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03_TaxonomicBackbone.Rmd
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03_TaxonomicBackbone.Rmd 100.79 KiB
- Data preparation
- Load packages
- Read in taxon names from sPlot and TRY
- Import lists of species classified into groups
- Combine species lists
- A-priori cleaning of names
- Manual cleaning
- Match names against Taxonomic Name Resolution Service (TNRS)
- TNRS settings {#ID}
- Sources for name resolution {#ID}
- Family Classification
- Retrieve results
- General procedure {#ID}
- Iteration 1 - Read and combine TNRS result files
- Select best match for each submitted name
- Family level {#ID}
- Genus level
- Species level
- Subspecies level
- Identifying "non-matched" species that are spermatophyta
- Select certain or uncertain names
- Delete subspecies information and rerun match in TNRS
- Save species list to submit to TNRS for iteration 2
- Iteration 2 - Reimport resolved species names from TNRS and mark solved
- Family level
- Genus level
- Species level
- Subspecies level
- Save species list to submit to TNRS for iteration 3
- Iteration 3 - Reimport resolved species names from TNRS_NCBI
- Family level
- Genus level
- Species level
- Variety level
- Select certain or uncertain names
- Iteration 4 - Using The Plant List matching tools for unresolved names
- Merge the resolved species lists into a Backbone
- Read files
- Tag unresolved names and create output columns
- Complete list of families
- Resolve genera with missing family info with TNRS
- Complement with data from TRY 5.0
- Complement with data from The Catalogue of Life
- Manually fix some known issues
- Derive info from other species of the same Genera in the Backbone itself
- Delete records assigned to mushroom families, if any
- Create field Name_short
- Create field is_vascular_plant and Taxon group
- Export Backbone
- Description of fields in the Backbone
- Export species list to request in TRY
- Check how many species from sPlot where submitted to TRY5.0
- Statistics
- Statistics for backbone combining names in sPlot3.0 and TRY5.0
- All taxon name entries
- Based on unique standardized names
- Stats for the corrected names in sPlot only:
- Appendix
- Create lists of genera manually classified into taxonomic groups
- R-settings
title: "Taxonomic Backbone - sPlot 3.0"
author: "Francesco Maria Sabatini"
date: "`r format(Sys.time(), '%d %B, %Y')`"
tags:
- database
- big data
- traits
- taxonomy
output:
html_document:
number_sections: true
toc: true
toc_depth: 2
abstract: "This document describes the workflow (with contributions from Oliver Purschke, Jürgen Dengler and Florian Jansen) that was used to generate the taxonomic backbone that standardizes taxon names across the (i) global vegetation plot database sPlot version 3.0 and (ii) the global plant trait data base TRY version 5. "
urlcolor: blueTimestamp: r date()
Drafted: Francesco Maria Sabatini
Revised: Helge Bruelheide, Borja Jimenez-Alfaro
Version: 1.3
Changes to Version 1.1 Additional manual cleaning of species names from BJA, UJ and HB.
Changes to Version 1.2 Changed order of ranking TNRS databases, when a name is matched across more than 1 DB; Using cleaned version of DT table (after stripping non-closed quotation marks). Additionally check with TPL those species that, even if resolved in TNRS, did not return an accepted name.
Changes to Version 1.3 Manual check of names BEFORE matching with TNRS
Data preparation
Load packages
library(reshape2)
library(tidyverse)
library(readr)
library(data.table)
library(knitr)
library(kableExtra)
library(stringr)
library(taxize)
library(Taxonstand)
library(vegdata)Read in taxon names from sPlot and TRY
#import and save splot names from DT table
DT0 <- readr::read_delim("../sPlot_data_export/sPlot_3_0_2_species_test.csv",
delim="\t",
col_type = cols(
PlotObservationID = col_double(),
Taxonomy = col_character(),
`Taxon group` = col_character(),
`Taxon group ID` = col_double(),
`Turboveg2 concept` = col_character(),
`Matched concept` = col_character(),
Match = col_double(),
Layer = col_double(),
`Cover %` = col_double(),
`Cover code` = col_character(),
x_ = col_double()
)
) Import lists of species classified into groups
These objects are defined in the appendix
load("../_derived/taxa_manual.RData")splot.species <- DT0 %>%
rename(Species.original=`Turboveg2 concept`, Matched.concept=`Matched concept`) %>%
filter(`Taxon group`!="Mushroom") %>%
dplyr::select(Species.original, Matched.concept) %>%
distinct() %>%
group_by(Matched.concept) %>%
mutate(fungi= word(Matched.concept, 1) %in% mushroom) %>%
ungroup() %>%
filter(fungi==F) %>%
dplyr::select(Species.original, Matched.concept)
write_csv(splot.species, path = "../_derived/splot3.0.2.species.csv")!!! I used the column from TRY with the full species name, not the column with only two-word name strings
splot.species <- read_csv("../_derived/splot3.0.2.species.csv")
try.species <- readr::read_csv("../_input/AccSpecies_TRY5.csv", col_names = F, locale = locale(encoding = 'Latin1')) %>%
dplyr::select(-X6, -X7) %>%
rename(try.ID=X1, FullSpecies=X2, Species=X3, Genus=X4, Family=X5, GrowthForm=X8)
# Sneak in species from the Alpine database (Borja & Riccardo), as a courtesy to Project #18
alpine.species <- read_delim("../_input/new_alpine_species.txt", col_names = F, delim = "\t", locale = locale(encoding = 'Latin1')) %>%
rename(Species=X1)Use the Matched.concept column, as it already contains some standardization by Stephan Hennekkens according to synbiosys.
sPlot 3.0.1 contains r nrow(unique(splot.species[,2])) different species names.
TRY 5. contains r nrow(try.species).
I add to this a list of r nrow(alpine.species) alpine species delivered from Riccardo Testolin, within sPlot Project #18.
Combine species lists
spec.list.TRY.sPlot <- splot.species %>%
dplyr::select(Matched.concept) %>%
rename(Species=Matched.concept) %>%
mutate(Source="S") %>%
bind_rows(try.species %>%
dplyr::select(FullSpecies) %>% ##using the full name from TRY
rename(Species=FullSpecies) %>%
mutate(Source="T")) %>%
bind_rows(alpine.species %>%
mutate(Source="A")) %>%
reshape2::dcast(Species ~ Source) %>%
mutate(A=ifelse(A>=1, "A", "")) %>%
mutate(S=ifelse(S>=1, "S", "")) %>%
mutate(T=ifelse(T>=1, "T", "")) %>%
mutate(Source=paste(S, T, A, sep="")) %>%
dplyr::select(-A, -S, -T)
#Number of species unique and in common across databasesThe total number of species in the backbone is r nrow(spec.list.TRY.sPlot).
