Reproducibility in the use of taxonomic information

Published

April 23, 2024

1 Introduction

One aspect of scientific reproducibility that is specific to the biological sciences is how taxonomic information is obtained, used, and reported. In the discussion, we address the topics of using vouchers and the importance of adequately documenting the justification of taxonomic identifications. Here we will see a tool for obtaining and curating taxonomic information in a reproducible manner.

We will use the R package ‘taxize’. By the way, the article describing the package was published in the journal F1000Research, one of the journals we saw at the beginning of the semester is completely open.

Chamberlain and Szöcs (2013).

2 Why ‘taxize’?

There are online databases from which taxonomic information for various biological organisms can be obtained. However, there are advantages to performing these searches programmatically:

  1. it is more efficient if you have to search for many taxa
  2. the search becomes a reproducible part of the workflow

The idea of taxize is to make the extraction and use of taxonomic information easy and reproducible.

Image: Rohan Chakravarty/CC BY-NC-ND 3.0.

3 What does ‘taxize’ do?

‘taxize’ connects with several taxonomic databases and more can be gradually added. This information can be used to carry out common tasks in the research process. For example:

3.1 Resolves Taxonomic Names

If we have a list of specimens, we may want to know if we are using updated names and if the names we have are spelled correctly. We can do this using the Global Names Resolver (GNR) application from the Encyclopedia of Life, through taxize.

As an example, let’s look at occurrence data that I downloaded from GBIF. I downloaded records of birds from the genus Ramphocelus in Costa Rica, from the National Zoological Collection. Perhaps, I am working with or planning to work with these specimens.

The data is here (https://doi.org/10.15468/dl.d8frtc)

and this is an example of the bird:

<

font size=“2”> Ramphocelus sanguinolentus, La Fortuna, Costa Rica

Code 
# read the data
dat <- read.csv(file = "./additional_files/0098054-200613084148143.csv",
    header = T, sep = "\t")

# what are the species in CR?
Ram.names <- levels(dat$species)
Ram.names
NULL

Let’s see which databases I can use to search for the names of my species

Code 
library(taxize)
require(kableExtra)
data.sources <- gnr_datasources()

