Load libraries:

library(tidyverse)      # To do data science
library(magrittr)       # To use %<>% pipes
library(here)           # To find files
library(janitor)        # To clean input data
library(readxl)         # To read Excel files
library(digest)         # To generate hashes
library(rgbif)          # To use GBIF services

Set file paths (all paths should be relative to this script):

# Raw files:
raw_data_file = "../data/raw/checklist-of-rusts.tsv"

# Processed files:
dwc_taxon_file = "../data/processed/taxon.csv"
dwc_distribution_file = "../data/processed/distribution.csv"
dwc_relationship_file = "../data/processed/resourcerelationship.csv"

2 Read and pre-process raw data

Create a data frame raw_data from the source data:

# Read the source data:
raw_data <- read_delim(raw_data_file, delim = "\t") 

Clean the data somewhat: remove empty rows if present

raw_data %<>% remove_empty_rows()     # Remove empty rows

2.1 Extract host plant information

The checklist includes information about rust fungi (scientificName) and their associated host plants (hostPlant). Although the publication of belgian rust fungi is our central focus, it is also informative to include the the host taxa in the taxon core. For this, we need to extract the host plant information from hostPlant in raw data and we add them to the checklist under scientificName.

Extract the scientific names from all species in raw_data (including the scientific names of the rust fungi as an intermediate step. We need this information later for the mapping of the Resource Relationship extension, see paragraph 5):

host_plant <- raw_data %>% select(scientificName, hostPlant)

Split hostPlant on scientific name, using “,” as a separator:

host_plant %<>% separate(
  sep = ", ", 
  into = c(paste("species", c(1:20), sep = "_")),
  fill = "right")

Gather all host species in one row (omitting “NA”):

host_plant %<>% gather("interaction", "scientificName_host", starts_with("species"), na.rm = TRUE)

Remove leading whitespaces:

host_plant %<>% mutate(scientificName_host = str_trim(scientificName_host))

This dataframe gives a nice structured overview of all interactions between the host plant and the parasite. We clean it and we save the information in a new dataframe interactions, as we will need this to construct the Resource Relationship extension (see paragraph 5):

interactions <- host_plant %<>% select(-interaction) %<>% 
                                         rename("scientificName_parasite" = "scientificName")

Select unique scientificNames:

host_plant %<>% distinct(scientificName_host) %<>% 

Rename scientificName_host to enable binding by rows:

host_plant %<>% rename(scientificName = scientificName_host)

Add a column resourceRelation to raw_data and host_plant to indicate whether the species in scientificNameis a parasite or a host:

raw_data %<>% mutate(resourceRelation = "parasite")
host_plant %<>% mutate(resourceRelation = "host")

Bind raw_data and host_plant by rows:

raw_data %<>% bind_rows(host_plant)

2.2 Generate taxonID

To uniquely identify a taxon in the Taxon Core and reference taxa in the Extensions, we need a taxonID. Since we need it in all generated files, we generate it here in the raw data frame. It is a combination of dataset-shortname:taxon: and a hash based on the scientific name. As long as the scientific name doesn’t change, the ID will be stable:

# Vectorize the digest function (The digest() function isn't vectorized. So if you pass in a vector, you get one value for the whole vector rather than a digest for each element of the vector):
vdigest <- Vectorize(digest)

# Generate taxonID:
raw_data %<>% mutate(taxon_id = paste("uredinales-belgium-checklist", "taxon", vdigest(scientificName, algo="md5"), sep=":"))

Preview data:


2.3 Add bibliographicCitation

This checklist is a compilation of three published volumes of the Catalogue des Uredinales de Belgique (The rust fungi of Belgium). For each rust fungus, a volume number (part) and a page number (page) is provided. This provides a concise description of the species, most of the information is about taxonomy, observations and the hosts. We need to integrate these citations in both the taxon core and the extensions. So, we add bibliographicCitation to raw_data.

First, we complement the volume number with the full reference (given in the metadata). For this, we create the data frame full_reference:

To join both data frames, raw_part in full_reference should be an integer:

full_reference %<>% mutate(part = as.integer(part))

Join both data frames:

raw_data <- left_join(raw_data, full_reference, by = "part")

For each rust fungus taxon, the bibliographicCitation will contain the full reference with page number. For all host species, we refer to “Vanderweyen, A., & Fraiture, A. (2009, 2012) Catalogue des Uredinales de Belgique” (excluding volumne and page number). This is because the complete reference (with volumne and page number) applies to the rust fungus, not the host plant.

raw_data %<>% mutate(bibliographicCitation = case_when(
  resourceRelation == "parasite" ~  paste(full_reference, "Page:", page),
  resourceRelation == "host"     ~ "Vanderweyen A & Fraiture A (2009 & 2012) Catalogue des Uredinales de Belgique. Lejeunia, Revue de Botanique."))

