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])

##                 TaxonName 3094 3093 3092 3095 3096 3097 3098
## 1   Eragrostis tenuifolia    + <NA> <NA> <NA> <NA> <NA> <NA>
## 2        Cyperus sublimis <NA>    + <NA> <NA> <NA> <NA> <NA>
## 3    Digitaria abyssinica    +    1    2    2    2    3    1
## 4 Hyparrhenia filipendula <NA> <NA> <NA> <NA> <NA> <NA> <NA>
## 5    Erigeron floribundus    +    1 <NA> <NA> <NA> <NA> <NA>
## 6            Aerva lanata    +    1 <NA> <NA> <NA> <NA> <NA>

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)

## Missing column 'TaxonConceptID' in 'x'. All names will be considered as accepted names.

summary(sp_list)

## object size: 9 Kb 
## validation of 'taxlist' object: TRUE 
## 
## number of taxon usage names: 35 
## number of taxon concepts: 35 
## trait entries: 0 
## number of trait variables: 0 
## taxon views: 0

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")

## ------------------------------ 
## concept ID: 5 
## view ID: none 
## level: none 
## parent: none 
## 
## # accepted name: 
## 5 Erigeron floribundus NA 
## ------------------------------

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

## object size: 761.4 Kb 
## validation of 'taxlist' object: TRUE 
## 
## number of taxon usage names: 5393 
## number of taxon concepts: 3887 
## trait entries: 311 
## number of trait variables: 1 
## taxon views: 3 
## 
## concepts with parents: 3698 
## concepts with children: 1343 
## 
## hierarchical levels: form < variety < subspecies < species < complex < genus < family 
## number of concepts in level form: 2
## number of concepts in level variety: 95
## number of concepts in level subspecies: 71
## number of concepts in level species: 2521
## number of concepts in level complex: 1
## number of concepts in level genus: 1011
## number of concepts in level family: 186

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))

##   acropleustophyte        chamaephyte     climbing_plant facultative_annual 
##                  8                 25                 25                 20 
##    obligate_annual       phanerophyte   pleustohelophyte         reed_plant 
##                114                 26                  8                 14 
##      reptant_plant      tussock_plant               NA's 
##                 19                 52               3576

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

## object size: 761.4 Kb 
## validation of 'taxlist' object: TRUE 
## 
## number of taxon usage names: 5393 
## number of taxon concepts: 3887 
## trait entries: 311 
## number of trait variables: 1 
## taxon views: 3 
## 
## concepts with parents: 3698 
## concepts with children: 1343 
## 
## hierarchical levels: form < variety < subspecies < species < complex < genus < family 
## number of concepts in level form: 2
## number of concepts in level variety: 95
## number of concepts in level subspecies: 71
## number of concepts in level species: 2521
## number of concepts in level complex: 1
## number of concepts in level genus: 1011
## number of concepts in level family: 186

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")

## ------------------------------ 
## concept ID: 206 
## view ID: 1 
## level: species 
## parent: none 
## 
## # accepted name: 
## 206 Cyperus papyrus L. 
## 
## # synonyms (2): 
## 52612 Cyperus papyrus ssp. antiquorum (Willd.) Chiov. 
## 52613 Cyperus papyrus ssp. nyassicus Chiov. 
## ------------------------------

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

## ------------------------------ 
## concept ID: 206 
## view ID: 1 
## level: species 
## parent: 54853 Cyperus L. 
## 
## # accepted name: 
## 206 Cyperus papyrus L. 
## 
## # synonyms (2): 
## 52612 Cyperus papyrus ssp. antiquorum (Willd.) Chiov. 
## 52613 Cyperus papyrus ssp. nyassicus Chiov. 
## ------------------------------ 
## concept ID: 54853 
## view ID: 2 
## level: genus 
## parent: 55959 Cyperaceae Juss. 
## 
## # accepted name: 
## 54855 Cyperus L. 
## ------------------------------ 
## concept ID: 55959 
## view ID: 3 
## level: family 
## parent: none 
## 
## # accepted name: 
## 55961 Cyperaceae Juss. 
## ------------------------------

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

indented_list(Papyrus)

## Cyperaceae Juss.
##  Cyperus L.
##    Cyperus papyrus L.

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)