data.sources[, c(1, 5, 8, 9)]
created_at id refresh_period_days title
2012-07-06T11:36:36Z 1 14 Catalogue of Life Checklist
2012-07-06T11:38:14Z 2 14 Wikispecies
2012-02-09T10:31:13Z 3 14 Integrated Taxonomic Information SystemITIS
2012-02-09T10:47:55Z 4 14 National Center for Biotechnology Information
2012-02-09T11:16:43Z 5 14 Index Fungorum (Species Fungorum)
2012-02-09T11:28:38Z 6 14 GRIN Taxonomy for Plants
2012-02-09T11:32:18Z 7 14 Union 4
2012-02-09T12:08:54Z 8 14 The Interim Register of Marine and Nonmarine Genera
2012-02-09T12:40:45Z 9 14 World Register of Marine Species
2012-02-09T12:55:04Z 10 14 Freebase
2012-02-09T13:01:40Z 11 14 GBIF Backbone Taxonomy
2012-02-09T15:36:33Z 12 14 Encyclopedia of Life
2012-02-09T18:21:08Z 93 14 Passiflora vernacular names
2012-02-09T18:21:09Z 94 14 Inventory of Fish Species in the Wami River Basin
2012-02-09T18:21:09Z 95 14 Pheasant Diversity and Conservation in the Mt. Gaoligonshan Region
2012-02-09T18:21:09Z 96 14 Finding Species
2012-02-09T18:21:10Z 97 14 Birds of Lindi Forests Plantation
2012-02-09T18:21:11Z 98 14 Nemertea
2012-02-09T18:21:12Z 99 14 Kihansi Gorge Amphibian Species Checklist
2012-02-09T18:21:12Z 100 14 Mushroom Observer
2012-02-09T18:21:14Z 101 14 TaxonConcept
2012-02-09T18:21:15Z 102 14 Amphibia and Reptilia of Yunnan
2012-02-09T18:21:17Z 103 14 Common names of Chilean Plants
2012-07-06T11:49:07Z 104 14 Invasive Species of Belgium
2012-02-09T18:21:20Z 105 14 ZooKeys
2012-02-09T18:21:23Z 106 14 COA Wildlife Conservation List
2012-02-09T18:21:25Z 107 14 AskNature
2012-02-09T18:21:31Z 108 14 China: Yunnan, Southern Gaoligongshan, Rapid Biological Inventories Report No. 04
2012-02-09T18:21:34Z 109 14 Native Orchids from Gaoligongshan Mountains, China
2012-02-09T18:21:37Z 110 14 Illinois Wildflowers
2012-02-09T18:21:45Z 112 14 Coleorrhyncha Species File
2012-02-09T18:21:46Z 113 14 /home/dimus/files/dwca/zoological names.zip
2012-02-09T18:21:57Z 114 14 Peces de la zona hidrogeográfica de la Amazonia, Colombia (Spreadsheet)
2012-02-09T18:22:04Z 115 14 Eastern Mediterranean Syllidae
2012-02-09T18:22:06Z 116 14 Gaoligong Shan Medicinal Plants Checklist
2012-02-09T18:22:14Z 117 14 birds_of_tanzania
2012-02-09T18:22:23Z 118 14 AmphibiaWeb
2012-02-09T18:22:38Z 119 14 tanzania_plant_sepecimens
2012-02-09T18:22:45Z 120 14 Papahanaumokuakea Marine National Monument
2012-02-09T18:23:21Z 121 14 Taiwanese IUCN species list
2012-02-09T18:23:27Z 122 14 BioPedia
2012-02-09T18:24:06Z 123 14 AnAge
2012-02-09T18:24:25Z 124 14 Embioptera Species File
2012-02-09T18:24:28Z 125 14 Global Invasive Species Database
2012-02-09T18:24:38Z 126 14 Sendoya S., Fernández F. AAT de hormigas (Hymenoptera: Formicidae) del Neotrópico 1.0 2004 (Spreadsheet)
2012-02-09T18:25:00Z 127 14 Flora of Gaoligong Mountains
2012-02-09T18:25:16Z 128 14 ARKive
2012-02-09T18:25:27Z 129 14 True Fruit Flies (Diptera, Tephritidae) of the Afrotropical Region
2012-02-09T18:25:30Z 130 14 3i - Typhlocybinae Database
2012-02-09T18:26:09Z 131 14 CATE Sphingidae
2012-02-09T18:26:28Z 132 14 ZooBank
2012-02-09T18:26:44Z 133 14 Diatoms
2012-02-09T18:27:14Z 134 14 AntWeb
2012-02-09T18:27:40Z 135 14 Endemic species in Taiwan
2012-02-09T18:28:15Z 136 14 Dermaptera Species File
2012-02-09T18:28:21Z 137 14 Mantodea Species File
2012-02-09T18:28:29Z 138 14 Birds of the World: Recommended English Names
2012-02-09T18:29:01Z 139 14 New Zealand Animalia
2012-02-09T18:30:39Z 140 14 Blattodea Species File
