Retrieve Taxonomic Data From WoRMS
Source:vignettes/retrieve_worms_data.Rmd
retrieve_worms_data.RmdWoRMS
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
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 = c("Phytoplankton"),
verbose = FALSE)Match Taxa Names
Taxon names can be matched with the WoRMS API to retrieve Aphia IDs
and corresponding taxonomic information. The
get_worms_records_name() 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$scientific_name),
size = 10,
replace = TRUE)
# Match taxa names with WoRMS
worms_records <- get_worms_records_name(unique(taxa_names),
fuzzy = TRUE,
best_match_only = TRUE,
marine_only = TRUE,
verbose = FALSE)
# Print result as tibble
tibble(worms_records)## # A tibble: 4 × 28
## name status AphiaID url scientificname authority unacceptreason taxonRankID
## <chr> <chr> <int> <chr> <chr> <chr> <chr> <int>
## 1 Unic… no co… NA NA NA NA NA NA
## 2 Scri… accep… 109545 http… Scrippsiella Balech e… NA 180
## 3 Cyli… accep… 149004 http… Cylindrotheca… (Ehrenbe… NA 220
## 4 Dipl… accep… 109515 http… Diplopsalis R.S.Berg… NA 180
## # ℹ 20 more variables: rank <chr>, valid_AphiaID <int>, valid_name <chr>,
## # valid_authority <chr>, parentNameUsageID <int>, kingdom <chr>,
## # phylum <chr>, class <chr>, order <chr>, family <chr>, genus <chr>,
## # citation <chr>, lsid <chr>, isMarine <int>, isBrackish <int>,
## # isFreshwater <int>, isTerrestrial <int>, isExtinct <lgl>, match_type <chr>,
## # modified <chr>Get WoRMS records from AphiaID
Taxonomic records can also be retrieved using Aphia IDs, employing
the same retry and error-handling logic as the
get_worms_records_name() function.
# Randomly select ten Aphia IDs
aphia_ids <- sample(unique(shark_data$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 as tibble
tibble(worms_records)## # A tibble: 10 × 27
## AphiaID url scientificname authority status unacceptreason taxonRankID
## <int> <chr> <chr> <chr> <chr> <lgl> <int>
## 1 1310442 https://w… Octactis spec… (Ehrenbe… accep… NA 220
## 2 146715 https://w… Aphanothece Nägeli, … accep… NA 180
## 3 837459 https://w… Tripos lineat… (Ehrenbe… accep… NA 220
## 4 134529 https://w… Pyramimonas Schmarda… accep… NA 180
## 5 575737 https://w… Binuclearia l… (Schmidl… accep… NA 220
## 6 110153 https://w… Heterocapsa t… (Ehrenbe… unacc… NA 220
## 7 148899 https://w… Bacillariophy… Haeckel,… accep… NA 60
## 8 106287 https://w… Hemiselmis Parke, 1… accep… NA 180
## 9 249711 https://w… Desmodesmus (R.Choda… accep… NA 180
## 10 109553 https://w… Protoperidini… Bergh, 1… accep… NA 180
## # ℹ 20 more variables: rank <chr>, valid_AphiaID <int>, valid_name <chr>,
## # valid_authority <chr>, parentNameUsageID <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 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 as tibble
tibble(worms_taxonomy)## # A tibble: 10 × 10
## aphia_id scientific_name worms_kingdom worms_phylum worms_class worms_order
## <dbl> <chr> <chr> <chr> <chr> <chr>
## 1 1310442 Octactis speculum Chromista Ochrophyta Dictyochop… Dictyochal…
## 2 146715 Aphanothece Bacteria Cyanobacter… Cyanophyce… Chroococca…
## 3 837459 Tripos lineatus Chromista Myzozoa Dinophyceae Gonyaulaca…
## 4 134529 Pyramimonas Plantae NA Pyramimona… Pyramimona…
## 5 575737 Binuclearia laut… Plantae NA Ulvophyceae Ulotrichal…
## 6 110153 Heterocapsa triq… Chromista Myzozoa Dinophyceae Peridinial…
## 7 148899 Bacillariophyceae Chromista Heterokonto… Bacillario… NA
## 8 106287 Hemiselmis Chromista Cryptophyta Cryptophyc… Pyrenomona…
## 9 249711 Desmodesmus Plantae NA Chlorophyc… Sphaerople…
## 10 109553 Protoperidinium Chromista Myzozoa Dinophyceae Peridinial…
## # ℹ 4 more variables: worms_family <chr>, worms_genus <chr>,
## # worms_species <chr>, worms_hierarchy <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 a few national monitoring stations
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$aphia_id,
verbose = FALSE)
# Print result
tibble(distinct(plankton_groups))## # A tibble: 23 × 2
## scientific_name plankton_group
## <chr> <chr>
## 1 Pauliella taeniata Diatoms
## 2 Amylax triacantha Dinoflagellates
## 3 Aphanocapsa Cyanobacteria
## 4 Aphanothece Cyanobacteria
## 5 Chaetoceros similis Diatoms
## 6 Dinobryon balticum Other
## 7 Dinophysis acuminata Dinoflagellates
## 8 Dinophysis norvegica Dinoflagellates
## 9 Gymnodinium Dinoflagellates
## 10 Protodinium simplex Other
## # ℹ 13 more rows
# Add plankton groups to data and summarize abundance results
plankton_group_sum <- shark_data_subset %>%
mutate(plankton_group = plankton_groups$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))
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:
##
## Markus Lindh, Anders Torstensson (2025). SHARK4R: Retrieving,
## Analyzing, and Validating Marine Data from SHARK and Nordic
## Microalgae. R package version 0.1.7.9000.
## https://doi.org/10.5281/zenodo.14169399
##
## A BibTeX entry for LaTeX users is
##
## @Manual{,
## title = {SHARK4R: Retrieving, Analyzing, and Validating Marine Data from SHARK and Nordic Microalgae},
## author = {Markus Lindh and Anders Torstensson},
## year = {2025},
## note = {R package version 0.1.7.9000},
## url = {https://doi.org/10.5281/zenodo.14169399},
## }