knitr::kable(spec.list.TRY.sPlot %>%
mutate(Source=factor(Source,
levels=c("S", "T", "A", "ST", "SA", "TA", "STA"),
labels=c("sPlot only", "TRY only", "Alpine only",
"sPlot + TRY", "sPlot + Alpine", "TRY + Alpine",
"sPlot + TRY + Alpine"))) %>%
group_by(Source) %>%
summarize(Num.taxa=n()),
caption="Number of taxa per database") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")A-priori cleaning of names
Stripping unwanted characters as well as abbreviation (such as hybrid markers) which would prevent name matching:
#Ancillary function to change to lower case
firstup <- function(x) {
substr(x, 1, 1) <- toupper(substr(x, 1, 1))
x
}
spec.list.TRY.sPlot <- spec.list.TRY.sPlot %>%
mutate(OriginalNames=Species) %>%
mutate(Species=tolower(Species)) %>%
mutate(Species=firstup(Species)) %>%
dplyr::select(OriginalNames, Species, Source) %>%
mutate(Species=gsub('*', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub('cf. ', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub('Cf. ', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub('[', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub(']', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub(' x ', ' ', Species, fixed=TRUE)) %>%
mutate(Species=gsub('×', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub('aff ', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub('(', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub(')', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub(' cf ', ' ', Species, fixed=TRUE)) %>%
mutate(Species=gsub(' aff. ', ' ', Species, fixed=TRUE)) %>%
mutate(Species=gsub('c‚e', 'ceae', Species, fixed=TRUE)) %>%
mutate(Species=gsub(' ', ' ', Species, fixed=TRUE)) %>%
mutate(Species=gsub(' ', ' ', Species, fixed=TRUE)) %>%
mutate(Species=gsub(' ', ' ', Species, fixed=TRUE)) %>%
mutate(Species=gsub('x-', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub('X-', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub('×', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub('like ', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub(',', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub('#', '', Species, fixed=TRUE)) %>%
mutate(Species=gsub('_', ' ', Species))For all names, that have a number in their first word, and consist of > 1 words, remove that word:
spec.list.TRY.sPlot <- spec.list.TRY.sPlot %>%
mutate(firstWordWithNumbers=grepl('[0-9]', word(Species, 1))) %>%
mutate(numberOfWords= sapply(gregexpr("\\W+", Species), length) + 1) %>%
mutate(Species=ifelse((firstWordWithNumbers & numberOfWords > 1),
sapply(Species,
function(x) substr(x, start=regexpr(pattern =' ', text=x)+1,
stop=nchar(x))), Species))Correct some name abbreviations using taxname.abbr in vegdata:
spec.list.TRY.sPlot <- spec.list.TRY.sPlot %>%
mutate(Species=taxname.abbr(spec.list.TRY.sPlot$Species)) %>%
dplyr::select(OriginalNames, Species, Source) %>%
distinct()Manual cleaning
Fix known issues in some species names
#Manual cleaning
spec.list.TRY.sPlot <- spec.list.TRY.sPlot %>%
mutate(Species=tolower(Species)) %>%
mutate(Species=firstup(Species)) %>%
mutate(Species=gsub("^Str ", "", Species)) %>%
mutate(Species=gsub("^Unknown ", "", Species)) %>%
mutate(Species=firstup(Species)) %>%
mutate(Species=gsub(" [0-9]*$", "", Species)) %>% #delete digits at end of object
mutate(Species=gsub("^\\d+|\\d+$", "", Species)) %>% #delete digits at the beginning or end of a string
mutate(Species=gsub(" sp.$", "", Species)) %>%
mutate(Species=gsub(" sp$", "", Species)) %>%
mutate(Species=gsub(" species$", "", Species)) %>%
mutate(Species=gsub(" *$", "", Species)) %>%
mutate(Species=gsub(" #$", "", Species)) %>%
mutate(Species=gsub(" m$", "", Species)) %>%
mutate(Species=gsub("acea ", "aceae ", Species)) %>%
mutate(Species=gsub('^Agropyrum', 'Agropyron', Species)) %>%
mutate(Species=gsub('^Anno ', 'Annona ', Species)) %>%
mutate(Species=gsub('Adpdytes dimidiata', 'Apodytes dimidiata', Species)) %>%
mutate(Species=gsub('Adenostorna fasciculaturn', 'Adenostoma fasciculatum', Species)) %>%
mutate(Species=gsub('Arctostapliylos glallca', 'Arctostaphylos glauca', Species)) %>%
mutate(Species=gsub('Bituminosa bituminosa', 'Bituminaria bituminosa', Species)) %>%
mutate(Species=gsub('Causurina equisitifolia', 'Causuarina equisetifolia', Species)) %>%
mutate(Species=gsub('Convulvus arvensis', 'Convolvulus arvensis', Species)) %>%
mutate(Species=gsub('Diospyrus dygina', 'Diospyros dygina', Species)) %>%
mutate(Species=gsub('^Dodoea', 'Dodonaea', Species)) %>%
mutate(Species=gsub('^Boheravia', 'Boerhavia', Species)) %>%
mutate(Species=gsub('Centaria maculosa', 'Centaurea maculosa', Species)) %>%
mutate(Species=gsub('Chamrenerium angustifolium', 'Chamaenerion angustifolium', Species)) %>%
mutate(Species=gsub('^Chicorium', 'Cichorium', Species)) %>%
mutate(Species=gsub('^Cirsiumum', 'Cirsium', Species)) %>%
mutate(Species=gsub('^Colubrium', 'Colubrina', Species)) %>%
mutate(Species=gsub('^Corymbium', 'Corymbia', Species)) %>%
mutate(Species=gsub('Cosmos bipinnata', 'Cosmos bipinnatus', Species)) %>%
mutate(Species=gsub('Diospyrus dygina', 'Diospyros digyna', Species)) %>%
mutate(Species=gsub('Diospyros egbert', 'Diospyros egbert-walkeri', Species)) %>%
mutate(Species=gsub('Dispyrus halesioides', 'Diospyros halesioides', Species)) %>%
mutate(Species=gsub('^Drymis', 'Drimys', Species)) %>%
mutate(Species=gsub('^Dysoxylon', 'Dysoxylum', Species)) %>%
mutate(Species=gsub('^Eleaegnus', 'Elaeagnus', Species)) %>%
mutate(Species=gsub('^Eleutherant', 'Eleutherantera', Species)) %>%
mutate(Species=gsub('^Echicea', 'Echinacea', Species)) %>%
mutate(Species=gsub('Gauteria foliolata', 'Gaultheria foliolosa', Species)) %>%
mutate(Species=gsub('^Geophylla', 'Geophyla', Species)) %>%
mutate(Species=gsub('Gloichidion insignis', 'Glochidion insigne', Species)) %>%
mutate(Species=gsub('^Glycium', 'Glycine', Species)) %>%
mutate(Species=gsub('^Hammalis', 'Hamamelis', Species)) %>%
mutate(Species=gsub('^Hippochoeris', 'Hypochaeris', Species)) %>%
mutate(Species=gsub('Ilix tephrohylla', 'Ilex tephrophylla', Species)) %>%
mutate(Species=gsub('^Jasininum', 'Jasminum', Species)) %>%
mutate(Species=gsub('Jenipa conjuta', 'Jenipa conjunta', Species)) %>%
mutate(Species=gsub('^Lechytis', 'Lecythis', Species)) %>%
mutate(Species=gsub('Lespedeza juncus', 'Lespedeza juncea', Species)) %>%
mutate(Species=gsub('Licania apelata', 'Licania apetala', Species)) %>%
mutate(Species=gsub('Limeum arenicola', 'Limeum arenicolum', Species)) %>%
mutate(Species=gsub('^Maniota', 'Manihot', Species)) %>%
mutate(Species=gsub('^Menta', 'Mentha', Species)) %>%
mutate(Species=gsub('Metophyum brownei', 'Metopium brownei', Species)) %>%
mutate(Species=gsub('Miliusa tomentosum', 'Miliusa tomentosa', Species)) %>%
mutate(Species=gsub('Mimululus ringens', 'Mimulus ringens', Species)) %>%
mutate(Species=gsub('Nardus strictus', 'Nardus stricta', Species)) %>%
mutate(Species=gsub('Neea glomeratha', 'Neea glomerata', Species)) %>%
mutate(Species=gsub('^Onopordon', 'Onopordum', Species)) %>%
mutate(Species=gsub('^Orbigynia', 'Orbignya', Species)) %>%
mutate(Species=gsub('Orites excelsa', 'Orites excelsus', Species)) %>%
mutate(Species=gsub('Paedorata lutea', 'Paederota lutea', Species)) %>%
mutate(Species=gsub('Palaquin ellipticum', 'Palaquium ellipticum', Species)) %>%
mutate(Species=gsub('Palmeria arfakensis', 'Palmeria arfakiana', Species)) %>%
mutate(Species=gsub('Petalostcmum purpureum', 'Petalostemum purpureum', Species)) %>%
mutate(Species=gsub('Petalostimum purpureum', 'Petalostemum purpureum', Species)) %>%
mutate(Species=gsub('^Petrosileum', 'Petroselinum', Species)) %>%