2012-02-09T18:30:57Z 141 14 Plecoptera Species File
2012-02-09T18:31:58Z 143 14 Coreoidea Species File
2012-02-09T18:32:28Z 144 14 Freshwater Animal Diversity Assessment - Normalized export
2012-02-09T18:33:38Z 145 14 Catalogue of Vascular Plant Species of Central and Northeastern Brazil
2012-02-09T18:35:12Z 146 14 Wikipedia in EOL
2012-02-09T18:36:49Z 147 14 Database of Vascular Plants of Canada (VASCAN)
2012-02-09T18:38:13Z 148 14 Phasmida Species File
2012-02-09T18:38:29Z 149 14 OBIS
2012-02-09T18:40:09Z 150 14 USDA NRCS PLANTS Database
2012-02-09T18:42:04Z 151 14 Catalog of Fishes
2012-02-09T18:43:41Z 152 14 Aphid Species File
2012-02-09T18:44:03Z 153 14 The National Checklist of Taiwan
2012-02-09T18:46:06Z 154 14 Psocodea Species File
2012-02-09T18:46:24Z 155 14 FishBase
2012-02-09T18:48:19Z 156 14 3i - Typhlocybinae Database
2012-02-09T18:48:44Z 157 14 Belgian Species List
2012-02-09T18:51:49Z 158 14 EUNIS
2012-02-09T18:58:36Z 159 14 CU*STAR
2012-02-09T19:10:42Z 161 14 Orthoptera Species File
2012-02-09T19:11:37Z 162 14 Bishop Museum
2012-02-09T19:18:20Z 163 14 IUCN Red List of Threatened Species
2012-02-09T19:20:46Z 164 14 BioLib.cz
2012-02-09T19:43:03Z 165 14 Tropicos - Missouri Botanical Garden
2012-02-09T20:05:41Z 166 14 nlbif
2012-02-09T20:36:27Z 167 14 The International Plant Names Index
2012-05-07T13:45:07Z 168 14 Index to Organism Names
2012-05-07T13:50:15Z 169 14 uBio NameBank
2013-05-31T01:17:28Z 170 14 Arctos
2013-12-10T03:02:58Z 171 14 Checklist of Beetles (Coleoptera) of Canada and Alaska. Second Edition.
2014-12-08T11:17:24Z 172 14 The Paleobiology Database
2014-12-08T19:50:56Z 173 14 The Reptile Database
2014-12-09T21:27:18Z 174 14 The Mammal Species of The World
2014-12-11T00:19:59Z 175 14 BirdLife International
2015-03-03T13:48:51Z 176 14 Checklist da Flora de Portugal (Continental, Açores e Madeira)
2016-07-20T11:13:25Z 177 14 FishBase Cache
2016-10-18T20:00:31Z 178 14 Silva
2016-10-19T10:13:10Z 179 14 Open Tree of Life Reference Taxonomy
2016-10-30T00:46:40Z 180 14 iNaturalist Taxonomy
2016-11-03T16:09:05Z 181 14 The Interim Register of Marine and Nonmarine Genera
2017-03-22T15:26:50Z 182 14 Gymno
2020-05-25T02:43:22Z 183 14 Index Animalium by Charles Davies Sherborn
2020-05-25T10:32:16Z 184 14 ASM Mammal Diversity Database
2020-05-27T01:41:11Z 185 14 IOC World Bird List
2020-05-28T00:01:22Z 186 14 MCZbase
2020-05-28T16:50:17Z 187 14 The eBird/Clements Checklist of Birds of the World
2020-05-30T00:58:32Z 188 14 American Ornithological Society
2020-05-31T00:36:35Z 189 14 Howard and Moore Complete Checklist of the Birds of the World
2020-05-31T01:23:24Z 193 14 Myriatrix
2021-03-19T16:48:41Z 194 14 PLAZI treatments
2021-10-21T12:27:48Z 195 14 AlgaeBase
2021-12-28T13:04:34Z 196 14 World Flora Online Plant List 2023-12
2021-12-29T13:29:11Z 197 14 World Checklist of Vascular Plants
2021-12-30T14:32:51Z 198 14 The Leipzig Catalogue of Vascular Plants
2022-01-14T22:16:40Z 200 14 The Terrestrial Parasite Tracker
2022-02-14T15:59:43Z 201 14 ICTV Virus Taxonomy
2022-02-18T21:58:40Z 202 14 Discover Life Bee Species Guide
2023-03-01T22:57:31Z 203 14 MycoBank
2023-03-01T23:38:23Z 204 14 Fungal Names
2023-05-01T16:19:21Z 205 14 Nomenclator Zoologicus
2023-08-22T20:12:02Z 206 14 Ruhoff 1980
2023-10-09T19:23:23Z 207 14 Wikidata
2023-12-06T21:42:18Z 208 14 List of Prokaryotic names with Standing in Nomenclature
2023-12-22T12:47:04Z 209 14 New Zealand Organizm Register

