Retrieve Taxonomic Data From WoRMS

WoRMS

The World Register of Marine Species (WoRMS) is a comprehensive database providing authoritative lists of marine organism names, managed by taxonomic experts. It combines data from the Aphia database and other sources like AlgaeBase and FishBase, offering species names, higher classifications, and additional data. WoRMS is continuously updated and maintained by taxonomists. In this tutorial, we source the R package worrms to access WoRMS data for our function. Please note that the authors of SHARK4R are not affiliated with WoRMS.

Getting Started

Installation

You can install the latest version of SHARK4R from CRAN using:

install.packages("SHARK4R")

Load the SHARK4R, dplyr and ggplot2 libraries:

library(SHARK4R)
library(dplyr)
library(ggplot2)

Retrieve Data Using SHARK4R

Retrieve Phytoplankton Data From SHARK

Phytoplankton data, including scientific names and AphiaIDs, are downloaded from SHARK. To see more download options, please visit the Retrieve Data From SHARK tutorial.

# Retrieve all phytoplankton data from April 2015
shark_data <- get_shark_data(fromYear = 2015,
                             toYear = 2015,
                             months = 4,
                             dataTypes = "Phytoplankton",
                             verbose = FALSE)

Match Taxa Names

Taxon names can be matched with the WoRMS API to retrieve Aphia IDs and corresponding taxonomic information. The match_worms_taxa() function incorporates retry logic to handle temporary failures, ensuring that all names are processed successfully.

# Find taxa without Aphia ID
no_aphia_id <- shark_data %>%
  filter(is.na(aphia_id))

# Randomly select taxa with missing aphia_id
taxa_names <- sample(unique(no_aphia_id$scientific_name),
                     size = 10,
                     replace = TRUE)

# Match taxa names with WoRMS
worms_records <- match_worms_taxa(unique(taxa_names),
                                  fuzzy = TRUE,
                                  best_match_only = TRUE,
                                  marine_only = TRUE,
                                  verbose = FALSE)

# Print result
print(worms_records)
## # A tibble: 4 × 29
##   name  AphiaID url   scientificname authority status unacceptreason taxonRankID
##   <chr>   <int> <chr> <chr>          <chr>     <chr>  <chr>                <int>
## 1 Dipl…  109515 http… Diplopsalis    R.S.Berg… accep… <NA>                   180
## 2 Scri…  109545 http… Scrippsiella   Balech e… accep… <NA>                   180
## 3 Cyli…  149004 http… Cylindrotheca… (Ehrenbe… accep… <NA>                   220
## 4 Unic…      NA <NA>  <NA>           <NA>      no co… <NA>                    NA
## # ℹ 21 more variables: rank <chr>, valid_AphiaID <int>, valid_name <chr>,
## #   valid_authority <chr>, parentNameUsageID <int>, originalNameUsageID <int>,
## #   kingdom <chr>, phylum <chr>, class <chr>, order <chr>, family <chr>,
## #   genus <chr>, citation <chr>, lsid <chr>, isMarine <int>, isBrackish <int>,
## #   isFreshwater <int>, isTerrestrial <int>, isExtinct <lgl>, match_type <chr>,
## #   modified <chr>

Get WoRMS records from AphiaID

Taxonomic records can also be retrieved using Aphia IDs, employing the same retry and error-handling logic as the match_worms_taxa() function.

# Randomly select ten Aphia IDs
aphia_ids <- sample(unique(shark_data$aphia_id),
                    size = 10)

# Remove NAs
aphia_ids <- aphia_ids[!is.na(aphia_ids)]

# Retrieve records
worms_records <- get_worms_records(aphia_ids,
                                   verbose = FALSE)

# Print result
print(worms_records)
## # A tibble: 10 × 28
##    AphiaID url        scientificname authority status unacceptreason taxonRankID
##      <int> <chr>      <chr>          <chr>     <chr>  <lgl>                <int>
##  1  106287 https://w… Hemiselmis     Parke, 1… accep… NA                     180
##  2  248149 https://w… Pseudopedinel… Skuja, 1… accep… NA                     220
##  3   17657 https://w… Eutreptiella   A.M.Cunh… accep… NA                     180
##  4  110156 https://w… Peridiniella … (Levande… accep… NA                     220
##  5  110257 https://w… Protoperidini… (Jørgens… accep… NA                     220
##  6  146545 https://w… Merismopedia   Meyen, 1… accep… NA                     180
##  7  149168 https://w… Proboscia ala… (Brightw… unass… NA                     220
##  8  160553 https://w… Dinobryon fac… (Willén)… accep… NA                     220
##  9  148899 https://w… Bacillariophy… Haeckel,… accep… NA                      60
## 10 1310442 https://w… Octactis spec… (Ehrenbe… accep… NA                     220
## # ℹ 21 more variables: rank <chr>, valid_AphiaID <int>, valid_name <chr>,
## #   valid_authority <chr>, parentNameUsageID <int>, originalNameUsageID <int>,
## #   kingdom <chr>, phylum <chr>, class <chr>, order <chr>, family <chr>,
## #   genus <chr>, citation <chr>, lsid <chr>, isMarine <int>, isBrackish <int>,
## #   isFreshwater <int>, isTerrestrial <int>, isExtinct <int>, match_type <chr>,
## #   modified <chr>