mutate(Species=gsub('Phlomis herba', 'Phlomis herba-venti', Species)) %>%
mutate(Species=gsub('^Phyllirea', 'Phillyrea', Species)) %>%
mutate(Species=gsub('Physilus pumula', 'Physalus pumila', Species)) %>%
mutate(Species=gsub('Picea maria', 'Picea mariana', Species)) %>%
mutate(Species=gsub('Picea retroXexa', 'Picea retroflexa', Species)) %>%
mutate(Species=gsub('Pilayella litoralis', 'Pilayella littoralis', Species)) %>%
mutate(Species=gsub('Placocarpus schaereri', 'Platecarpus schaerer', Species)) %>%
mutate(Species=gsub('Placocarpus schraereri', 'Platecarpus schaerer', Species)) %>%
mutate(Species=gsub('^Pulteea', 'Pultenaea', Species)) %>%
mutate(Species=gsub('Quercus rubrum', 'Quercus rubra', Species)) %>%
mutate(Species=gsub('Rubus fruticosa', 'Rubus fruticosus', Species)) %>%
mutate(Species=gsub('Rubus saxatile', 'Rubus saxatilis', Species)) %>%
mutate(Species=gsub('Rubus sylvatici', 'Rubus sylvaticus', Species)) %>%
mutate(Species=gsub('^Sanguiria', 'Sanguinaria', Species)) %>%
mutate(Species=gsub('Sarauja nepaulensis', 'Sarauja nepalensis', Species)) %>%
mutate(Species=gsub('^Sateria', 'Setaria', Species)) %>%
mutate(Species=gsub('Sauraiea nepulensis', 'Saurauia nepalensis', Species)) %>%
mutate(Species=gsub('Schneckia australis', 'Schenckia australis', Species)) %>%
mutate(Species=gsub('Smirnium oleastrum', 'Smyrnium olusatrum', Species)) %>%
mutate(Species=gsub('Solms laubachia', 'Solms-laubachia himalayensis', Species)) %>%
mutate(Species=gsub('Stellaria chamaejasme', 'Stellera chamaejasme', Species)) %>%
mutate(Species=gsub('Steraria parviflora', 'Setaria parviflora', Species)) %>%
mutate(Species=gsub('^Stuartia', 'Stewartia', Species)) %>%
mutate(Species=gsub('Sycops sinensis', 'Sycopsis sinensis', Species)) %>%
mutate(Species=gsub('Tacetum vulgare', 'Tanacetum vulgare', Species)) %>%
mutate(Species=gsub('Talinurn angustissimun', 'Talinun angustissimun', Species)) %>%
mutate(Species=gsub('Talloma hodgsoni', 'Talauma hodgsonii', Species)) %>%
mutate(Species=gsub('Taraxacum albo', 'Taraxacum album', Species)) %>%
mutate(Species=gsub('Tetragonia falcata', 'Tetragona falcata', Species)) %>%
mutate(Species=gsub('Trapogogon', 'Tragopogon', Species)) %>%
mutate(Species=gsub('Zyzyphus saeri', 'Zizyphus saeri', Species)) %>%
mutate(Species=gsub('^Helicrysum', 'Helichrysum', Species)) %>%
mutate(Species=gsub('^Diceropappus rhinocerotis', 'Elytropappus rhinocerotis', Species)) %>%
mutate(Species=gsub('^Euphorbiace ', 'Euphorbiacaea ', Species)) %>%
mutate(Species=gsub('^Gloecapsa', 'Gloeocapsa', Species)) %>%
mutate(Species=gsub('Glycirhiza', 'Glycyrrhiza', Species)) %>%
mutate(Species=gsub('Abiesnordmannia', 'Abies nordmannia', Species)) %>%
mutate(Species=gsub('Alnus inca', 'Alnus incana', Species)) %>%
mutate(Species=gsub('Amalencier alnifolia', 'Amalenchier alnifolia', Species)) %>%
mutate(Species=gsub('Antylis barba-jovis', 'Anthyllis barba-jovis', Species)) %>%
mutate(Species=gsub('^Albizzia "', 'Albizia ', Species)) %>%
mutate(Species=gsub('^Ipomoena ', 'Ipomoea ', Species)) %>%
mutate(Species=gsub('^Ipomea ', 'Ipomoea ', Species)) %>%
mutate(Species=gsub('Ipomo wolco', 'Ipomoea wolcottiana', Species)) %>%
## additional manual cleaning from UJ, BJA, HB
mutate(Species=gsub('Abacaba palm', 'Oenocarpus balickii', Species)) %>%
mutate(Species=gsub('Acerkuomeii', 'Acer kuomeii', Species)) %>%
mutate(Species=gsub('Alder$', 'Alnus', Species)) %>%
mutate(Species=gsub('Amapa$', 'Tabebuia', Species)) %>%
mutate(Species=gsub('Amapa amargoso', 'Parahancornia amapa', Species)) %>%
mutate(Species=gsub('Amapa doce$', 'Tabebuia', Species)) %>%
mutate(Species=gsub('Amapai$', 'Tabebuia', Species)) %>%
mutate(Species=gsub('Amapaí$', 'Tabebuia', Species)) %>%
mutate(Species=gsub('Amapa m1', 'Tabebuia', Species)) %>%
mutate(Species=gsub('Amaranth$', 'Amaranthus', Species)) %>%
mutate(Species=gsub('Amophora fruticosa', 'Amorpha fruticosa', Species)) %>%
mutate(Species=gsub('Anacardiace ', 'Anacardiaceae ', Species)) %>%
mutate(Species=gsub('Anagallisarvensis', 'Anagallis arvensis', Species)) %>%
mutate(Species=gsub('Anemonenarcissiflora var.', 'Anemone narcissiflora', Species)) %>%
mutate(Species=gsub('Anenome ', 'Anemone', Species)) %>%
mutate(Species=gsub('Anona ', 'Annona ', Species)) %>%
mutate(Species=gsub('Antylis ', 'Anthyllis', Species)) %>%
mutate(Species=gsub('Apocyncadea gelbblueh$', 'Apocynaceae', Species)) %>%
mutate(Species=gsub('Aracium', 'Crepis', Species)) %>%
mutate(Species=gsub('Ardis mexic', 'Ardisia mexicana subsp. siltepecana', Species)) %>%
mutate(Species=gsub('Ardis verap', 'Ardisia verapazensis', Species)) %>%
mutate(Species=gsub('Argenomne hummemannii', 'Argemone hunnemanni', Species)) %>%
mutate(Species=gsub('Artabotus', 'Artabotrys', Species)) %>%
mutate(Species=gsub('Artemisiaintegrifolia', 'Artemisia integrifolia', Species)) %>%
mutate(Species=gsub('Asclepiacea$', 'Asclepiadaceae', Species)) %>%
mutate(Species=gsub('Asclep. klimmer', 'Asclepiadaceae', Species)) %>%
mutate(Species=gsub('Astartoseris triquetra', 'Lactuca triquetra', Species)) %>%
mutate(Species=gsub('Asteracee ', 'Asteraceae ', Species)) %>%
mutate(Species=gsub('Avenula glauc$', 'Avenula', Species)) %>%
mutate(Species=gsub('Baikea plurijuga', 'Baikiaea plurijuga', Species)) %>%
mutate(Species=gsub('Binse rundbl', 'Juncaceae', Species)) %>%
mutate(Species=gsub('Blättrige fabaceae th', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Bonel macro$', 'Bonellia macrocarpa subsp. macrocarpa', Species)) %>%
mutate(Species=gsub('Boraginacee samtig', 'Boraginaceae', Species)) %>%
mutate(Species=gsub('Bri¢fitos', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Bryophyte$', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Bryopsida', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Carallia macrophylla', 'Carallia', Species)) %>%
mutate(Species=gsub('Carexectabilis', 'Carex spectabilis', Species)) %>%
mutate(Species=gsub('Carex fein', 'Carex', Species)) %>%
mutate(Species=gsub('Cerania vermicularis', 'Thamnolia vermicularis', Species)) %>%
mutate(Species=gsub('Chamelauci merredin', 'Chamelaucium', Species)) %>%
mutate(Species=gsub('Chamelau drummon', 'Chamelaucium', Species)) %>%
mutate(Species=gsub('Charophyta', 'Characeae', Species)) %>%
mutate(Species=gsub('Cheiridopsis-keimlinge', 'Cheiridopsis', Species)) %>%
mutate(Species=gsub('Chenopodiacee$', 'Chenopodiaceae', Species)) %>%
mutate(Species=gsub('Chiangioden mexicanum', 'Chiangiodendron mexicanum', Species)) %>%
mutate(Species=gsub('Chiranthode pentadactylon', 'Chiranthodendron pentadactylon', Species)) %>%
mutate(Species=gsub('Chrysobalan ', 'Chrysobalanus ', Species)) %>%
mutate(Species=gsub('Cladapodiella', 'Cladopodiella', Species)) %>%
mutate(Species=gsub('Cleidium ', 'Cleidion ', Species)) %>%
mutate(Species=gsub('Collema/leptogium lichenoides', 'Collemataceae', Species)) %>%
mutate(Species=gsub('Comarostaph discolor', 'Comarostaphylis discolor', Species)) %>%
mutate(Species=gsub('Combretdodendrum africana', 'Combretodendrum africanum', Species)) %>%
mutate(Species=gsub('Commelinacaea floscopa', 'Floscopa glomerata', Species)) %>%
mutate(Species=gsub('Coyncia setigera', 'Coincya setigera', Species)) %>%
mutate(Species=gsub('Crataeva', 'Crateva', Species)) %>%
mutate(Species=gsub('Craterosperma', 'Rubiaceae', Species)) %>%
mutate(Species=gsub('Crespicium', 'Burseraceae', Species)) %>%
mutate(Species=gsub('Critoniadel nubigenus', 'Critoniadelphus nubigenus', Species)) %>%
mutate(Species=gsub('Crotalaria/vigna?', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Croto billb', 'Croton billbergianus subsp. pyramidalis', Species)) %>%