Let’s check if they are spelled correctly

Code 
name.res <- gnr_resolve(sci = Ram.names, data_source_ids = c(3:4))
name.res[, -1]

And what if they weren’t?

Code 
Ram.names2 <- Ram.names
Ram.names2[2] <- "Ramphocelus passerini"
name.res2 <- gnr_resolve(sci = Ram.names2, data_source_ids = c(3:4))
name.res2[, -1]
submitted_name matched_name data_source_title score
Ramphocelus passerini Ramphocelus passerinii Bonaparte, 1831 Integrated Taxonomic Information SystemITIS 0.75
Ramphocelus passerini Ramphocelus passerinii National Center for Biotechnology Information 0.75

3.2 Identifies Synonyms

Let’s search for synonyms for these species

Code 
synonyms(sci_id = Ram.names, db = "itis")

To use some databases, it is necessary to obtain an ‘API key’. This cannot be done automatically with ‘taxize’ but instructions on how to obtain and save the API key for use from R can be obtained. Let’s look at a couple of examples:

Code 
use_tropicos()
use_iucn()
use_entrez()

# for more information
`?`(key_helpers())
`?`(`taxize-authentication`)

3.2.1 Exercise 1

  1. Install the ‘usethis’ package with the command install.packages("usethis")
  2. Obtain the ‘API key’ for a database of your interest
  3. Add this ‘API key’ to your environment in R with the command usethis::edit_r_environ()
  4. Restart R and verify that you have the ‘API key’ using getkey()
  5. If everything went well, you’re ready to use it.

 

3.3 Extracts Taxonomic Classification

We can obtain information about the higher taxonomic classification of our species. If your key is for ‘tropicos’ or ‘entrez’, you can use the respective databases (tropicos and ncbi). For example:

Code 
Ram.class <- classification(Ram.names, db = "ncbi")

Ram.class[[1]]

and if we just want to know the family…

Code 
Ram.fam <- tax_name(sci = Ram.names, get = "family", db = "ncbi")

Ram.fam

3.4 Gets Downstream Names

Perhaps we want to know which or how many are the members of a certain taxonomic group. For example, how many species are in the genus Ramphocelus?

Code 
# separate the genus
genus <- strsplit(Ram.names[1], " ")[[1]][1]

# get the species
Ram.down <- downstream(sci_id = genus, downto = "species", db = "ncbi")
Ram.down[[1]]

3.5 Gets Conservation Status Information

If you have the ‘API key’ for IUCN, you can obtain information about conservation status.

NOTE: The authors of ‘taxize’ warn to use with caution as there may be errors

Code 
Ram.sum <- iucn_summary(Ram.names)
iucn_status(Ram.sum)
get_iucn(Ram.names)

4 Examples of Applications in Reproducible Science

Let’s see some examples of how using tools like ‘taxize’ contributes to more reproducible research.

4.1 Lists of Hosts in Thousands of Communities

Article by Gibb et al. 2020, Nature

From the methods section:

“We compiled animal host–pathogen associations from several source databases, to provide as comprehensive a dataset as possible of zoonotic host species and their pathogens: the Enhanced Infectious Diseases (EID2) database; the Global Mammal Parasite Database v.2.0 (GMPD2) which collates records of parasites of cetartiodactyls, carnivores and primates; a reservoir hosts database; a mammal–virus associations database; and a rodent zoonotic reservoirs database augmented with pathogen data from the Global Infectious Disease and Epidemiology Network (GIDEON) (Supplementary Table 8). We harmonized species names across all databases, excluding instances in which either hosts or pathogens could not be classified to species level. To prevent erroneous matches due to misspelling or taxonomic revision, all host species synonyms were accessed from Catalogue Of Life using ‘taxize’ v.0.8.939. Combined, the dataset contained 20,382 associations between 3,883 animal host species and 5,694 pathogen species.”

Let’s see the code from the article and make a small modification to apply the function to our data.

Code 
# taxize/GBIFr
require(taxize)
require(rgbif)

# function to find and resolve taxonomic synonyms based on Encyclopedia of Life
findSyns2 <- function(x){
  
  # get specific species name
  #taxname = hosts_vec[x]
  # a small change to use the function with our data
  taxname = x
  # print progress
  print(paste("Processing:", taxname, sep=" "))
  
  # phyla
  phyla = c("Chordata","Arthropoda","Gastropoda", "Mollusca")
  
  # (1) resolve misspellings
  taxname_resolved = gnr_resolve(taxname, with_canonical_ranks = TRUE)$matched_name2[1]
  if(!is.null(taxname_resolved)){ if(length(strsplit(taxname_resolved, " ", fixed=TRUE)[[1]]) == 2 ){ taxa = taxname_resolved }}
  if(!is.null(taxname_resolved)){ if(length(strsplit(taxname_resolved, " ", fixed=TRUE)[[1]]) > 2 ){ taxa = paste(strsplit(taxname_resolved, " ", fixed=TRUE)[[1]][1:2], collapse=" ")} }
  