Get WoRMS Taxonomy

SHARK sources taxonomic information from Dyntaxa, which is reflected in columns starting with taxon_xxxxx. Equivalent columns based on WoRMS can be retrieved using the add_worms_taxonomy() function.

# Retrieve taxonomic table
worms_taxonomy <- add_worms_taxonomy(aphia_ids,
                                     verbose = FALSE)

# Print result
print(worms_taxonomy)

# Enrich SHARK data with taxonomic data from WoRMS
shark_data_with_worms <- shark_data %>%
  left_join(worms_taxonomy, by = "aphia_id")
## # A tibble: 10 × 10
##    aphia_id worms_scientific_name    worms_kingdom worms_phylum     worms_class 
##       <dbl> <chr>                    <chr>         <chr>            <chr>       
##  1   106287 Hemiselmis               Chromista     Cryptophyta      Cryptophyce…
##  2   248149 Pseudopedinella elastica Chromista     Ochrophyta       Dictyochoph…
##  3    17657 Eutreptiella             Protozoa      Euglenozoa       Euglenophyc…
##  4   110156 Peridiniella catenata    Chromista     Myzozoa          Dinophyceae 
##  5   110257 Protoperidinium steinii  Chromista     Myzozoa          Dinophyceae 
##  6   146545 Merismopedia             Bacteria      Cyanobacteria    Cyanophyceae
##  7   149168 Proboscia alata          Chromista     Heterokontophyta Bacillariop…
##  8   160553 Dinobryon faculiferum    Chromista     Ochrophyta       Chrysophyce…
##  9   148899 Bacillariophyceae        Chromista     Heterokontophyta Bacillariop…
## 10  1310442 Octactis speculum        Chromista     Ochrophyta       Dictyochoph…
## # ℹ 5 more variables: worms_order <chr>, worms_family <chr>, worms_genus <chr>,
## #   worms_species <chr>, worms_hierarchy <chr>

Retrieve WoRMS Taxonomic Hierarchies

To explore the full hierarchical taxonomy records of your Aphia IDs, you can use the get_worms_taxonomy_tree() function. This function retrieves records for the entire taxonomic tree from WoRMS, including parent-child relationships, and can optionally fetch all descendants (e.g. species) under a genus or known synonyms.

# Retrieve taxonomic tree
worms_tree <- get_worms_taxonomy_tree(
  aphia_ids[1],                # use first id only in this example
  add_descendants = FALSE,     # only retrieve hierarchy for given AphiaIDs
  add_synonyms = FALSE,        # do not retrieve synonyms
  verbose = FALSE              # suppress progress messages
)

# Print result
print(worms_tree)
## # A tibble: 7 × 28
##   AphiaID url   scientificname authority status unacceptreason taxonRankID rank 
##     <int> <chr> <chr>          <chr>     <chr>  <lgl>                <int> <chr>
## 1       7 http… Chromista      <NA>      accep… NA                      10 King…
## 2  582418 http… Hacrobia       <NA>      accep… NA                      20 Subk…
## 3   17638 http… Cryptophyta    Cavalier… accep… NA                      30 Phyl…
## 4   17639 http… Cryptophyceae  Fritsch,… accep… NA                      60 Class
## 5   20997 http… Pyrenomonadal… G. Novar… accep… NA                     100 Order
## 6   20998 http… Chroomonadace… Clay, Ku… accep… NA                     140 Fami…
## 7  106287 http… Hemiselmis     Parke, 1… accep… NA                     180 Genus
## # ℹ 20 more variables: valid_AphiaID <int>, valid_name <chr>,
## #   valid_authority <chr>, parentNameUsageID <int>, originalNameUsageID <lgl>,
## #   kingdom <chr>, phylum <chr>, class <chr>, order <chr>, family <chr>,
## #   genus <chr>, citation <chr>, lsid <chr>, isMarine <int>, isBrackish <int>,
## #   isFreshwater <int>, isTerrestrial <int>, isExtinct <lgl>, match_type <chr>,
## #   modified <chr>

Assign Phytoplankton Groups

Phytoplankton data are often categorized into major groups such as Dinoflagellates, Diatoms, Cyanobacteria, and Others. This grouping can be achieved by referencing information from WoRMS and assigning taxa to these groups based on their taxonomic classification, as demonstrated in the example below.