##   TaxonConceptID Parent                                TaxonName
## 1              1     NA                         Lemnetea minoris
## 2              2      1                 Salvinio-Eichhornietalia
## 3              3      2                       Pistion stratiotes
## 4              4      3 Lemno paucicostatae-Pistietum stratiotes
## 5              5     NA                                Potametea
## 6              6      5                       Nymphaeetalia loti
##                                   AuthorName       Level
## 1    Koch & Tüxen ex den Hartog & Segal 1964       class
## 2 Borhidi ex Borhidi, Muñiz & del Risco 1979       order
## 3                (Schmitz 1971) Schmitz 1988    alliance
## 4                                Lebrun 1947 association
## 5                Klika ex Klika & Novák 1941       class
## 6                                Lebrun 1947       order

Concepts$TaxonUsageID <- Concepts$TaxonConceptID

Syntax <- df2taxlist(Concepts)

## No values for 'AcceptedName' in 'x'. all names will be considered as accepted names.

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

## object size: 11.2 Kb 
## validation of 'taxlist' object: TRUE 
## 
## number of taxon usage names: 26 
## number of taxon concepts: 26 
## trait entries: 0 
## number of trait variables: 0 
## taxon views: 1 
## 
## concepts with parents: 22 
## concepts with children: 16 
## 
## hierarchical levels: association < alliance < order < class 
## number of concepts in level association: 10
## number of concepts in level alliance: 7
## number of concepts in level order: 5
## number of concepts in level class: 4

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)

##   TaxonConceptID                             TaxonName
## 1              1                          Stratiotetea
## 2              3                  Pistion pantropicale
## 3              8               Utriculario-Nymphaeetum
## 4              8 Utriculario exoletae-Nymphaeetum loti
## 5              9                          Phragmitetea
## 6             10                           Papyretalia
##                   AuthorName
## 1    den Hartog & Segal 1964
## 2               Schmitz 1971
## 3 (Lebrun 1947) Léonard 1950
## 4    Szafranski & Apema 1983
## 5      Tüxen & Preising 1942
## 6                Lebrun 1947

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)

##   TaxonConceptID Code
## 1             12  HE1
## 2             13  HE2
## 3             14  HE3
## 4             20  HE4
## 5             17  HE5
## 6             18  HE6

taxon_traits(Syntax) <- Codes
Syntax

## object size: 13.8 Kb 
## validation of 'taxlist' object: TRUE 
## 
## number of taxon usage names: 37 
## number of taxon concepts: 26 
## trait entries: 10 
## number of trait variables: 1 
## taxon views: 1 
## 
## concepts with parents: 22 
## concepts with children: 16 
## 
## hierarchical levels: association < alliance < order < class 
## number of concepts in level association: 10
## number of concepts in level alliance: 7
## number of concepts in level order: 5
## number of concepts in level class: 4

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")

## ------------------------------ 
## concept ID: 8 
## view ID: 1 
## level: association 
## parent: none 
## 
## # accepted name: 
## 8 Nymphaeetum loti Lebrun 1947 
## 
## # synonyms (2): 
## 29 Utriculario-Nymphaeetum (Lebrun 1947) Léonard 1950 
## 30 Utriculario exoletae-Nymphaeetum loti Szafranski & Apema 1983 
## ------------------------------

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")

## ------------------------------ 
## concept ID: 5 
## view ID: 1 
## level: class 
## parent: none 
## 
## # accepted name: 
## 5 Potametea Klika ex Klika & Novák 1941 
## ------------------------------ 
## concept ID: 6 
## view ID: 1 
## level: order 
## parent: 5 Potametea Klika ex Klika & Novák 1941 
## 
## # accepted name: 
## 6 Nymphaeetalia loti Lebrun 1947 
## ------------------------------ 
## concept ID: 7 
## view ID: 1 
## level: alliance 
## parent: 6 Nymphaeetalia loti Lebrun 1947 
## 
## # accepted name: 
## 7 Nymphaeion loti Lebrun 1947 
## ------------------------------ 
## concept ID: 8 
## view ID: 1 
## level: association 
## parent: 7 Nymphaeion loti Lebrun 1947 
## 
## # accepted name: 
## 8 Nymphaeetum loti Lebrun 1947 
## 
## # synonyms (2): 
## 29 Utriculario-Nymphaeetum (Lebrun 1947) Léonard 1950 
## 30 Utriculario exoletae-Nymphaeetum loti Szafranski & Apema 1983 
## ------------------------------

indented_list(Nymplot)

## Potametea Klika ex Klika & Novák 1941
##  Nymphaeetalia loti Lebrun 1947
##   Nymphaeion loti Lebrun 1947
##    Nymphaeetum loti Lebrun 1947

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.