Selecting a list of target species is a common task in macroecological and conservation studies. For example, a researcher may seek to model the distribution of exclusively endemic trees within a designated Biome, a particular State, or a specific vegetation type.
By applying different filters to the database, anyone can obtain a verified taxonomic list of Brazilian species of algae, fungi, and plants for any Brazilian state, region, biome, and vegetation type. Additionally, the filter can be applied by family, genus, life form, habitat, endemism level, origin, nomenclatural status, and taxonomic status. In this vignette, users will learn how to use florabr package to select a list of species based on these features.
Before you begin, use the load_florabr function to load
the data. For more detailed information on obtaining and loading the
data, please refer to [Getting started with florabr] - how to reference
vignette???
One of the primary objectives of this package is to assist in selecting a species list based on taxonomic classification (Kingdom, Group, Family, and Genus), characteristics (life form, habitat), and distribution (federal states, biomes, vegetation types, and endemism). Specifically, you can filter by:
To explore all available options for each filter, use the
get_attributes() function with the desired attribute. This
function will provide a list with the available options to use in the
select_species() function.
#Get available options to filter by lifeForm
get_attributes(data = bf,
attribute = "lifeForm")[[1]]
#> lifeForm
#> 1 Aquatic-Benthos
#> 2 Aquatic-Neuston
#> 3 Aquatic-Plankton
#> 4 Shrub
#> 5 Tree
#> 6 Bamboo
#> 7 Cushion
#> 8 Dendroid
#> 9 Unknown
#> 10 Dracaenoid
#> 13 Herb
#> 14 Flabellate
#> 15 Foliose
#> 16 Liana/scandent/vine
#> 19 Palm_tree
#> 21 Pendent
#> 23 Subshrub
#> 24 Succulent
#> 25 Thallose
#> 26 Mat
#> 27 Weft
#> 28 Tuft
#Get available options to filter by Biome
get_attributes(data = bf,
attribute = "Biome")[[1]]
#> biome
#> 1 Amazon
#> 2 Atlantic_Forest
#> 3 Caatinga
#> 4 Cerrado
#> 5 Pampa
#> 6 Pantanal
#> 7 Not_found_in_brazil
#Get available options to filter by vegetation
get_attributes(data = bf,
attribute = "vegetation")[[1]]
#> vegetation
#> 1 Anthropic_Area
#> 2 Caatinga
#> 3 Amazonian_Campinarana
#> 4 High_Altitude_Grassland
#> 5 Flooded_Field
#> 6 Grassland
#> 7 Highland_Rocky_Field
#> 8 Carrasco
#> 9 Cerrado
#> 10 Gallery_Forest
#> 11 Inundated_Forest_Igapo
#> 12 Terra_Firme_Forest
#> 13 Inundated_Forest
#> 14 Seasonallly_Deciduous_Forest
#> 15 Seasonal_Evergreen_Forest
#> 16 Seasonally_Semideciduous_Forest
#> 17 Rainforest
#> 18 Mixed_Ombrophyllous_Forest
#> 19 Mangrove
#> 20 Palm_Grove
#> 21 Restinga
#> 22 Amazonian_Savanna
#> 23 Aquatic_vegetation
#> 24 Rock_outcrop_vegetation
#> 25 Not_found_in_BrazilAs an illustration, let’s consider the scenario where we aim to retrieve a list of all native and endemic trees with confirmed occurrences in the Atlantic Forest:
af_spp <- select_species(data = bf,
include_subspecies = FALSE, include_variety = FALSE,
kingdom = "Plantae", group = "All", subgroup = "All",
family = "All", genus = "All",
lifeForm = "Tree", #Specify tree species
filter_lifeForm = "in",
habitat = "All", filter_habitat = "in",
biome = "Atlantic_Forest", #Occuring in the At. Forest
filter_biome = "in", #In Atlantic Forest
state = "All", filter_state = "in",
vegetation = "All", filter_vegetation = "in",
endemism = "Endemic", #Only endemics to Brazil
origin = "Native", #Only natives
taxonomicStatus = "Accepted",
nomenclaturalStatus = "All")
nrow(af_spp)
#> [1] 2372The filter returned 2372 species that meet the specified criteria. It’s important to note that these selections include species with confirmed occurrences in the Atlantic Forest, and some of them may also have confirmed occurrences in other biomes.