Remove full_reference:

raw_data %<>% select(-full_reference)

2.4 Further pre-processing:

Add prefix raw_ to all column names in raw_data to avoid name clashes with Darwin Core terms:

colnames(raw_data) <- paste0("raw_", colnames(raw_data))

Preview data:

raw_data %>% head()

3 Create taxon core

taxon <- raw_data

3.1 generic names

raw_scientificName includes information about the genus, specific epithet, infraspecific epithet, (bracket)authorship and taxonRank. The full scientific name will be mapped under scientificName in the taxon core. For enhanced readability, we parse raw_scientificName into its different components using the parsenames() function from rgbif.

parsed_names <- parsenames(taxon $ raw_scientificName)

Overview of the different compenents:


Select the columns needed for further mapping in the taxon core, i.e. genusorabove, specificepithet, authorship, bracketauthorship, rankmarker and infraspecificepithet (these will be mapped to, respectively, genus, specificEpithet, scientificNameAuthorship, scientificNameAuthorship, taxonRank and infraspecificEpithet). We also select scientificname as we need this to merge parsed_names with scientificName in the Taxon Core.

parsed_names %<>% select(scientificname, genusorabove, specificepithet, authorship, bracketauthorship, rankmarker, infraspecificepithet)

The parsenames() function from rgbif does not identify all taxonomic ranks. We need to manually add all taxonomic ranks for which the rankmarker was left empty:

parsed_names %>% select(scientificname, rankmarker) %>% 

Almost all these taxa are genera, only one taxon (Salix fragilis x Salix pendandra) is a hybrid. We manually add these taxonomic ranks to parsed_names:

parsed_names %<>% mutate(rankmarker = case_when( & scientificname != "Salix fragilis x Salix pentandra" ~ "genus",
  scientificname == "Salix fragilis x Salix pentandra" ~ "hybrid",
  TRUE ~ rankmarker))

Some columns contain NA’s. We replace these by blanks:

parsed_names %<>% replace_na(list(
  specificepithet      = "",   
  infraspecificepithet = "",
  authorship           = "",
  bracketauthorship    = ""))

Add the prefix pn_ to the column names of parsed_names (to remove these columns after the mapping process):

colnames(parsed_names) <- paste0("pn_", colnames(parsed_names))

Amount of species in the Taxon Core before merge:

## [1] 755

Merge parsed_names with the Taxon Core:

taxon <- inner_join(taxon, parsed_names, by = c("raw_scientificName" = "pn_scientificname"))

Is the amount of species in the Taxon Core the same as before the merge (should be TRUE)?

## [1] TRUE

3.2 Term mapping

Map the source data to Darwin Core Taxon:

3.2.1 language

taxon %<>% mutate(language = "en")

3.2.2 license

taxon %<>% mutate(license = "")

3.2.3 rightsHolder

taxon %<>% mutate(rightsHolder = "Botanical Garden Meise")

3.2.4 bibliographicCitation

taxon %<>% mutate(bibliographicCitation = raw_bibliographicCitation) 

3.2.5 institutionID

taxon %<>% mutate(institutionID = "")

3.2.6 datasetID

taxon %<>% mutate(datasetID = "")

3.2.7 datasetName

taxon %<>% mutate(datasetName = "Catalogue of the Rust Fungi of Belgium")

3.2.8 taxonID

taxon %<>% mutate(taxonID = raw_taxon_id)

3.2.9 scientificName

taxon %<>% mutate(scientificName = raw_scientificName)

3.2.10 kingdom

taxon %<>% mutate(kingdom = case_when(
  raw_resourceRelation == "parasite" ~ "Fungi",
  raw_resourceRelation == "host"     ~ "Plantae"))

3.2.11 phylum

taxon %<>% mutate(phylum = case_when(
  raw_resourceRelation == "parasite" ~ "Basidiomycota",
  raw_resourceRelation == "host"     ~ ""))

3.2.12 order

taxon %<>% mutate(order = case_when(
  raw_resourceRelation == "parasite" ~ "Uredinales",
  raw_resourceRelation == "host"     ~ ""))

3.2.13 family

taxon %<>% mutate(family = raw_family) %<>% replace_na(replace = list(family = "")) #' Remove NA's for host species

3.2.14 genus

taxon %<>% mutate(genus = pn_genusorabove) %<>% replace_na(replace = list(genus = "")) #' Remove NA's for host species

