In ropensci/taxlist: Handling Taxonomic Lists

1. Getting started

The package taxlist aims to implement an object class and functions (methods) for handling taxonomic data in R. The homonymous object class taxlist can be further linked to biodiversity records (e.g. for observations in vegetation plots).

The taxlist package is developed on the repository GitHub (https://github.com/ropensci/taxlist) and can be installed in your R-session using the package devtools:

library(devtools)
install_github("ropensci/taxlist", build_vignettes = TRUE)

Since this package is already available in the Comprehensive R Archive Network (CRAN), it is also possible to install it using the function install.packages:

install.packages("taxlist", dependencies = TRUE)

Of course, you have to load taxlist into your R-session.

library(taxlist)

For accessing to this vignette, use following command:

vignette("taxlist-intro")

2. Extracting a species list from a vegetation table

2.1 Example data

One of the main tasks of taxlist is to structure taxonomic information for a further linkage to biodiversity records. This structure have to be on the one side consistent with taxonomic issues (e.g. synonyms, hierarchies, etc.), on the other side have to be flexible for containing different depth of information availability (from plain species lists to hierarchical structures).

In this guide, we will work with a species list from phytosociological relevés collected at the borderline between the Democratic Republic of the Congo and Rwanda (Mullenders 1953 Vegetatio 4(2): 73--83).

The digitized data can be loaded by following command:

load(file.path(path.package("taxlist"), "Cross.rda"))

The data is formatted as data.frame in R, including the names of the species in the first column:

head(Cross[, 1:8])

2.2 From plain list to taxlist

As already mentioned, the first column in the cross table contains the names of the species occurring in the observed plots. Thus, we can use this character vector to construct a taxlist object. This can be achieved through the function df2taxlist().

sp_list <- Cross[, "TaxonName"]
sp_list <- df2taxlist(x = sp_list)
summary(sp_list)

Note that the function summary provides a quick overview in the content of the resulting object. This function can be also applied to a specific taxon:

summary(object = sp_list, ConceptID = "Erigeron floribundus")

3. Built-in data set

3.1 Easplist

The installation of taxlist includes the data Easplist, which is formatted as a taxlist object. This data is a subset of the species list used by the database SWEA-Dataveg (GIVD ID AF-006):

data(Easplist)
Easplist

3.2 Access to slots

The common ways to access to the content of slots in S4 objects are either using the function slot(object, name) or the symbol @ (i.e. object@name). Additional functions, which are specific for taxlist objects are taxon_names, taxon_relations, taxon_traits and taxon_views (see the help documentation).

Additionally, it is possible to use the methods $ and [ , the first for access to information in the slot taxonTraits, while the second can be also used for other slots in the object.

summary(as.factor(Easplist$life_form))

3.3 Subsets

Methods for the function subset are also implemented in this package. Such subsets usually apply pattern matching (for character vectors) or logical operations and are analogous to query building in relational databases. The subset method can be apply to any slot by setting the value of the argument slot.

Papyrus <- subset(x = Easplist, subset = grepl("papyrus", TaxonName), slot = "names")
summary(Papyrus, "all")

Or the very same results:

Papyrus <- subset(x = Easplist, subset = TaxonConceptID == 206, slot = "relations")
summary(Papyrus, "all")

Similarly, you can look for a specific name.

Phraaus <- subset(
  x = Easplist,
  subset = charmatch("Phragmites australis", TaxonName), slot = "names"
)
summary(Phraaus, "all")

3.4 Hierarchical structure

Objects belonging to the class taxlist can optionally content parent-child relationships and taxonomic levels. Such information is also included in the data Easplist, as shown in the summary output.

Easplist

Note that such information can get lost once subset() has been applied, since the respective parents or children from the original data set are not anymore in the subset. May you like to recover parents and children, you can use the functions get_parents() or get_children(), respectively.

summary(Papyrus, "all")
Papyrus <- get_parents(Easplist, Papyrus)
summary(Papyrus, "all")

Another way to represent taxonomic ranks is by using the function indented_list().

indented_list(Papyrus)

4. Applying taxlist to syntaxonomic schemes

4.1 Example of a phytosociological classification

To illustrate the flexibility of the taxlist objects, the next example will handle a syntaxonomical scheme. As example it will be used a scheme proposed by the author for aquatic and semi-aquatic vegetation in Tanzania (Alvarez 2017 Phytocoenologia in review). The scheme includes 10 associations classified into 4 classes:

4.2 Building the taxlist object

The content for the taxonomic list is included in a data frame and can be downloaded by following command:

load(file.path(path.package("taxlist"), "wetlands_syntax.rda"))

The data frame Concepts contains the list of syntaxon names that are considered as accepted in the previous scheme. This list will be used to insert the new concepts in the taxlist object.

head(Concepts)

Concepts$TaxonUsageID <- Concepts$TaxonConceptID

Syntax <- df2taxlist(Concepts)

levels(Syntax) <- c("association", "alliance", "order", "class")

taxon_views(Syntax) <- data.frame(
  ViewID = 1, Secundum = "Alvarez (2017)",
  Author = "Alvarez M", Year = 2017,
  Title = "Classification of aquatic and semi-aquatic vegetation in East Africa",
  stringsAsFactors = FALSE
)

Syntax@taxonRelations$ViewID <- 1

Syntax

Note that the function new created an empty object (prototype), while levels insert the custom levels (syntaxonomical hierarchies). For the later function, the levels have to be inserted from the lower to the higher ranks. Furthermore the reference defining the concepts included in the syntaxonomic scheme was inserted in the object using the function taxon_views and finally the concepts were inserted by the function add_concept.

The next step will be inserting those names that are considered as synonyms for the respective syntaxa. Synonyms are included in the data frame Synonyms.

head(Synonyms)
Syntax <- add_synonym(Syntax,
  ConceptID = Synonyms$TaxonConceptID,
  TaxonName = Synonyms$TaxonName, AuthorName = Synonyms$AuthorName
)

Finally, the codes provided for the associations will be inserted as traits properties) of them in the slot taxonTraits.

head(Codes)
taxon_traits(Syntax) <- Codes
Syntax

For instance, you may like to get the parental chain from an association (e.g. for Nymphaeetum loti).

Nymplot <- subset(Syntax, charmatch("Nymphaeetum", TaxonName), slot = "names")
summary(Nymplot, "all")

Note that there is the logical arguments keep_parents and keep_children to preserve hierarchical information in the subset:

Nymplot <- subset(Syntax, charmatch("Nymphaeetum", TaxonName),
  slot = "names",
  keep_parents = TRUE
)
summary(Nymplot, "all")
indented_list(Nymplot)

By using the function subset() we just created a new object containing only the association Nymphaeetum loti and its parental chain. This subset was then used to extract the parental chain from Syntax.

ropensci/taxlist documentation built on Sept. 17, 2024, 3:39 p.m.