#First 7 unique values of biomes in the filtered dataset
unique(af_spp$biome)[1:7]
#> [1] "Atlantic_Forest"
#> [2] "Atlantic_Forest;Cerrado"
#> [3] "Atlantic_Forest;Caatinga"
#> [4] "Amazon;Atlantic_Forest;Caatinga;Cerrado"
#> [5] "Amazon;Atlantic_Forest;Cerrado"
#> [6] "Atlantic_Forest;Caatinga;Cerrado"
#> [7] "Amazon;Atlantic_Forest"If you wish to exclusively select species with confirmed occurrences in the Atlantic Forest, modify the filter_biome parameter to “only”:
only_af_spp <- select_species(data = bf,
include_subspecies = FALSE, include_variety = FALSE,
kingdom = "Plantae", group = "All", subgroup = "All",
family = "All", genus = "All",
lifeForm = "Tree", #Specify tree species
filter_lifeForm = "in",
habitat = "All", filter_habitat = "in",
biome = "Atlantic_Forest", #Occuring in the At. Forest
filter_biome = "only", #ONLY in Atlantic Forest
state = "All", filter_state = "in",
vegetation = "All", filter_vegetation = "in",
endemism = "Endemic", #Only endemics to Brazil
origin = "Native", #Only natives
taxonomicStatus = "Accepted",
nomenclaturalStatus = "All")
nrow(only_af_spp)
#> [1] 1858
unique(only_af_spp$biome)
#> [1] "Atlantic_Forest"Now, the filter has resulted in 1858 species, all exclusively confined to the Atlantic Forest biome.
Furthermore, the package offers the flexibility to apply various
filtering options (please consult ?select_species for
comprehensive details). For instance, consider the scenario where we aim
to compile a list of native and endemic trees with confirmed occurrences
limited solely to the Atlantic Forest biome and with confirmed
occurrences in the states of Paraná (PR), Santa Catarina (SC), and Rio
Grande do Sul (RS):
south_af_spp <- select_species(data = bf,
include_subspecies = FALSE, include_variety = FALSE,
kingdom = "Plantae", group = "All", subgroup = "All",
family = "All", genus = "All",
lifeForm = "Tree", #Specify tree species
filter_lifeForm = "in",
habitat = "All", filter_habitat = "in",
biome = "Atlantic_Forest", #Occuring in the At. Forest
filter_biome = "only", #Only in Atlantic Forest
state = c("PR", "SC", "RS"), #states - Use the acronynms
filter_state = "in", #IN at least one of these states
vegetation = "All", filter_vegetation = "in",
endemism = "Endemic", #Only endemics to Brazil
origin = "Native", #Only natives
taxonomicStatus = "Accepted",
nomenclaturalStatus = "All")
nrow(south_af_spp)
#> [1] 372
#First 10 unique values of states in the filtered dataset
unique(south_af_spp$states)[1:10]
#> [1] "BA;ES;PR;RJ;SC;SP" "AL;BA;CE;ES;MA;MG;PB;PE;PR;RJ;SE;SP"
#> [3] "PR;RS;SC" "BA;CE;ES;MA;MG;PE;PR;RJ;SP"
#> [5] "MG;PR;RJ;SC;SP" "MG;PR;RJ;RS;SC;SP"
#> [7] "BA;ES;MG;PR;RJ;SC;SP" "MG;PR;RS;SC;SP"
#> [9] "PR;RJ;RS;SC;SP" "ES;MG;PR;RJ;RS;SC;SP"By utilizing filter_state = “in”, our selection encompassed species occurring in all three states, as well as those appearing in only two or even one of them. To impose a more rigorous criterion, selecting solely those species with confirmed occurrences in all three states, we can use filter_state = “and”:
south_af_spp2 <- select_species(data = bf,
include_subspecies = FALSE, include_variety = FALSE,
kingdom = "Plantae", group = "All", subgroup = "All",
family = "All", genus = "All",
lifeForm = "Tree", #Specify tree species
filter_lifeForm = "in",
habitat = "All", filter_habitat = "in",
biome = "Atlantic_Forest", #Occurring in the At. Forest
filter_biome = "only", #Only in Atlantic Forest
state = c("PR", "SC", "RS"), #states - Use the acronynms
filter_state = "and", #PR and SC and RS
vegetation = "All", filter_vegetation = "in",
endemism = "Endemic", #Only endemics to Brazil
origin = "Native", #Only natives
taxonomicStatus = "Accepted",
nomenclaturalStatus = "All")
nrow(south_af_spp2)
#> [1] 29
#All unique states in the filtered dataset
unique(south_af_spp2$states)
#> [1] "PR;RS;SC" "MG;PR;RS;SC;SP" "PR;RS;SC;SP" "ES;PR;RS;SC;SP"
#> [5] "MG;PR;RS;SC"Now, our selection consists solely of species with confirmed occurrences in all of the specified states. However, by utilizing the “and” argument, we permit the filter to include species with occurrences in additional states. To confine the filter exclusively to species with confirmed occurrences in all three states, without any occurrences elsewhere, we can use filter_state = “only”:
south_af_spp3 <- select_species(data = bf,
include_subspecies = FALSE, include_variety = FALSE,
kingdom = "Plantae", group = "All", subgroup = "All",
family = "All", genus = "All",
lifeForm = "Tree", #Specify tree species
filter_lifeForm = "in",
habitat = "All", filter_habitat = "in",
biome = "Atlantic_Forest", #Occuring in the At. Forest
filter_biome = "only", #Only in Atlantic Forest
state = c("PR", "SC", "RS"), #states - Use the acronynms
filter_state = "only", #PR and SC and RS, no other else
vegetation = "All", filter_vegetation = "in",
endemism = "Endemic", #Only endemics to Brazil
origin = "Native", #Only natives
taxonomicStatus = "Accepted",
nomenclaturalStatus = "All")
nrow(south_af_spp3)
#> [1] 13
#The unique state in the filtered dataset
unique(south_af_spp3$states)
#> [1] "PR;RS;SC"Now, the filter return only 13 species, all of them occurring in PR, SC and RS; with no recorded occurrences in any other states.
In addition to selecting a species list based on their characteristics, the package also includes a function for subsetting species by name. Please note that this function exclusively operates with binomial names (Genus + specificEpithet), such as Araucaria angustifolia, and does not support complete scientific names (including Author or infraspecificEpithet), such as Araucaria angustifolia (Bertol.) Kuntze or Araucaria angustifolia var. stricta Reitz.
If you have species with complete scientific names, you can extract
the binomial names using the function get_binomial(). Don’t
worry about excessive spaces between words; the function will remove any
extra spaces from the names.