3.2.15 specificEpithet

taxon %<>% mutate(specificEpithet = pn_specificepithet) %<>% replace_na(replace = list(specificEpithet = "")) #' Remove NA's for host species

3.2.16 infraspecificEpithet

taxon %<>% mutate(infraspecificEpithet = pn_infraspecificepithet) %<>% replace_na(replace = list(infraspecificEpithet = "")) #' Remove NA's for host species

3.2.17 taxonRank

Information for taxonRank is contained in pn_rankmarker:

taxon %>% distinct(pn_rankmarker)

Generate taxonRankby recoding abbreviations:

taxon %<>% mutate(taxonRank = recode(pn_rankmarker,
  "sp." = "species",
  "var." = "variety",
  "subsp." = "subspecies",
  "fam." = "family",
  "f." = "form"))

3.2.18 scientificNameAuthorship

The format specifications for scientificNameAuthorship is given here.The mapping is as follows: * no (bracket)authorship provided: scientificNameAuthorship is left blank. * only authorship is provided, no information on bracketauthorship: we copy values in pn_authorship. * both authorship and bracketauthorship is provided: the required format here is (bracketauthorship) authorship.

taxon %<>% mutate(scientificNameAuthorship = case_when(
  pn_bracketauthorship != "" & pn_authorship != "" ~ paste( paste0("(", pn_bracketauthorship, ")"), pn_authorship, sep =" "),
  pn_bracketauthorship == "" & pn_authorship != "" ~ pn_authorship,
  TRUE ~ ""))

3.2.19 nomenclaturalCode

taxon %<>% mutate(nomenclaturalCode = "ICN")

3.3 Post-processing

Remove the original columns:

taxon %<>% select(-starts_with("raw_"), -starts_with("pn_"))

Preview data:


Save to CSV:

write_csv(taxon, dwc_taxon_file, na = "")

4 Create distribution extension

Map the data to Species Distribution.

4.1 Pre-processing

The distribution applies to rust fungi only. We remove the host plants from raw_data and create distribution:

distribution <- raw_data %>% filter(raw_resourceRelation == "parasite")

4.2 Term mapping

4.2.1 taxonID

distribution %<>% mutate(taxonID = raw_taxon_id)

4.2.2 locationID

distribution %<>% mutate(locationID = "ISO_3166-2:BE")

4.2.3 locality

distribution %<>% mutate(locality = "Belgium")

4.2.4 countryCode

distribution %<>% mutate(countryCode = "BE")

4.2.5 occurrenceStatus

Information for occurrenceStatus is contained in raw_occurrenceStatus:

distribution %>% distinct(raw_occurrenceStatus)

These values are conform the IUCN definitions.

distribution %<>% mutate(occurrenceStatus = raw_occurrenceStatus)

4.2.6 establishmentMeans

establishmentMeans information is contained in raw_establishmentMeans:

distribution %>% distinct(raw_establishmentMeans)

alien is not part of the establishmentMeans vocabulary. Here, we use introduced:

distribution %<>% mutate(establishmentMeans = recode(raw_establishmentMeans,
  "alien" = "introduced",
  .missing = ""))

4.2.7 eventDate

eventDate information is contained in raw_from (date of first observation) and raw_to (date of last observation). We will map this information as a ISO 8601 date range (start_date / end_date). For this, we need to clean and merge the information in raw_from and raw_to.

raw_from contains the information for the first observation. At this point, the following date formats are given: dd.m.yyyy, m.yyyy, m/yyyy and yyyy, with the information for the days and years expressed as arabic numericals and for the months as arabic or roman numericals. We need to split raw_from to convert the roman numericals.

distribution %<>% separate(raw_from, 
                           into = c("day", "month", "year"),
                           remove = FALSE,
                           extra = "merge",
                           fill = "left")     

Convert roman to arabic numericals in month:

distribution %<>% mutate(month = recode(month,
    "I"   = "01",
    "II"  = "02",
    "III" = "03",
    "IV"  = "04",
    "V"   = "05",
    "VI"  = "06",
    "VII" = "07",
    "VIII"= "08",
    "IX"  = "09",
    "X"   = "10",
    "XI"  = "11",
    "XII" = "12"))

We remerge the year, month and day information for the date of first observation and express this in a ISO 8601 format. we need to add the leading zeros to months (using sprintf), we combine year, month and date information with “-” (using paste) and we strip the trailing “-NA” if day or month is “NA” (using gsub):

distribution %<>% mutate(start_date = 
  gsub("-NA", "",
             sprintf("%02d", as.integer(month)),
             sprintf("%02d", as.integer(day)),
             sep = "-")))