mutate(Species=gsub('Dana„ racemosa', 'Danae racemosa', Species)) %>%
mutate(Species=gsub('Deehasia', 'Dehaasia', Species)) %>%
mutate(Species=gsub('Dichapetala', 'Dichapetalum', Species)) %>%
mutate(Species=gsub('Distel bractea', 'Asteracaea', Species)) %>%
mutate(Species=gsub('Distelig asteraceae', 'Asteracaea', Species)) %>%
mutate(Species=gsub('Dodon visco', 'Dodonaea viscosa', Species)) %>%
mutate(Species=gsub('Doldenbluetler', 'Apiaceae', Species)) %>%
mutate(Species=gsub('Echinosurus capitatus', 'Poaceae', Species)) %>%
mutate(Species=gsub('Einähriges gras$', 'Poaceae', Species)) %>%
mutate(Species=gsub('Einähriges gras von gestern$', 'Poaceae', Species)) %>%
mutate(Species=gsub('Einblütiges rispengras', 'Poaceae', Species)) %>%
mutate(Species=gsub('Eiovaltrichtergrundblatt orchidee', 'Orchidaceae', Species)) %>%
mutate(Species=gsub('Elongata subsp.', 'Pohlia elongata', Species)) %>%
mutate(Species=gsub('Enriquebelt ', 'Enriquebeltrania ', Species)) %>%
mutate(Species=gsub('Entermorpha ', 'Enteromorpha ', Species)) %>%
mutate(Species=gsub('Erodiurn$', 'Erodium', Species)) %>%
mutate(Species=gsub('Euc. chloroclada x camaldulensis', 'Eucalyptus', Species)) %>%
mutate(Species=gsub('Euphorbiacée ipatouduluga gouduatché', 'Euphorbiaceae', Species)) %>%
mutate(Species=gsub('Fabacee kleeblatt stengel schwarzdrüsi', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Fabaceenstrauch wie 132446 f', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Fabaceenstr kleinbltrg', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Fabacee wie lotus f', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Farn', 'Pteridophyta', Species)) %>%
mutate(Species=gsub('Farn cystopteris', 'Cystopteris', Species)) %>%
mutate(Species=gsub('Fern', 'Pteridophyta', Species)) %>%
mutate(Species=replace(Species, list=word(Species, 1)=="Fingergras", values="Digitaria")) %>%
mutate(Species=replace(Species, list=word(Species, 1)=="Fingerhirse", values="Digitaria")) %>%
mutate(Species=gsub('Gelbe onagraceae', 'Onagraceae', Species)) %>%
mutate(Species=gsub('Gramine', 'Poaceae', Species)) %>%
mutate(Species=gsub('Graminea', 'Poaceae', Species)) %>%
mutate(Species=gsub('Graminia', 'Poaceae', Species)) %>%
mutate(Species=gsub('Grannenquecke', 'Poaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Gras",
values="Poaceae")) %>%
mutate(Species=gsub('Gynostachi dicanthus', 'Gymnostachium diacanthus', Species)) %>%
mutate(Species=gsub('Hafer haarkranz', 'Poaceae', Species)) %>%
mutate(Species=gsub('Hapolosiphon', 'Hapalosiphon', Species)) %>%
mutate(Species=gsub('Heliocrysum', 'Helichrysum', Species)) %>%
mutate(Species=replace(Species, list=word(Species, 1)=="Hepaticae", values="Bryophyta")) %>%
mutate(Species=gsub('Hepaticas', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Hepatophyta', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Hermerocalis', 'Hemerocallis', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Hirse",
values="Poaceae")) %>%
mutate(Species=gsub('Hirte trian', 'Hirtella triandra subsp. media', Species)) %>%
mutate(Species=replace(Species, list=word(Species, 1)=="Hohlzahn", values="Lamiaceae")) %>%
mutate(Species=gsub('Hondurodend urceolatum', 'Hondurodendron urceolatum', Species)) %>%
mutate(Species=gsub('Hornklee gelb', 'Fabaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Horstgras",
values="Poaceae")) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Huehnerhirse",
values="Digitaria")) %>%
mutate(Species=gsub('Hydrocoleus lyngbyaceus', 'Hydrocoleum lyngbyaceum', Species)) %>%
mutate(Species=gsub('Hyernima nipensis', 'Hieronyma nipensis', Species)) %>%
mutate(Species=gsub('Hyeronima', 'Hieronyma', Species)) %>%
mutate(Species=gsub('Hypocal angusti', 'Hypocalymma angustifolium', Species)) %>%
mutate(Species=gsub('Hypocalym nambung', 'Hypocalymma', Species)) %>%
mutate(Species=gsub('Hyprium', 'Hypericum', Species)) %>%
mutate(Species=gsub('Igelkolben', 'Sparganium', Species)) %>%
mutate(Species=gsub('Ilexã‚â paraguariensis', 'Ilex', Species)) %>%
mutate(Species=gsub('Ipomea', 'Ipomoea', Species)) %>%
mutate(Species=gsub('Ipomoena', 'Ipomoea', Species)) %>%
mutate(Species=gsub('Jm kürbis stark behaart', 'Cucurbitaceae', Species)) %>%
mutate(Species=gsub('Juncaginacee/triglochin', 'Triglochin', Species)) %>%
mutate(Species=gsub('Juncas', 'Juncus', Species)) %>%
mutate(Species=gsub('Keilblatt cyperus', 'Cyperus', Species)) %>%
mutate(Species=gsub('Khh 3010 polygalacee', 'Polygalaceae', Species)) %>%
mutate(Species=gsub(' Khh 3014 liliacee 3f„ch. kapsel schwarze samen', 'Liliaceae', Species)) %>%
mutate(Species=gsub('Khh 3024 brachiaria', 'Brachiaria', Species)) %>%
mutate(Species=gsub('Khh 3025 liliaceae gelbe blten breite bl„tter', 'Liliaceae', Species)) %>%
mutate(Species=gsub('Khh 3037 ficus', 'Ficus', Species)) %>%
mutate(Species=gsub('Khh 3054 ficus iteophylla miq.', 'Ficus', Species)) %>%
mutate(Species=gsub('Kl. borstgras', 'Poaceae', Species)) %>%
mutate(Species=gsub('Kleine malvaceae', 'Malvaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Kletter",
values="Asteraceae")) %>%
mutate(Species=gsub('Klimmer asclepiadaceae', 'Asclepiadaceae', Species)) %>%
mutate(Species=gsub('Klimmer curcuvitaceae', 'Cucurbitaceae', Species)) %>%
mutate(Species=gsub('Kl. sauergras', 'Cyperaceae', Species)) %>%
mutate(Species=gsub('Knabenkraut gefleckt', 'Orchis', Species)) %>%
mutate(Species=gsub('Knubbelblüt. gras haarkranz vgl f', 'Poaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Koenigskerze",
values="Verbascum")) %>%
mutate(Species=gsub('Kriechgras zynodon', 'Poaceae', Species)) %>%
mutate(Species=gsub('Kürbis', 'Cucurbitaceae', Species)) %>%
mutate(Species=gsub('Lamiaceen strauch', 'Lamiaceae', Species)) %>%
mutate(Species=gsub('Lamiacee orange', 'Lamiaceae', Species)) %>%
mutate(Species=gsub('Lamiales orobanchaceae + phrymaceae + plantaginaceae + scrophulariaceae', 'Orobanchaceae', Species)) %>%
mutate(Species=gsub('Lantanacamara wandelrösschen', 'Lantana camara', Species)) %>%
mutate(Species=gsub('Lasiopeta watheroo k. shepherd & c. wilkins ks', 'Lasiopetalum', Species)) %>%
mutate(Species=gsub('Leg-inderteminada', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Legu 1fiedrig groá schlank', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Legume$', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Leguminosae spgm', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Leguminosea', 'Fabaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Leguminose",
values="Fabaceae")) %>%
mutate(Species=gsub('Leheelo grass', 'Poaceae', Species)) %>%
mutate(Species=gsub('Lepid carra', 'Lepiderema', Species)) %>%
mutate(Species=gsub('Lich caloplaca', 'Caloplaca', Species)) %>%
mutate(Species=gsub('Liliacee', 'Liliaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Lilie",
values="Liliaceae")) %>%
mutate(Species=gsub('Liliengewächs', 'Liliaceae', Species)) %>%
mutate(Species=gsub('Lisea', 'Litsea', Species)) %>%
mutate(Species=gsub('Lisymachia', 'Lysimachia', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Liverwort",
values="Bryophyta")) %>%
mutate(Species=gsub('Livwort', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Lonicerachrysantha', 'Lonicera chrysantha', Species)) %>%
mutate(Species=gsub('Lycoctamnus barbatus', 'Aconitum barbatum', Species)) %>%
mutate(Species=gsub('Lygopus', 'Lycopus', Species)) %>%
mutate(Species=gsub('Maitenus', 'Maytenus', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Malpighiace",
values="Malpighiaceae")) %>%
