taxize is a taxonomic toolbelt for R. taxize wraps APIs for a large suite of taxonomic databases availab on the web.
taxizeFirst, install and load taxize into the R session.
install.packages("taxize")
library(taxize)
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##
## New to taxize? Tutorial at http://ropensci.org/tutorials/taxize_tutorial.html
## citation(package='taxize') for the citation for this package
## API key names have changed. Use tropicosApiKey, eolApiKey, ubioApiKey, and pmApiKey in your .Rprofile file.
## Use suppressPackageStartupMessages() to suppress these startup messages in the future
Advanced users can also download and install the latest development copy from GitHub.
This is a common task in biology. We often have a list of species names and we want to know a) if we have the most up to date names, b) if our names are spelled correctly, and c) the scientific name for a common name. One way to resolve names is via the Global Names Resolver (GNR) service provided by the Encyclopedia of Life. Here, we are searching for two misspelled names:
temp <- gnr_resolve(names = c("Helianthos annus", "Homo saapiens"))
## Loading required package: rjson
temp[, -c(1, 4)]
## matched_name data_source_title
## 1 Helianthus annuus L. Catalogue of Life
## 2 Helianthus annus L. EOL
## 3 Helianthus annus EOL
## 4 Helianthus annus uBio NameBank
## 5 Homo sapiens Linnaeus, 1758 Catalogue of Life
The correct spellings are Helianthus annuus and Homo sapiens. Another approach uses the Taxonomic Name Resolution Service via the Taxosaurus API developed by iPLant and the Phylotastic organization. In this example, we provide a list of species names, some of which are misspelled, and we'll call the API with the tnrs function.
mynames <- c("Helianthus annuus", "Pinus contort", "Poa anua", "Abis magnifica",
"Rosa california", "Festuca arundinace", "Sorbus occidentalos", "Madia sateva")
tnrs(query = mynames, source = "iPlant_TNRS")[, -c(5:7)]
## Calling http://taxosaurus.org/retrieve/14512c6611cb44a5c4992ac72df8f75f
## submittedname acceptedname sourceid score
## 7 Helianthus annuus Helianthus annuus iPlant_TNRS 1
## 4 Pinus contort Pinus contorta iPlant_TNRS 0.98
## 5 Poa anua Poa annua iPlant_TNRS 0.96
## 3 Abis magnifica Abies magnifica iPlant_TNRS 0.96
## 8 Rosa california Rosa californica iPlant_TNRS 0.99
## 2 Festuca arundinace Festuca arundinacea iPlant_TNRS 0.99
## 1 Sorbus occidentalos Sorbus occidentalis iPlant_TNRS 0.99
## 6 Madia sateva Madia sativa iPlant_TNRS 0.97
It turns out there are a few corrections: e.g., Madia sateva should be Madia sativa, and Rosa california should be Rosa californica. Note that this search worked because fuzzy matching was employed to retrieve names that were close, but not exact matches. Fuzzy matching is only available for plants in the TNRS service, so we advise using EOL's Global Names Resolver if you need to resolve animal names.
taxize takes the approach that the user should be able to make decisions about what resource to trust, rather than making the decision. Both the EOL GNR and the TNRS services provide data from a variety of data sources. The user may trust a specific data source, thus may want to use the names from that data source. In the future, we may provide the ability for taxize to suggest the best match from a variety of sources.
Another common use case is when there are many synonyms for a species. In this example, we have three synonyms of the currently accepted name for a species.
mynames <- c("Helianthus annuus ssp. jaegeri", "Helianthus annuus ssp. lenticularis",
"Helianthus annuus ssp. texanus")
tsn <- get_tsn(mynames)
library(plyr)
ldply(tsn, itis_acceptname)
submittedtsn acceptedname acceptedtsn
1 525928 Helianthus annuus 36616
2 525929 Helianthus annuus 36616
3 525930 Helianthus annuus 36616
Another task biologists often face is getting higher taxonomic names for a taxa list. Having the higher taxonomy allows you to put into context the relationships of your species list. For example, you may find out that species A and species B are in Family C, which may lead to some interesting insight, as opposed to not knowing that Species A and B are closely related. This also makes it easy to aggregate/standardize data to a specific taxonomic level (e.g., family level) or to match data to other databases with different taxonomic resolution (e.g., trait databases).
A number of data sources in taxize provide the capability to retrieve higher taxonomic names, but we will highlight two of the more useful ones: Integrated Taxonomic Information System (ITIS) and National Center for Biotechnology Information (NCBI). First, we'll search for two species, Abies procera} and *Pinus contorta within ITIS.
specieslist <- c("Abies procera", "Pinus contorta")
classification(specieslist, db = "itis")
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## Retrieving data for taxon 'Abies procera'
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##
## Retrieving data for taxon 'Pinus contorta'
## $`Abies procera`
## name rank
## 1 Plantae Kingdom
## 2 Viridaeplantae Subkingdom
## 3 Streptophyta Infrakingdom
## 4 Tracheophyta Division
## 5 Spermatophytina Subdivision
## 6 Gymnospermae Infradivision
## 7 Pinopsida Class
## 8 Pinales Order
## 9 Pinaceae Family
## 10 Abies Genus
## 11 Abies procera Species
##
## $`Pinus contorta`
## name rank
## 1 Plantae Kingdom
## 2 Viridaeplantae Subkingdom
## 3 Streptophyta Infrakingdom
## 4 Tracheophyta Division
## 5 Spermatophytina Subdivision
## 6 Gymnospermae Infradivision
## 7 Pinopsida Class
## 8 Pinales Order
## 9 Pinaceae Family
## 10 Pinus Genus
## 11 Pinus contorta Species
##
## attr(,"class")
## [1] "classification"
## attr(,"db")
## [1] "itis"
It turns out both species are in the family Pinaceae. You can also get this type of information from the NCBI by doing classification(specieslist, db = 'ncbi').