  # if taxa == NA, return list with nothing defined 
  if(is.na(taxa)){   if(class(syns)[1] == 'simpleError'){ return(data.frame(Original=taxname, Submitted=taxname_resolved, Accepted_name=NA, Selected_family=NA, Selected_order=NA, Selected_class=NA, Synonyms=NA))} }
  
  # (2) remove sub-species categorizations and set 'genus' and 'species' variables
  genus = NULL
  if(length(strsplit(taxa, " ", fixed=TRUE)[[1]]) %in% c(2,3)){ genus = strsplit(taxa," ",fixed=TRUE)[[1]][1]; species = strsplit(taxa," ",fixed=TRUE)[[1]][2] }
  if(length(strsplit(taxa, "_", fixed=TRUE)[[1]]) %in% c(2,3)){ genus = strsplit(taxa,"_",fixed=TRUE)[[1]][1]; species = strsplit(taxa,"_",fixed=TRUE)[[1]][2] }
  if(length(strsplit(taxa, " ", fixed=TRUE)[[1]]) >3 | length(strsplit(taxa, "_" , fixed=TRUE)[[1]][1]) > 3){ return("name error") }
  if(is.null(genus)){ genus = taxa; species = NA }
  
  # (3) use genus to lookup family, order, class
  syns = tryCatch( name_lookup(genus)$data, error = function(e) e)
  if(class(syns)[1] == 'simpleError'){ return(data.frame(Original=taxname, Submitted=taxa, Accepted_name=NA, Selected_family=NA, Selected_order=NA, Selected_class=NA, Synonyms=NA))}
  
  # for cases where the lookup does not find a phylum within the specified range
  if(all(! syns$phylum %in% phyla)){
    fam1 = syns$family[ !is.na(syns$family) & !is.na(syns$phylum) ]
    order1 = syns$order[ !is.na(syns$family) & !is.na(syns$phylum) ]
    class1 = syns$class[ !is.na(syns$family) & !is.na(syns$phylum) ]
    datfam = data.frame(fam1=fam1, order=1:length(fam1), order1=order1, class1=class1)
    # select highest frequency fam/class/order combo
    fam2 = as.data.frame( table(datfam[ , c(1,3,4)]) )
    family2 = as.vector(fam2[ fam2$Freq==max(fam2$Freq, na.rm=TRUE), "fam1"] ) 
    order2 = as.vector(fam2[ fam2$Freq==max(fam2$Freq, na.rm=TRUE), "order1"] )
    class2 = as.vector(fam2[ fam2$Freq==max(fam2$Freq, na.rm=TRUE), "class1"] )
    if(length(fam2) > 1){
      datfam2 = datfam[datfam$fam1 %in% family2, ]
      family2 = as.vector(datfam2[datfam2$order == min(datfam2$order, na.rm=TRUE), "fam1"])
      order2 = as.vector(datfam2[datfam2$order == min(datfam2$order, na.rm=TRUE), "order1"])
      class2 = as.vector(datfam2[datfam2$order == min(datfam2$order, na.rm=TRUE), "class1"])
    }
  } else {  # for everything else
    fam1 = syns$family[ !is.na(syns$family) & !is.na(syns$phylum) & (syns$phylum %in% phyla) ]
    order1 = syns$order[ !is.na(syns$family) & !is.na(syns$phylum) & (syns$phylum %in% phyla) ]
    class1 = syns$class[ !is.na(syns$family) & !is.na(syns$phylum) & (syns$phylum %in% phyla) ]
    datfam = data.frame(fam1=fam1, order=1:length(fam1), order1 = order1, class1=class1)
    # select highest frequency fam/class/order combo
    fam2 = as.data.frame( table(datfam[ , c(1,3,4)]) )
    family2 = as.vector(fam2[ fam2$Freq==max(fam2$Freq, na.rm=TRUE), "fam1"] ) 
    order2 = as.vector(fam2[ fam2$Freq==max(fam2$Freq, na.rm=TRUE), "order1"] )
    class2 = as.vector(fam2[ fam2$Freq==max(fam2$Freq, na.rm=TRUE), "class1"] )
    # select highest in list if more than one max
    if(length(family2) > 1){
      datfam2 = datfam[datfam$fam1 %in% family2, ]
      family2 = as.vector(datfam2[datfam2$order == min(datfam2$order, na.rm=TRUE), "fam1"])
      order2 = as.vector(datfam2[datfam2$order == min(datfam2$order, na.rm