mutate(Species=gsub('Malpighiales chrysobalanaceae + humiriaceae', 'Malpighiaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Malve",
values="Malvaceae")) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Mammutgras",
values="Poaceae")) %>%
mutate(Species=gsub('Mammutgrass', 'Poaceae', Species)) %>%
mutate(Species=gsub('Maqui guian', 'Maquira guianensis subsp. costaricana', Species)) %>%
mutate(Species=gsub('Marchantiophyta', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Mariana aphylla', 'Maireana aphylla', Species)) %>%
mutate(Species=gsub('Mehrfingeriges ährengras', 'Poaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Melastomata",
values="Melastomataceae")) %>%
mutate(Species=gsub('Mesembr minibl', 'Mesembryanthemum', Species)) %>%
mutate(Species=gsub('Mesostomma kotschyanum', 'Mesostemma kotschyana', Species)) %>%
mutate(Species=gsub('Microhepatics', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Micromeria micrantha', 'Micromeria graeca subsp. micrantha', Species)) %>%
mutate(Species=gsub('Mimose minifiedrig f', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Miniepilobium', 'Epilobium', Species)) %>%
mutate(Species=gsub('Minimargerite', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Miniochna', 'Ochna', Species)) %>%
mutate(Species=gsub('Minischilf 132466 f', 'Poaceae', Species)) %>%
mutate(Species=gsub('Mistletoe', 'Viscum', Species)) %>%
mutate(Species=gsub('Mniaecia', 'Mniaceae', Species)) %>%
mutate(Species=gsub('Molemo', 'Turraea', Species)) %>%
mutate(Species=gsub('Molses', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Momisa pigra', 'Mimosa pigra', Species)) %>%
mutate(Species=gsub('Monandrus squarrosus', 'Cyperus squarrosus', Species)) %>%
mutate(Species=gsub('Monchema debile', 'Monechma debile', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Monochna",
values="Polygalaceae")) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Moos",
values="Bryophyta")) %>%
mutate(Species=gsub('Moospolster grau-grün', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Mortonioden ', 'Mortoniodendron ', Species)) %>%
mutate(Species=gsub('Mos onbekend', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Mossen overige', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Mougetia', 'Mougeotia', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Musci",
values="Bryophyta")) %>%
mutate(Species=gsub('Myciantes', 'Myrcianthes', Species)) %>%
mutate(Species=gsub('Myrciaã‚â pulchra', 'Myrcia pulchra', Species)) %>%
mutate(Species=gsub('Myrcianov.', 'Myrcia', Species, fixed = T)) %>%
mutate(Species=gsub('Myrsi coria', 'Myrsine coriacea', Species)) %>%
mutate(Species=gsub('Myrtaceenstrauch', 'Myrtaceae', Species)) %>%
mutate(Species=gsub('Nachtkerze fru dreispaltig', 'Onagracaee', Species)) %>%
mutate(Species=gsub('Neobartsia crenoloba', 'Bartsia crenoloba', Species)) %>%
mutate(Species=gsub('None$', 'Nonea', Species)) %>%
mutate(Species=gsub('Ocos adenophylla', 'Symplocos adenophylla', Species)) %>%
mutate(Species=gsub('Officinale subsp. group', 'Taraxacum officinale s.l.', Species)) %>%
mutate(Species=gsub('Orch$', 'Orchidaceae', Species)) %>%
mutate(Species=gsub('Orchid', 'Orchidaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Orchidee",
values="Orchidaceae")) %>%
mutate(Species=replace(Species,
list=word(Species, 1) %in% c("Papilonacea", "Papilionacea"),
values="Fabaceae")) %>%
mutate(Species=gsub('Pasania dodoniifolia', 'Lithocarpus dodonaeifolius', Species)) %>%
mutate(Species=gsub('Phoebengmoensis', 'Phoebe hungmoensis', Species)) %>%
mutate(Species=gsub('Picra antid$', 'Picramnia antidesma subsp. fessonia', Species)) %>%
mutate(Species=gsub('Pinopsida', 'Coniferae', Species)) %>%
mutate(Species=gsub('Pisonianov.', 'Pisonia', Species, fixed=T)) %>%
mutate(Species=gsub('Pithecellob ', 'Pithecellobium ', Species)) %>%
mutate(Species=gsub('Pithecocten', 'Pithecoctenium', Species)) %>%
mutate(Species=gsub('Pleradenoph longicuspis', 'Pleradenophora longicuspis', Species)) %>%
mutate(Species=gsub('Pleuranthod ', 'Pleuranthodendron ', Species)) %>%
mutate(Species=gsub('Poales', 'Poaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1) %in% c("Polygalacea", "Polygalacee"),
values="Polygalaceae")) %>%
mutate(Species=replace(Species,
list=word(Species, 1) %in% c("Polygonaceae", "Polygonacee"),
values="Polygonaceae")) %>%
mutate(Species=gsub('Polygonumlongisetum', 'Polygonum longisetum', Species)) %>%
mutate(Species=gsub('Posoq coria subsp. maxima', 'Posoqueria coriacea subsp. maxima', Species)) %>%
mutate(Species=gsub('Prosthecidi ', 'Prosthecidiscus ', Species)) %>%
mutate(Species=gsub('Pseudo bidens', '', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1) %in%
c("Pseudobriza", "Pseudofingergras",
"Pseudogerste", "Puschelgras", "Quecke",
"Queckenblatt", "Queckengras",
"Roggen/hafer", "Ruchgras", "Silbergras",
"Suessgras"),
values="Poaceae")) %>%
mutate(Species=gsub('Ptarmica', 'Achillea', Species)) %>%
mutate(Species=gsub('Pterost cauline leaves n. gibson & m.n. lyons', 'Pterostegia', Species)) %>%
mutate(Species=gsub('Quararibeaã‚â guianensis', 'Quararibea guianensis', Species)) %>%
mutate(Species=gsub('Rainfarn f', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Ranke ipomoea', 'Ipomoea', Species)) %>%
mutate(Species=gsub('Ranke rubiaceae', 'Rubiaceae', Species)) %>%
mutate(Species=gsub('Rauwolfia', 'Rauvolfia', Species)) %>%
mutate(Species=gsub('Rheinfarn', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Rhodostemon kunthiana', 'Rhodostemonodaphne kunthiana', Species)) %>%
mutate(Species=gsub('Riccardia/aneura', 'Bryophyta', Species)) %>%
mutate(Species=gsub('Rietgras steril 134051a', 'Poaceae', Species)) %>%
mutate(Species=gsub('Rosenbergio formosum', 'Rosenbergiodendron formosum', Species)) %>%
mutate(Species=gsub('Rotes puschelgras', 'Poaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Rubiacea",
values="Rubiaceae")) %>%
mutate(Species=gsub('Rytidospe goomallin a.g. gunness et al. oakp 10/', 'Rytidosperma', Species)) %>%
mutate(Species=gsub('Salacia idoensis', 'Salacia', Species)) %>%
mutate(Species=gsub('Samphire', 'Amaranthaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1) %in%
c("Sauergras", "Schlanksegge", "Sedge",
"Segge", "Simse"),
values="Cyperaceae")) %>%
mutate(Species=gsub('Scaev repen subsp. north sandp r.j. cranf & p.j. spenc', 'Scaevola repens', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Schachtelhalm",
values="Equisetaceae")) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Schnittlauch",
values="Amaryllidaceae")) %>%
mutate(Species=gsub('Schwertlilie trocken', 'Iridaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1) %in% c("Scropholacea", "Scrophulariacea", "Scroph."),
values="Scrophulariacea")) %>%
mutate(Species=gsub('Sitzende onagraceae', 'Onagraceae', Species)) %>%
mutate(Species=gsub('Sonnenblume', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Stachelgurke', 'Cucurbitaceae', Species)) %>%
mutate(Species=gsub('Stark behaarte malve', 'Malvaceae', Species)) %>%
mutate(Species=gsub('Staude asteraceae bl watteweich f', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Staude crotalaria unterseite silber', 'Crotalaria', Species)) %>%
mutate(Species=gsub('Staude solanum', 'Solanaceae', Species)) %>%
mutate(Species=gsub('Staude tephrosia', 'Tephrosia', Species)) %>%
mutate(Species=gsub('Stipagrosist panicle gross', 'Stipagrostis', Species)) %>%
mutate(Species=gsub('Asteraceae u silber', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Stratonostoc communeá', 'Stratonostoc commune', Species)) %>%