complete_names <- c("Araucaria brasiliana var. ridolfiana (Pi.Savi) Gordon",
" Solanum restingae S.Knapp",
"Butia cattarinensis Noblick & Lorenzi ",
"Homo sapiens")
#Human specie was used just as an example that will be used to illustrate the
#next function
binomial_names <- get_binomial(species_names = complete_names)
binomial_names
#> [1] "Araucaria brasiliana" "Solanum restingae" "Butia cattarinensis"
#> [4] "Homo sapiens"Additionally, you can verify the spelling, nomenclatural status, and taxonomic status of species names using the check_names() function. If the function is unable to locate the name of a species in the database (due to a typo, for example), it can suggest potential names based on similarities to other entries in the database. To see how the function works, let’s utilize the previously created binomial_names dataset:
#Create example
checked_names <- check_names(data = bf,
species = binomial_names,
max_distance = 0.1,
kingdom = "Plantae")
checked_names
# input_name Spelling Suggested_name Distance taxonomicStatus nomenclaturalStatus acceptedName family
# 1 Araucaria brasiliana Correct Araucaria brasiliana 0 Synonym <NA> Araucaria angustifolia Araucariaceae
# 2 Araucaria brasiliana Correct Araucaria brasiliana 0 Synonym Illegitimate Araucaria angustifolia Araucariaceae
# 3 Solanum restingae Correct Solanum restingae 0 Accepted Correct Solanum restingae Solanaceae
# 4 Butia cattarinensis Probably_incorrect Butia catarinensis 1 Accepted <NA> Butia catarinensis Arecaceae
# 5 Homo sapiens Not_found <NA> NA <NA> <NA> <NA> <NA>We can see that Araucaria brasiliana is spelling correctly, but it is a synonym of Araucaria angustifolia. Solanum restingae is spelling correctly and it is an accepted name. In the case of Butia cattarinensis, the spelling appears to be potentially incorrect (as the name wasn’t found in the database); however, a similar name, Butia catarinensis, is suggested by the function. The ‘Distance’ column indicates the Levenshtein edit distance between the input and the suggested name. The spelling of Homo sapiens was flagged as Not_found (as expected, given that Homo sapiens is not a plant!). Consequently, the name was not located in the database, and there were no comparable names available.
To retrieve species information from the Flora e Funga do Brasil
database, employ the subset_species() function. For optimal
performance, we highly recommend utilizing the
get_binomial() and check_names() functions
beforehand. This ensures that you’re exclusively working with species
present in the Flora e Funga do Brasil database. To see how the function
works, let’s use the accepted names in checked_names created
previously:
#Get only accepted names
accepted_names <- unique(checked_names$acceptedName)
accepted_names <- na.omit(accepted_names) #Remove NA
#Subset species
my_sp <- subset_species(data = bf, species = accepted_names,
include_subspecies = FALSE,
include_variety = FALSE,
kingdom = "Plantae")
my_sp
#> species scientificName
#> 11785 Solanum restingae Solanum restingae S.Knapp
#> 26790 Araucaria angustifolia Araucaria angustifolia (Bertol.) Kuntze
#> 99881 Butia catarinensis Butia catarinensis Noblick & Lorenzi
#> acceptedName kingdom Group Subgroup family genus
#> 11785 <NA> Plantae Angiosperms <NA> Solanaceae Solanum
#> 26790 <NA> Plantae Gymnosperms <NA> Araucariaceae Araucaria
#> 99881 <NA> Plantae Angiosperms <NA> Arecaceae Butia
#> lifeForm habitat Biome States
#> 11785 Shrub Terrestrial Atlantic_Forest BA
#> 26790 Tree Terrestrial Atlantic_Forest;Pampa MG;PR;RJ;RS;SC;SP
#> 99881 Palm_tree Terrestrial Atlantic_Forest;Pampa RS;SC
#> vegetationType
#> 11785 Restinga
#> 26790 High_Altitude_Grassland;Mixed_Ombrophyllous_Forest;
#> Seasonally_Semideciduous_Forest