# Subset data from one national monitoring station
nat_stations <- shark_data %>%
  filter(station_name %in% c("BY5 BORNHOLMSDJ"))

# Randomly select one sample from the nat_stations
sample <- sample(unique(nat_stations$shark_sample_id_md5), 1)

# Subset the random sample
shark_data_subset <- shark_data %>%
  filter(shark_sample_id_md5 == sample)

# Assign groups by providing both scientific name and Aphia ID
plankton_groups <- assign_phytoplankton_group(
  scientific_names = shark_data_subset$scientific_name,
  aphia_ids = shark_data_subset$aphia_id,
  verbose = FALSE)

# Print result
distinct(plankton_groups)

# Add plankton groups to data and summarize abundance results
plankton_group_sum <- shark_data_subset %>%
  mutate(plankton_group = plankton_groups$plankton_group) %>%
  filter(parameter == "Abundance") %>%
  group_by(plankton_group) %>%
  summarise(sum_plankton_groups = sum(value, na.rm = TRUE))

# Plot a pie chart
ggplot(plankton_group_sum,
       aes(x = "", y = sum_plankton_groups, fill = plankton_group)) +
  geom_col(width = 1) +
  coord_polar(theta = "y") +
  labs(
    title = "Phytoplankton Groups",
    subtitle = paste(unique(shark_data_subset$station_name),
                     unique(shark_data_subset$sample_date)),
    fill = "Plankton Group"
  ) +
  theme_void() +
  theme(plot.background = element_rect(fill = "white", color = NA))
## # A tibble: 23 × 2
##    scientific_name      plankton_group 
##    <chr>                <chr>          
##  1 Pauliella taeniata   Diatoms        
##  2 Amylax triacantha    Dinoflagellates
##  3 Aphanocapsa          Cyanobacteria  
##  4 Aphanothece          Cyanobacteria  
##  5 Chaetoceros similis  Diatoms        
##  6 Dinobryon balticum   Other          
##  7 Dinophysis acuminata Dinoflagellates
##  8 Dinophysis norvegica Dinoflagellates
##  9 Gymnodinium          Dinoflagellates
## 10 Protodinium simplex  Other          
## # ℹ 13 more rows

Assign Custom Phytoplankton Groups

You can add custom plankton groups by using the custom_groups parameter, allowing flexibility to categorize plankton based on specific taxonomic criteria. Please note that the order of the list matters: taxa are assigned to the last matching group. For example: Mesodinium rubrum will be excluded from the Ciliates group because it appears after Ciliates in the list in the example below.

# Define custom plankton groups using a named list
custom_groups <- list(
  "Cryptophytes" = list(class = "Cryptophyceae"),
  "Green Algae" = list(class = c("Trebouxiophyceae",
                                 "Chlorophyceae",
                                 "Pyramimonadophyceae"),
                       phylum = "Chlorophyta"),
  "Ciliates" = list(phylum = "Ciliophora"),
  "Mesodinium rubrum" = list(scientific_name = "Mesodinium rubrum"),
  "Dinophysis" = list(genus = "Dinophysis")
)

# Assign groups by providing scientific name only, and adding custom groups
plankton_groups <- assign_phytoplankton_group(
  scientific_names = shark_data_subset$scientific_name,
  custom_groups = custom_groups,
  verbose = FALSE)

# Add new plankton groups to data and summarize abundance results
plankton_custom_group_sum <- shark_data_subset %>%
  mutate(plankton_group = plankton_groups$plankton_group) %>%
  filter(parameter == "Abundance") %>%
  group_by(plankton_group) %>%
  summarise(sum_plankton_groups = sum(value, na.rm = TRUE))

# Plot a new pie chart, including the custom groups
ggplot(plankton_custom_group_sum,
       aes(x = "", y = sum_plankton_groups, fill = plankton_group)) +
  geom_col(width = 1) +
  coord_polar(theta = "y") +
  labs(
    title = "Phytoplankton Custom Groups",
    subtitle = paste(unique(shark_data_subset$station_name),
                     unique(shark_data_subset$sample_date)),
    fill = "Plankton Group"
  ) +
  theme_void() +
  theme(plot.background = element_rect(fill = "white", color = NA))

Citation

## To cite package 'SHARK4R' in publications use:
## 
##   Lindh, M. and Torstensson, A. (2026). SHARK4R: Accessing and
##   Validating Marine Environmental Data from 'SHARK' and Related
##   Databases. R package version 1.1.1.
##   https://CRAN.R-project.org/package=SHARK4R
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {SHARK4R: Accessing and Validating Marine Environmental Data from 'SHARK' and Related Databases},
##     author = {Markus Lindh and Anders Torstensson},
##     year = {2026},
##     note = {R package version 1.1.1},
##     url = {https://CRAN.R-project.org/package=SHARK4R},
##   }