mutate(Species=gsub('Strauch asteraceae nadelblätt.', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Strauch blatt wie salix reticulata astera', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Strauch blatt wie salix reticulata astera 132534b', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Strauch fabaceae gerieft schote', 'Fabaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Strauch" &
word(Species,2)=="Rubiaceae",
values="Rubiaceae")) %>%
mutate(Species=gsub('Fabaceae samtig bl lanzettlich', 'Fabaceae', Species)) %>%
mutate(Species=gsub('Ochna mini', 'Ochna', Species)) %>%
mutate(Species=gsub('Stryphnoden microstachyum', 'Stryphnodendron microstachyum', Species)) %>%
mutate(Species=gsub('Sumpfgladiole haarig', 'Gladiolus', Species)) %>%
mutate(Species=gsub('Sygnum ramphicarpa', 'Scrophulariaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1)=="Symplococar",
values="Symplococarpon")) %>%
mutate(Species=gsub('Sysirinchium', 'Sisyrinchium', Species)) %>%
mutate(Species=gsub('Syzigium accuminatisima', 'Syzygium acuminatissimum', Species)) %>%
mutate(Species=gsub('Tabernaemon ', 'Tabernaemontana ', Species)) %>%
mutate(Species=gsub('Thalassodend', 'Thalassodendron', Species)) %>%
mutate(Species=gsub('Thinouia canescens', 'Thinouia', Species)) %>%
mutate(Species=gsub('Thistle', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Trisetumicatum', 'Trisetum spicatum', Species)) %>%
mutate(Species=gsub('Undetermined sedge', 'Cyperaceae', Species)) %>%
mutate(Species=replace(Species,
list=word(Species, 1) %in%
c("Liverwort", "Liverworts", "Moss"),
values="Bryophyta")) %>%
mutate(Species=gsub('Vismi bacci', 'Vismia baccifera subsp. ferruginea', Species)) %>%
mutate(Species=gsub('Weidenr”schen', 'Onagraceae', Species)) %>%
mutate(Species=gsub('Weißpelziger brauner Spross Asteracea', 'Asteraceae', Species)) %>%
mutate(Species=gsub('Wie stipagrostis', 'Poaceae', Species)) %>%
mutate(Species=gsub('Wincassia', 'Fabaceae', Species)) %>%
mutate(Species=gsub('xDactyloden st-quintini', 'Dactylodenia st-quintinii', Species)) %>%
mutate(Species=gsub('Zizyphus sp1 IUCN1', 'Zizyphus', Species)) %>%
mutate(Species=gsub('Zwiebel Lilaceae steril', 'Lilaceae', Species)) %>%
mutate(Species=gsub('Zwstr faurea', 'Faurea', Species)) %>%
mutate(Species=gsub('Quercus crispla', 'Quercus crispula', Species)) %>%
mutate(Species=gsub('Corallorrhiza', 'Corallorhiza', Species)) %>%
mutate(Species=gsub('Brunella vulgaris', 'Prunella vulgaris', Species))A total of r nrow(spec.list.TRY.sPlot %>% filter(OriginalNames != Species)) species names were modified. Although substantially improved, the species list has still quite a lot of inconsistencies.
The total list submitted to TNRS contains r length(unique(spec.list.TRY.sPlot$Species)) species names.
Match names against Taxonomic Name Resolution Service (TNRS)
Export species name list
write_csv(spec.list.TRY.sPlot %>% dplyr::select(Species) %>% distinct() ,
path = "../_derived/TNRS_submit/tnrs_submit_iter1.csv")The csv-file of species names was submitted to Taxonomic Name Resolution Service web application (Boyle et al. 2013, iPlant Collaborative (2015). TNRS version 4.0 was used, which became available in August 2015 (this version also included The Plant List version 1.1). TNRS was queried on 24/02/2020.
TNRS settings {#ID}
The following settings were used for resolving names on TNRS.
Sources for name resolution {#ID}
The initial TNRS name resolution run was based on the five standard sources that were ranked according to preference in the following order (default of TNRS):
- The Plant List (TPL)[@TPL2013]
- The Global Compositae Checklist (GCC)[@Flann2009]
- The International Legume Database and Information Service (ILDIS)[@ILDIS2006]
- Tropicos [@TROPICOS2013]
- PLANTS Database (USDA)[@USDA2012]
Family Classification
Resolved names were assigned to families based on the APGIII classification [@Chase2009], the same classification system used by Tropicos.
Retrieve results
Once the matching process was finished, results were retrieved from TNRS using the Detailed Download option that included the full name information (parsed components, warnings, links to sources, etc.). We retrieved all the matches for each species, constrained by source (TNRS default), where the name in the first source was selected as best match, unless there was no suitable match found in that source, the match from the next lower-ranked source was selected, until all resources where exhausted.
General procedure {#ID}
Manually inspect the TNRS-results table in a spreadsheat application (i.e. LibreOffice or Excel). Starting with the highest taxonomic rank considered (i.e. Family). For instance, if manual checking of the TRNS output reveals that all accepted names or synonyms that have accuracy scores >0.9 are correct taxon names, use the following selection procedure:
- Name_matched_rank (==Family)
- Taxonomic_status (==Accepted, Synomyn)
- Family_score (>0.9)
Continue this selection procedure for entries that were matched at lower taxonomic ranks, i.e. genus, species, etc..
Iteration 1 - Read and combine TNRS result files
Read the files downloaded from TNRS into R.
tnrs.res0 <- readr::read_delim("../_derived/TNRS_submit/tnrs_results_iter1.txt", delim="\t", locale = locale(encoding = 'UTF-8'),quote="",
col_type = cols(
.default = col_character(),
Name_number = col_double(),
Overall_score = col_double(),
Name_score = col_double(),
Author_score = col_double(),
Family_score = col_double(),
Genus_score = col_double(),
Specific_epithet_score = col_double(),
Infraspecific_epithet_score = col_double(),
Infraspecific_epithet_2_score = col_double(),
Selected = col_logical()
))Select best match for each submitted name
Best matches are selected in successive steps, depending at which taxonomic level each record was matched. Records were sorted based on decreasing match scores. Matches at low taxonomic level (variety, subspecies) were favoured over matches at high taxonomic levels (family, sections). When having exactly the same ranks, the records were ranked based on their source, as explained above.
For each name submitted, only the record having the highest rank was retained.
#reorder priorities
TNRS.priorities <- c("tpl;gcc;tropicos;usda",
"tpl;gcc;tropicos",
"tpl;gcc;usda",
"tpl;ildis;tropicos",
"tpl;ildis;usda",
"tpl;tropicos;usda",
"tpl;gcc",
"tpl;ildis",
"tpl;tropicos",
"tpl;usda",
"gcc;tropicos;usda",
"gcc;tropicos",
"tropicos;gcc",
"gcc;usda",
"gcc",
"ildis;tropicos;usda",
"ildis;tropicos",
"ildis;usda",
"ildis",
"tpl", # move tpl down the list,
# because for legumes and composites,
# tpl relies on gcc or ILDIS
"tropicos;usda",
"tropicos",
"usda" )tnrs.res <- tnrs.res0 %>%
mutate(Name_matched_rank=factor(Name_matched_rank,
levels=c("variety", "subspecies", "species", "genus",
"family", "section", "supersection",
"infraspecies", "forma", "race",
"nothosubspecies", "proles", "monstr",
"series"))) %>%
mutate(Source=factor(Source, levels=TNRS.priorities)) %>%
mutate(Taxonomic_status=factor(Taxonomic_status,
levels=c("Accepted","Synonym", "No opinion","Invalid",
"Illegitimate","Misapplied","Rejected name"))) %>%
#filter(Taxonomic_status %in% c("Accepted", "Synonym")) %>%
arrange(Name_number,
desc(Genus_score),
desc(Specific_epithet_score),
desc(Infraspecific_epithet_2_score),
desc(Infraspecific_epithet_score),
desc(Family_score),
desc(Name_score),
desc(Overall_score),
Source,
Taxonomic_status) %>%
group_by(Name_submitted) %>%
slice(1)After this first step, there are r sum(tnrs.res$Name_matched=="No suitable matches found.") records for which no match was found. Another r sum(tnrs.res$Overall_score<0.9) were unreliably matched (overall match score <0.9).