#> 99881 Restinga
#> origin endemism taxonomicStatus nomenclaturalStatus
#> 11785 Native Endemic Accepted Correct
#> 26790 Native Non-endemic Accepted Correct
#> 99881 Native Endemic Accepted <NA>
#> vernacularName
#> 11785 <NA>
#> 26790 araucaria, pinheiro-do-parana, curi, pinheiro-brasileiro,
#> pinho-do-parana
#> 99881 <NA>
#> taxonRank
#> 11785 Species
#> 26790 Species
#> 99881 SpeciesWe can also include subspecies and/or varieties:
my_sp2 <- subset_species(data = bf, species = accepted_names,
include_subspecies = TRUE,
include_variety = TRUE,
kingdom = "Plantae")
my_sp2[1:5,]
#> species scientificName
#> 11785 Solanum restingae Solanum restingae S.Knapp
#> 26790 Araucaria angustifolia Araucaria angustifolia (Bertol.) Kuntze
#> 35204 Araucaria angustifolia Araucaria angustifolia var. alba Reitz
#> 35205 Araucaria angustifolia Araucaria angustifolia var. caiova Reitz
#> 35206 Araucaria angustifolia Araucaria angustifolia var. caiuva Mattos
#> acceptedName kingdom Group Subgroup family
#> 11785 <NA> Plantae Angiosperms <NA> Solanaceae
#> 26790 <NA> Plantae Gymnosperms <NA> Araucariaceae
#> 35204 Araucaria angustifolia Plantae Gymnosperms <NA> Araucariaceae
#> 35205 Araucaria angustifolia Plantae Gymnosperms <NA> Araucariaceae
#> 35206 Araucaria angustifolia Plantae Gymnosperms <NA> Araucariaceae
#> genus lifeForm habitat Biome States
#> 11785 Solanum Shrub Terrestrial Atlantic_Forest BA
#> 26790 Araucaria Tree Terrestrial Atlantic_Forest;Pampa MG;PR;RJ;RS;SC;SP
#> 35204 Araucaria
#> 35205 Araucaria
#> 35206 Araucaria
#> vegetationType
#> 11785 Restinga
#> 26790 High_Altitude_Grassland;Mixed_Ombrophyllous_Forest;
#> Seasonally_Semideciduous_Forest
#> 35204
#> 35205
#> 35206
#> origin endemism taxonomicStatus nomenclaturalStatus
#> 11785 Native Endemic Accepted Correct
#> 26790 Native Non-endemic Accepted Correct
#> 35204 <NA> <NA> Synonym <NA>
#> 35205 <NA> <NA> Synonym <NA>
#> 35206 <NA> <NA> Synonym <NA>
#> vernacularName
#> 11785 <NA>
#> 26790 araucaria, pinheiro-do-parana, curi, pinheiro-brasileiro,
#> pinho-do-parana
#> 35204 <NA>
#> 35205 <NA>
#> 35206 <NA>
#> taxonRank
#> 11785 Species
#> 26790 Species
#> 35204 Variety
#> 35205 Variety
#> 35206 VarietyWe can retrieve all synonyms of a species list. This can be particularly useful, for example, when searching for records of a species and all its synonyms (as listed in the Flora e Funga do Brasil) in online databases like GBIF. To accomplish this, utilize the function get_synonym. To understand how the function works, let’s search for the synonyms of two species:
spp <- c("Araucaria angustifolia", "Adesmia paranensis")
spp_syn <- get_synonym(data = bf, species = spp)
spp_syn
#> synonym acceptedName taxonomicStatus nomenclaturalStatus
#> 35323 Araucaria brasiliana Araucaria angustifolia Synonym <NA>
#> 35325 Araucaria brasiliensis Araucaria angustifolia Synonym <NA>
#> 35327 Araucaria dioica Araucaria angustifolia Synonym <NA>
#> 35328 Araucaria elegans Araucaria angustifolia Synonym <NA>
#> 35329 Araucaria ridolfiana Araucaria angustifolia Synonym <NA>
#> 35330 Araucaria saviana Araucaria angustifolia Synonym <NA>
#> 35332 Columbea angustifolia Araucaria angustifolia Synonym Legitimate_but_incorrect
#> 35333 Columbea brasiliana Araucaria angustifolia Synonym Legitimate_but_incorrect
#> 60644 Pinus dioica Araucaria angustifolia Synonym <NA>
#> 141020 Araucaria bibbiani Araucaria angustifolia Synonym <NA>
#> 141021 Araucaria lindleyana Araucaria angustifolia Synonym <NA>
#> 141041 Araucaria brasiliana Araucaria angustifolia Synonym Illegitimate
#> 85308 Adesmia psoraleoides Adesmia paranensis Synonym Legitimate_but_incorrectWe can see that Araucaria angustifolia has 12 synonyms in Flora e Funga do Brasil, while Adesmia paranensis has one synonym.