=TRUE), "order1"])
      class2 = as.vector(datfam2[datfam2$order == min(datfam2$order, na.rm=TRUE), "class1"])
    } 
  }

  # (4) search for species synonyms in ITIS
  syns = tryCatch(suppressMessages(synonyms(taxa, db='itis')), error=function(e) e)
  if(class(syns)[1] == 'simpleError'){ return(data.frame(Original=taxname, Submitted=taxa, Accepted_name="failed", Selected_family=family2, Selected_order=order2, Selected_class=class2, Synonyms="failed"))}
  syns = as.data.frame(syns[[1]])
  
  # get info
  original = taxa
  accepted_name = taxa # save accepted name as original searched name
  if("acc_name" %in% names(syns)){ accepted_name = syns$acc_name } # unless search shows that this is not the accepted name
   if("syn_name" %in% names(syns)){ synonyms = unique(syns$syn_name) 
  } else{ synonyms = NA }
  
  # combine into list and add synonyms 
  result = data.frame(Original=taxname, 
                      Submitted=taxa,
                      Accepted_name=accepted_name,
                      Selected_family=family2,
                      Selected_order=order2,
                      Selected_class=class2)
  result = do.call("rbind", replicate(length(synonyms), result[1, ], simplify = FALSE))
  result$Synonyms = synonyms
  return(result)
}

# nest function within a tryCatch call in case of any errors
findSyns3 = function(x){
  result = tryCatch(findSyns2(x), error=function(e) NULL)
  return(result)
}

Earlier, we saw that there’s a name synonymous with Ramphocelus sanguinolentus. What would the function by Gibb et al. do with that?

Code 
Ram.syn1 <- findSyns3("Phlogothraupis sanguinolenta")
[1] "Processing: Phlogothraupis sanguinolenta"
══  1 queries  ═══════════════
✔  Found:  Phlogothraupis sanguinolenta
══  Results  ═════════════════

• Total: 1 
• Found: 1 
• Not Found: 0
Code 
Ram.syn1
Original Submitted Accepted_name Selected_family Selected_order Selected_class Synonyms
Phlogothraupis sanguinolenta Phlogothraupis sanguinolenta Ramphocelus sanguinolentus Thraupidae Passeriformes Aves Phlogothraupis sanguinolenta

And with one that’s misspelled?

Code 
Ram.syn2 <- findSyns3(Ram.names2[2])
[1] "Processing: Ramphocelus passerini"
══  1 queries  ═══════════════
✔  Found:  Ramphocelus passerinii
══  Results  ═════════════════

• Total: 1 
• Found: 1 
• Not Found: 0
Code 
Ram.syn2
Original Submitted Accepted_name Selected_family Selected_order Selected_class Synonyms
Ramphocelus passerini Ramphocelus passerinii Ramphocelus passerinii Thraupidae Passeriformes Aves NA

4.2 Camera trap data management

Article by Niedballa et al. 2016,Methods Ecol. Evol.

From the methods section:

“Users are free to use any species names (or abbreviations or codes) they wish. If scientific or common species names are used, the function checkSpeciesNames can check them against the ITIS taxonomic database (www.itis.gov) and returns their matching counterparts (utilizing the R package taxize (Chamberlain & Szöcs 2013) internally), making sure species names and spelling are standardized and taxonomically sound, and thus making it easier to combine data sets from different studies.”

Let’s see some examples from the camtrapR vignettes. We’re not going to download the package because the CRAN version of ‘camtrapR’ is not compatible with the CRAN version of ‘taxize’.

Let’s rewrite the current function updating the arguments of ‘taxize’.