Instead of a full classification, you may only want a single name, say a family name for your species of interest. The function *tax_name} is built just for this purpose. As with the classification function you can specify the data source with the db argument, either ITIS or NCBI.
tax_name(query = "Helianthus annuus", get = "family", db = "ncbi")
##
## Retrieving data for taxon 'Helianthus annuus'
## family
## 1 Asteraceae
I may happen that a data source does not provide information on the queried species, than one could take the result from another source and union the results from the different sources.
As mentioned most databases use a numeric code to reference a species. A general workflow in taxize is: Retrieve Code for the queried species and then use this code to query more data/information.
Below are a few examples. When you run these examples in R, you are presented with a command prompt asking for the row that contains the name you would like back; that output is not printed below for brevity. In this example, the search term has many matches. The function returns a data.frame of the matches, and asks for the user to input what row number to accept.
get_tsn(searchterm = "Heliastes", searchtype = "sciname")
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## Retrieving data for taxon 'Heliastes'
## target tsn
## 1 Heliastes bicolor 615238
## 2 Heliastes chrysurus 615250
## 3 Heliastes cinctus 615573
## 4 Heliastes dimidiatus 615257
## 5 Heliastes hypsilepis 615273
## 6 Heliastes immaculatus 615639
## 7 Heliastes opercularis 615300
## 8 Heliastes ovalis 615301
##
## More than one TSN found for taxon 'Heliastes'!
##
## Enter rownumber of taxon (other inputs will return 'NA'):
##
## Input accepted, took taxon 'Heliastes bicolor'.
## [1] "615238"
## attr(,"match")
## [1] "found"
## attr(,"class")
## [1] "tsn"
In another example, you can pass in a long character vector of taxonomic names:
splist <- c("annona cherimola", "annona muricata", "quercus robur")
get_tsn(searchterm = splist, searchtype = "sciname")
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## Retrieving data for taxon 'annona cherimola'
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##
## Retrieving data for taxon 'annona muricata'
##
##
## Retrieving data for taxon 'quercus robur'
## [1] "506198" "18098" "19405"
## attr(,"match")
## [1] "found" "found" "found"
## attr(,"class")
## [1] "tsn"
If someone is not a taxonomic specialist on a particular taxon he likely does not know what children taxa are within a family, or within a genus. This task becomes especially unwieldy when there are a large number of taxa downstream. You can of course go to a website like Wikispecies or Encyclopedia of Life to get downstream names. However, taxize provides an easy way to programatically search for downstream taxa, both for the Catalogue of Life (CoL) and the Integrated Taxonomic Information System. Here is a short example using the CoL in which we want to find all the species within the genus Apis (honey bees).
col_downstream(name = "Apis", downto = "Species")[[1]]
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## childtaxa_id childtaxa_name childtaxa_rank
## 1 6971712 Apis andreniformis Species
## 2 6971713 Apis cerana Species
## 3 6971714 Apis dorsata Species
## 4 6971715 Apis florea Species
## 5 6971716 Apis koschevnikovi Species
## 6 6845885 Apis mellifera Species
## 7 6971717 Apis nigrocincta Species
The result from the above call to col_downstream() is a data.frame that gives a number of columns of different information.
Biologist often need to match different sets of data tied to species. For example, trait-based approaches are a promising tool in ecology. One problem is that abundance data must be matched with trait databases. These two data tables may contain species information on different taxonomic levels and possibly data must be aggregated to a joint taxonomic level, so that the data can be merged. taxize can help in this data-cleaning step, providing a reproducible workflow:
We can use the mentioned classification-function to retrieve the taxonomic hierarchy and then search the hierarchies up- and downwards for matches. Here is an example to match a species with names on three different taxonomic levels.
A <- "gammarus roeseli"
B1 <- "gammarus roeseli"
B2 <- "gammarus"
B3 <- "gammaridae"
A_clas <- classification(A, db = 'ncbi')
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## Retrieving data for taxon 'gammarus roeseli'
B1_clas <- classification(B1, db = 'ncbi')
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## Retrieving data for taxon 'gammarus roeseli'
B2_clas <- classification(B2, db = 'ncbi')
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## Retrieving data for taxon 'gammarus'
B3_clas <- classification(B3, db = 'ncbi')
##
## Retrieving data for taxon 'gammaridae'
B1[match(A, B1)]
## [1] "gammarus roeseli"
A_clas[[1]]$Rank[tolower(A_clas[[1]]$ScientificName) %in% B2]
## NULL
A_clas[[1]]$Rank[tolower(A_clas[[1]]$ScientificName) %in% B3]
## NULL
If we find a direct match (here Gammarus roeseli), we are lucky. But we can also match Gammaridae with Gammarus roeseli, but on a lower taxonomic level. A more comprehensive and realistic example (matching a trait table with an abundance table) is given in the vignette on matching.