Family level {#ID}
Manually inspect sorted table and select all entries at the highest hierarchical level (family). Manually identify the family accuracy score threshold value above which a name can be considered a correct name. In the following case, this corresponds to a score $>$0.88.
index.family <- which(tnrs.res$Name_matched_rank == "family" &
(tnrs.res$Taxonomic_status == "Accepted" |
tnrs.res$Taxonomic_status == "Synonym") &
tnrs.res$Family_score > 0.88)
length(index.family)Genus level
index.genus <- which(tnrs.res$Name_matched_rank == "genus" &
( tnrs.res$Taxonomic_status %in% c("Synonym", "Accepted") &
tnrs.res$Genus_score > 0.83)
|
( tnrs.res$Taxonomic_status == "No opinion" &
tnrs.res$Genus_score >= 0.99))
length(index.genus)Species level
index.species <- which(tnrs.res$Name_matched_rank == "species" &
( (tnrs.res$Taxonomic_status == "Accepted" | #condition 1
tnrs.res$Taxonomic_status == "Synonym") &
tnrs.res$Genus_score > 0.78 &
tnrs.res$Name_score > 0.90)
|
( tnrs.res$Genus_score > 0.90 & # condition 2 - effective for records with subspecies information
(tnrs.res$Specific_epithet_score > 0.90)
))
length(index.species)Subspecies level
index.subspec <- which( (tnrs.res$Name_matched_rank %in% c("infraspecies", "subspecies") |
is.na(tnrs.res$Name_matched_rank)) & # there are a few records at sub-species level which are not categorized
(tnrs.res$Taxonomic_status == "Accepted" |
tnrs.res$Taxonomic_status == "Synonym"))
length(index.subspec)
index.variety <- which(tnrs.res$Name_matched_rank == "variety" &
(tnrs.res$Taxonomic_status == "Accepted" |
tnrs.res$Taxonomic_status == "Synonym"))
length(index.variety)
index.infraspec <- which(tnrs.res$Name_matched_rank == "infraspecies")
length(index.infraspec)
index.forma <- which(tnrs.res$Name_matched_rank == "forma")
length(index.forma)Identifying "non-matched" species that are spermatophyta
index.spermatophyt <- which(tnrs.res$Name_matched == "No suitable matches found."
& word(tnrs.res$Name_submitted, 1) == "Spermatophyta")
length(index.spermatophyt)
Select certain or uncertain names
Select names that do not fulfill the search criteria, i.e. that were not selected as certain species, for further name matching.
index.tnrs <- unique(c(index.family, index.forma, index.genus, index.species, index.subspec,
index.variety, index.spermatophyt))
tnrs.res.certain <- tnrs.res[index.tnrs,]
dim(tnrs.res.certain)
write.csv(tnrs.res.certain, file = "../_derived/TNRS_submit/tnrs.res.iter1.certain.csv")
tnrs.res.uncertain <- tnrs.res[-index.tnrs,]
dim(tnrs.res.uncertain)
write.csv(tnrs.res.uncertain, file = "../_derived/TNRS_submit/tnrs.res.iter1.uncertain.csv")
save(tnrs.res.certain, tnrs.res.uncertain, file="../_derived/TNRS_submit/tnrs.iter1.RData")
Delete subspecies information and rerun match in TNRS
Many unmatched records do contain subspecies information which could not be retrieved in TNRS, although genus and species seem to be spelled correctly. Also, sometimes the mismatch derives from having the word 'species' or 'sp' at the end of the name.
tnrs.submit.iter2 <- data.frame(old=tnrs.res.uncertain$Name_submitted) %>%
mutate(old=as.character(old)) %>%
mutate(new=old)
# delete remaining records of mushroom species
tnrs.submit.iter2 <- tnrs.submit.iter2 %>%
filter(!word(new,1) %in% mushroom)
# Extract family name for unidentified species
tnrs.submit.iter2 <- tnrs.submit.iter2 %>%
na.omit() %>%
group_by(old) %>%
mutate(family.lev=str_extract(word(new,1), pattern='([^\\s]+aceae)')) %>%
mutate(new=ifelse(is.na(family.lev), new, family.lev)) %>%
dplyr::select(-family.lev) %>%
ungroup()
#Cut to the first 2 words in the name string
tnrs.submit.iter2 <- tnrs.submit.iter2 %>%
group_by(old) %>%
mutate(Name_binomial=paste(word(new, c(1,2)), collapse=" ")) %>%
ungroup() %>%
mutate(Name_binomial=gsub(' NA$', '', Name_binomial))Save species list to submit to TNRS for iteration 2
write_csv(tnrs.submit.iter2 %>%
dplyr::select(Name_binomial) %>%
#After cleaning some names now match to those already resolved in iteration 1. Take them out
filter(!Name_binomial %in% tnrs.res.certain$Name_submitted) %>%
distinct(), path="../_derived/TNRS_submit/tnrs_submit_iter2.csv")Iteration 2 - Reimport resolved species names from TNRS and mark solved
tnrs.res.iter2.raw <- readr::read_delim("../_derived/TNRS_submit/tnrs_results_iter2.txt", delim="\t", locale = locale(encoding = 'UTF-8'),quote="",
col_type = cols(
.default = col_character(),
Name_number = col_double(),
Overall_score = col_double(),
Name_score = col_double(),
Author_score = col_double(),
Family_score = col_double(),
Genus_score = col_double(),
Specific_epithet_score = col_double(),
Infraspecific_epithet_score = col_double(),
Infraspecific_epithet_2_score = col_double(),
Selected = col_logical()
))
tnrs.res.iter2 <- tnrs.res.iter2.raw %>%
mutate(Name_matched_rank=factor(Name_matched_rank,
levels=c("variety", "subspecies", "species",
"genus", "family", "section",
"supersection", "infraspecies", "forma",
"race", "nothosubspecies", "proles",
"monstr", "series"))) %>%
mutate(Source=factor(Source, levels=TNRS.priorities)) %>%
mutate(Taxonomic_status=factor(Taxonomic_status,
levels=c("Accepted","Synonym", "No opinion",
"Invalid","Illegitimate","Misapplied",
"Rejected name"))) %>%
arrange(Name_number,
desc(Genus_score),
desc(Specific_epithet_score),
desc(Infraspecific_epithet_2_score),
desc(Infraspecific_epithet_score),
desc(Family_score),
desc(Name_score),
desc(Overall_score),
Source,
Taxonomic_status) %>%
group_by(Name_submitted) %>%
slice(1)Family level
index.family <- which(tnrs.res.iter2$Name_matched_rank == "family" &
(tnrs.res.iter2$Taxonomic_status == "Accepted" |
tnrs.res.iter2$Taxonomic_status == "Synonym") &
tnrs.res.iter2$Family_score > 0.88)
length(index.family)
Genus level
index.genus <- which(tnrs.res.iter2$Name_matched_rank == "genus" &
(tnrs.res.iter2$Taxonomic_status %in% c("Accepted","Synonym") &
tnrs.res.iter2$Genus_score >= 0.90 &
tnrs.res.iter2$Name_score > 0.49))
length(index.genus)
Species level
index.species <- which(tnrs.res.iter2$Name_matched_rank == "species" &
#(tnrs.res.iter2$Taxonomic_status == "Accepted" |
# tnrs.res.iter2$Taxonomic_status == "Synonym") &
tnrs.res.iter2$Genus_score >= 0.80 &
tnrs.res.iter2$Specific_epithet_score > 0.90)
length(index.species)Subspecies level
index.infraspec <- which(tnrs.res.iter2$Name_matched_rank == "infraspecies")
length(index.infraspec)
index.subspec <- which((tnrs.res.iter2$Name_matched_rank %in% c("infraspecies", "subspecies") |
is.na(tnrs.res.iter2$Name_matched_rank)) & # there are a few records at sub-species level which are not categorized
(tnrs.res.iter2$Taxonomic_status == "Accepted" |
tnrs.res.iter2$Taxonomic_status == "Synonym"))
length(index.subspec)
index.variety <- which(tnrs.res.iter2$Name_matched_rank == "variety" &
(tnrs.res.iter2$Taxonomic_status == "Accepted" |
tnrs.res.iter2$Taxonomic_status == "Synonym"))
length(index.variety)
index.forma <- which(tnrs.res.iter2$Name_matched_rank == "forma")
length(index.forma)
index.spermatophyt <- which(tnrs.res.iter2$Name_matched == "No suitable matches found."