Code 
checkSpeciesNames <- function(speciesNames, searchtype, accepted = TRUE,
    ask = TRUE) {
    if (!requireNamespace("taxize", quietly = TRUE)) {
        stop("Please install the package taxize to run this function")
    }
    if (!requireNamespace("ritis", quietly = TRUE)) {
        stop("Please install the package ritis to run this function")
    }
    searchtype <- match.arg(searchtype, choices = c("scientific",
        "common"))
    stopifnot(is.logical(accepted))
    stopifnot(is.character(speciesNames) | is.factor(speciesNames))
    speciesNames <- unique(as.character(speciesNames))
    file.sep <- .Platform$file.sep
    tsns <- try(taxize::get_tsn(sci_com = speciesNames, searchtype = searchtype,
        accepted = accepted, ask = ask, messages = FALSE))
    if (inherits(tsns, "try-error")) {
        message(paste("error in get_tsn. Exiting without results:\n",
            tsns, sep = ""))
        return(invisible(NULL))
    }
    tsns <- taxize::as.tsn(unique(tsns), check = FALSE)
    if (any(is.na(tsns))) {
        not.matched <- which(is.na(tsns))
        warning(paste("found no matches for", length(not.matched),
            "name(s):\n", paste(speciesNames[not.matched], collapse = ", ")),
            immediate. = TRUE, call. = FALSE)
        tsns_worked <- taxize::as.tsn(tsns[-not.matched], check = FALSE)
    } else {
        tsns_worked <- tsns
    }

    if (length(tsns_worked) >= 1) {
        scientific <- common <- author <- rankname <- taxon_status <- data.frame(matrix(NA,
            nrow = length(tsns_worked), ncol = 2), stringsAsFactors = FALSE)
        colnames(scientific) <- c("tsn", "combinedname")
        colnames(common) <- c("tsn", "commonName")
        colnames(author) <- c("tsn", "authorship")
        colnames(rankname) <- c("tsn", "rankname")
        colnames(taxon_status) <- c("tsn", "taxonUsageRating")

        for (i in 1:length(tsns_worked)) {

            scientific_tmp <- ritis::scientific_name(tsns_worked[i])
            common_tmp <- ritis::common_names(tsns_worked[i])
            author_tmp <- ritis::taxon_authorship(tsns_worked[i])
            rankname_tmp <- ritis::rank_name(tsns_worked[i])
            if ("tsn" %in% colnames(scientific_tmp)) {
                scientific[i, ] <- scientific_tmp[c("tsn", "combinedname")]
            }
            if ("tsn" %in% colnames(common_tmp)) {
                if (length(unique(common_tmp$tsn)) > 1) {
                  common2 <- tapply(common_tmp$commonName, INDEX = common_tmp$tsn,
                    FUN = paste, collapse = file.sep)
                  common_tmp <- data.frame(commonName = common2, tsn = rownames(common2),
                    stringsAsFactors = FALSE)
                }
                common[i, ] <- common_tmp[, c("tsn", "commonName")]
            }
            if ("tsn" %in% colnames(author_tmp)) {
                author[i, ] <- author_tmp[c("tsn", "authorship")]
            }
            if ("tsn" %in% colnames(rankname_tmp)) {
                rankname[i, ] <- rankname_tmp[c("tsn", "rankname")]
            }
            if (accepted == FALSE) {
                taxon_status_tmp <- ritis::core_metadata(tsns_worked[i])
                if ("tsn" %in% colnames(taxon_status_tmp)) {
                  taxon_status[i, ] <- taxon_status_tmp[c("tsn", "taxonUsageRating")]
                }
            }
        }
        dat.out <- data.frame(user_name = speciesNames, tsn = as.numeric(tsns))
        dat.out <- merge(x = dat.out, y = scientific, by = "tsn",
            all.x = TRUE, sort = FALSE)
        dat.out <- merge(x = dat.out, y = common, by = "tsn", all.x = TRUE,
            sort = FALSE)
        dat.out <- merge(x = dat.out, y = author, by = "tsn", all.x = TRUE,
            sort = FALSE)
        dat.out <- merge(x = dat.out, y = rankname, by = "tsn", all.x = TRUE,
            sort = FALSE)
        dat.out$itis_url <- NA
        dat.out$itis_url[match(tsns_worked, dat.out$tsn)] <- attributes(tsns_worked)$uri
        colnames(dat.out)[colnames(dat.out) == "combinedname"] <- "scientificName"
        if (accepted == FALSE) {
            dat.out <- merge(x = dat.out, y = taxon_status, by = "tsn",
                all.x = TRUE, sort = FALSE)
        } else {
            dat.out$taxon_status[!is.na(dat.out$tsn)] <- "valid"
        }
        return(dat.out)
    } else {
        stop("found no TSNs for speciesNames", call. = FALSE)
    }
}

Now, what can we do?