Compare the first records of start_date with raw_from:

distribution %>% select(raw_from, start_date) %>% head(n = 20)

remove day, month and year:

distribution %<>% select(-day, -month, -year)

The information for the date of the last observation is contained in raw_to. The same principles and steps can be repeated for the mapping of raw_to into end_date

Separate raw_to into day, month and year:

distribution %<>% separate(raw_to, 
                           into = c("day", "month", "year"),
                           remove = FALSE,
                           extra = "merge",
                           fill = "left")     

Convert roman to arabic numericals in month:

distribution %<>% mutate(month = recode(month,
    "I"   = "01",
    "II"  = "02",
    "III" = "03",
    "IV"  = "04",
    "V"   = "05",
    "VI"  = "06",
    "VII" = "07",
    "VIII"= "08",
    "IX"  = "09",
    "X"   = "10",
    "XI"  = "11",
    "XII" = "12"))

we need add the leading zeros to months (using sprintf), we combine year, month and date information with “-” (using paste) and we strip the trailing “-NA” if day or month is “NA” (using gsub):

distribution %<>% mutate(end_date = 
  gsub("-NA", "",
             sprintf("%02d", as.integer(month)),
             sprintf("%02d", as.integer(day)),
             sep = "-")))

Compare the first records of end_date with raw_from:

distribution %>% select(raw_to, end_date) %>% head(n = 20)

remove day, month and year:

distribution %<>% select(-day, -month, -year)

Merge start_date and end_date in eventDate (ISO 8601). When only one date is provided (start_date OR end_date), we copy the date for both start_date and end_date. This to emphasize that a taxon was detected exclusively at that particular time point:

distribution %<>% mutate(eventDate = case_when(
  start_date == "NA" & end_date == "NA" ~ "",
  start_date == "NA" & end_date != "NA" ~ paste(end_date, end_date, sep = "/"),
  start_date != "NA" & end_date == "NA" ~ paste(start_date, start_date, sep = "/"),
  TRUE ~ paste(start_date, end_date, sep = "/")))

Remove “/NA” or “NA/NA”:

distribution %<>% mutate(eventDate = gsub("([/][N][A]|([NA]{2}[/][NA]{2}))", "", eventDate))

4.2.8 source

distribution %<>% mutate(source = raw_bibliographicCitation)

4.3 Post-processing

Remove the original columns:

distribution %<>% select(-starts_with("raw_"), -start_date, -end_date)

Preview data:


Save to CSV:

write_csv(distribution, dwc_distribution_file, na = "")

5 Create resource relationship extension

resource_relationship <- raw_data

5.1 Term mapping

Map the data to Resource Relationship.

In this extension, we map all interactions between the rust fungi and host plants, with one line representing a one-to-one taxon interaction. These interactions are saved in interactions. We add this one-to-one taxon interactions to resource_relationship:

resource_relationship %<>% right_join(interactions, by = c("raw_scientificName" = "scientificName_parasite"))

5.1.1 resourceID

This Darwin Core term refers to the taxonID of the object of the relationship. This is the taxonID of the host plants. By joining resource_relationship with interactions on the the scientificName of the parasite, the taxonID’s of the host plants were removed from the resource_relationship. We need to extract them from raw_data:

taxonID_host <- raw_data %>% filter(raw_resourceRelation == "host") %>% 
  select(raw_scientificName, raw_taxon_id)

Join resource_relationship with taxonID_host:

resource_relationship %<>% left_join(taxonID_host, by = c("scientificName_host" = "raw_scientificName"))

Rename raw_taxon_id.y:

resource_relationship %<>% rename("resourceID" = "raw_taxon_id.y")

5.1.2 relatedResourceID

This Darwin Core term refers to the taxonID of the subject of the relationship. This is the taxonID of the rust fungi.

resource_relationship %<>% mutate(relatedResourceID = raw_taxon_id.x)  

5.1.3 taxonID

The taxonID refers to the resourceID, which is the taxonID of the fungi:

resource_relationship %<>% mutate(taxonID = relatedResourceID) 

5.1.4 relationshipOfResource

resource_relationship %<>% mutate(relationshipOfResource = "parasite of")

5.1.5 relationshipAccordingTo

resource_relationship %<>% mutate(relationshipAccordingTo = raw_bibliographicCitation)

5.2 Post-processing:

Remove the original columns:

resource_relationship %<>% select(-starts_with("raw_"), -scientificName_host)

Move taxonID to the first column:

resource_relationship %<>% select(taxonID, everything())

Preview data:


Save to CSV:

write_csv(resource_relationship, dwc_relationship_file, na = "")