4.2.1 Search information by common names

Code 
checkNames1 <- checkSpeciesNames(speciesNames = c("Bearded Pig", "Malayan Civet"),
    searchtype = "common")
Warning in `[<-.data.frame`(`*tmp*`, i, , value = structure(list(tsn =
c("625012", : replacement element 1 has 2 rows to replace 1 rows
Warning in `[<-.data.frame`(`*tmp*`, i, , value = structure(list(tsn =
c("625012", : replacement element 2 has 2 rows to replace 1 rows
Code 
checkNames1 %>%
    kbl() %>%
    kable_minimal()
tsn user_name scientificName commonName authorship rankname itis_url taxon_status
625012 Bearded Pig Sus barbatus bearded pig Müller, 1838 Species https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=625012 valid
622004 Malayan Civet Viverra tangalunga Malayan Civet Gray, 1832 Species https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=622004 valid

4.2.2 Search information by scientific names (including subspecies)

Code 
checkNames2 <- checkSpeciesNames(speciesNames = "Viverra tangalunga tangalunga",
    searchtype = "scientific")
checkNames2 %>%
    kbl() %>%
    kable_minimal()
tsn user_name scientificName commonName authorship rankname itis_url taxon_status
726578 Viverra tangalunga tangalunga Viverra tangalunga tangalunga NA Gray, 1832 Subspecies https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=726578 valid

4.2.3 Search information with an incorrect name

Code 
checkNames3 <- checkSpeciesNames(speciesNames = "Felis bengalensis",
    searchtype = "scientific", accepted = FALSE)

checkNames3
tsn user_name scientificName commonName authorship rankname itis_url taxonUsageRating
183793 Felis bengalensis Felis bengalensis leopard cat Kerr, 1792 Species https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=183793 invalid

4.2.4 Search information with an ambiguous name

Code 
checkNames4 <- checkSpeciesNames(speciesNames = "Chevrotain", searchtype = "common")
# choose from the menu
checkNames4

References

Chamberlain, S. A., & Szöcs, E. (2013). taxize: taxonomic search and retrieval in R. F1000Research, 2.

Session Information

R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.2 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/Costa_Rica
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] rgbif_3.7.9      kableExtra_1.3.4 taxize_0.9.100   knitr_1.46      
[5] emo_0.0.0.9000  

loaded via a namespace (and not attached):
 [1] gtable_0.3.4       xfun_0.43          ggplot2_3.5.0      htmlwidgets_1.6.4 
 [5] lattice_0.20-45    vctrs_0.6.5        tools_4.3.2        generics_0.1.3    
 [9] curl_5.2.0         parallel_4.3.2     tibble_3.2.1       fansi_1.0.6       
[13] highr_0.10         pkgconfig_2.0.3    data.table_1.14.10 assertthat_0.2.1  
[17] webshot_0.5.5      uuid_1.2-0         lifecycle_1.0.4    conditionz_0.1.0  
[21] compiler_4.3.2     stringr_1.5.1      munsell_0.5.0      ritis_1.0.0       
[25] codetools_0.2-18   htmltools_0.5.8.1  lazyeval_0.2.2     yaml_2.3.8        
[29] whisker_0.4.1      pillar_1.9.0       crayon_1.5.2       solrium_1.2.0     
[33] iterators_1.0.14   foreach_1.5.2      nlme_3.1-155       tidyselect_1.2.0  
[37] rvest_1.0.3        digest_0.6.35      stringi_1.8.3      dplyr_1.1.4       
[41] purrr_1.0.2        fastmap_1.1.1      grid_4.3.2         colorspace_2.1-0  
[45] cli_3.6.2          magrittr_2.0.3     bold_1.3.0         triebeard_0.4.1   
[49] crul_1.4.0         utf8_1.2.4         ape_5.7-1          scales_1.3.0      
[53] oai_0.4.0          lubridate_1.9.3    timechange_0.2.0   rmarkdown_2.26    
[57] httr_1.4.7         zoo_1.8-12         evaluate_0.23      viridisLite_0.4.2 
[61] rlang_1.1.3        urltools_1.7.3     Rcpp_1.0.12        glue_1.7.0        
[65] httpcode_0.3.0     formatR_1.14       xml2_1.3.6         svglite_2.1.3     
[69] rstudioapi_0.15.0  jsonlite_1.8.8     plyr_1.8.9         R6_2.5.1          
[73] systemfonts_1.0.5