& word(tnrs.res.iter2$Name_submitted, 1) == "Spermatophyta")
length(index.spermatophyt)
index.tnrs.iter2 <- unique(c(index.family, index.forma, index.genus, index.species, index.subspec,
index.variety, index.spermatophyt))
tnrs.res.iter2.certain <- tnrs.res.iter2[index.tnrs.iter2,]
dim(tnrs.res.iter2.certain)
write.csv(tnrs.res.iter2.certain, file = "../_derived/TNRS_submit/tnrs.res.iter2.certain.csv")
tnrs.res.iter2.uncertain <- tnrs.res.iter2[-index.tnrs.iter2,]
dim(tnrs.res.iter2.uncertain)
write.csv(tnrs.res.iter2.uncertain, file = "../_derived/TNRS_submit/tnrs.res.iter2.uncertain.csv")
save(tnrs.res.iter2.certain, tnrs.res.iter2.uncertain,
tnrs.submit.iter2, file="../_derived/TNRS_submit/tnrs.iter2.RData")Save species list to submit to TNRS for iteration 3
write_csv(tnrs.res.iter2.uncertain[,2], path = "../_derived/TNRS_submit/tnrs_submit_iter3.csv")This list was submitted to TNRS, but only selecting the NCBI database.
Iteration 3 - Reimport resolved species names from TNRS_NCBI
tnrs.res.iter3.raw <- readr::read_delim("../_derived/TNRS_submit/tnrs_results_iter3.txt", delim="\t",
locale = locale(encoding = 'UTF-8'),quote="",
col_type = cols(
.default = col_character(),
Name_number = col_double(),
Overall_score = col_double(),
Name_score = col_double(),
Author_score = col_double(),
Family_score = col_double(),
Genus_score = col_double(),
Specific_epithet_score = col_double(),
Infraspecific_epithet_score = col_double(),
Infraspecific_epithet_2_score = col_double(),
Selected = col_logical()
))
tnrs.ncbi <- tnrs.res.iter3.raw %>%
mutate(Name_matched_rank=factor(Name_matched_rank,
levels=c("variety", "subspecies", "species",
"genus", "family", "section", "supersection",
"infraspecies", "forma", "race",
"nothosubspecies", "proles", "monstr",
"series"))) %>%
mutate(Taxonomic_status=factor(Taxonomic_status,
levels=c("Accepted","Synonym", "No opinion","Invalid",
"Illegitimate","Misapplied","Rejected name"))) %>%
arrange(Name_number,
desc(Genus_score),
desc(Specific_epithet_score),
desc(Infraspecific_epithet_2_score),
desc(Infraspecific_epithet_score),
desc(Family_score),
desc(Name_score),
desc(Overall_score),
Source,
Taxonomic_status) %>%
group_by(Name_submitted) %>%
slice(1)Family level
index.family <- which(tnrs.ncbi$Name_matched_rank == "family" &
(tnrs.ncbi$Taxonomic_status == "Accepted"|
tnrs.ncbi$Taxonomic_status == "Synonym") &
tnrs.ncbi$Family_score > 0.85)
length(index.family)Genus level
index.genus <- which(tnrs.ncbi$Name_matched_rank == "genus" &
tnrs.ncbi$Taxonomic_status %in% c("Accepted", "Synonym", "No opinion") &
(
(tnrs.ncbi$Genus_score > 0.89 &
tnrs.ncbi$Name_score > 0.49) |
(tnrs.ncbi$Genus_score > 0.99 &
tnrs.ncbi$Name_score > 0.2)
))
length(index.genus)Species level
index.species.1 <- which(tnrs.ncbi$Name_matched_rank == "species" &
(tnrs.ncbi$Taxonomic_status == "Accepted" |
tnrs.ncbi$Taxonomic_status == "Synonym") &
tnrs.ncbi$Name_score > 0.94 &
tnrs.ncbi$Specific_epithet_score>=0.67)
length(index.species.1)
index.species.2 <- which(tnrs.ncbi$Name_matched_rank == "species" &
(tnrs.ncbi$Taxonomic_status == "Accepted" |
tnrs.ncbi$Taxonomic_status == "Synonym") &
tnrs.ncbi$Genus_score > 0.81 &
tnrs.ncbi$Name_score > 0.51 &
tnrs.ncbi$Specific_epithet_score>=0.67)
length(index.species.2)
index.species.3 <- which(tnrs.ncbi$Name_matched_rank == "species" &
tnrs.ncbi$Taxonomic_status == "No opinion" &
tnrs.ncbi$Genus_score > 0.7 &
tnrs.ncbi$Specific_epithet_score > 0.75)
length(index.species.3)
index.species <- unique(c(index.species.1, index.species.2, index.species.3))
length(index.species)Variety level
index.var <- which((tnrs.ncbi$Name_matched_rank == "subspecies" |
tnrs.ncbi$Name_matched_rank == "unknown" |
tnrs.ncbi$Name_matched_rank == "variety") &
(tnrs.ncbi$Taxonomic_status == "Accepted" |
tnrs.ncbi$Taxonomic_status == "No opinion" |
tnrs.ncbi$Taxonomic_status == "Synonym"))
length(index.var)
Select certain or uncertain names
index.ncbi <- unique(c(index.family, index.genus, index.species, index.var))
tnrs.ncbi.certain <- tnrs.ncbi[index.ncbi,]
nrow(tnrs.ncbi.certain)
write_csv(tnrs.ncbi.certain, path = "../_derived/TNRS_submit/tnrs.ncbi.certain.csv")
tnrs.ncbi.uncertain <- tnrs.ncbi[-index.ncbi,]
nrow(tnrs.ncbi.uncertain)
write_csv(tnrs.ncbi.uncertain, path = "../_derived/TNRS_submit/tnrs.ncbi.uncertain.csv")
save(tnrs.ncbi.certain, tnrs.ncbi.uncertain, file="../_derived/TNRS_submit/tnrs.iter3.RData")After iteration 3, there are still r nrow(tnrs.ncbi.uncertain) unresolved taxa.
Iteration 4 - Using The Plant List matching tools for unresolved names
Generate names list from tnrs.ncbi.uncertain to be matched against The Plant List, using Taxonstand::TPL. Add to this list, also all those species that in the first iterations did not return an accepted name.
tpl.submit <- tnrs.res.certain %>%
filter(is.na(Accepted_name)) %>%
dplyr::select(Name_submitted) %>%
bind_rows(tnrs.res.iter2.certain %>%
filter(is.na(Accepted_name)) %>%
dplyr::select(Name_submitted)) %>%
bind_rows(tnrs.ncbi.certain %>%
filter(is.na(Accepted_name)) %>%
dplyr::select(Name_submitted)) %>%
bind_rows(tnrs.ncbi.uncertain %>%
dplyr::select(Name_submitted)) %>%
distinct()
nrow(tpl.submit)
write_csv(tpl.submit, path="../_derived/TPL/tpl.submit.csv")
#divide in 99 batches
indices <- 1:nrow(tpl.submit)
chunks <- split(indices, sort(indices%%99))
library(doParallel)
library(parallel)
cl <- makeForkCluster(5, outfile="")
registerDoParallel(cl)
tpl.ncbi <- foreach(i=1:length(chunks), .combine=rbind) %dopar% {
tmp <- (TPL(tpl.submit$Name_submitted[chunks[[i]]]))
save(tmp, file=paste0("../_derived/TNRS_submit/TPL_foreach/tpl.ncbi", i,".RData"))
return(tmp)
}
stopCluster(cl)
save(tpl.ncbi, file = "../_derived/TPL/tpl_results_iter4.RData")#Reimport tpl.ncbi
tpl.ncbi <- NULL
for(ff in list.files("../_derived/TNRS_submit/TPL_foreach", full.names = T)){
load(ff)
tpl.ncbi <- rbind(tpl.ncbi, tmp)
}load("../_derived/TPL/tpl_results_iter4.RData")
tpl.ncbi.certain <- tpl.ncbi %>%
filter(Plant.Name.Index==T | Higher.level==T)
nrow(tpl.ncbi.certain)
write_csv(tpl.ncbi.certain, path = "../_derived/TPL/tpl.ncbi.certain.csv")
tpl.ncbi.uncertain <- tpl.ncbi %>%
filter(Plant.Name.Index==F & Higher.level==F) %>%
dplyr::select(Taxon)
nrow(tpl.ncbi.uncertain)
write_csv(tpl.ncbi.uncertain, path = "../_derived/TPL/tpl.ncbi.uncertain.csv")
save(tpl.ncbi.certain, tpl.ncbi.uncertain, file="../_derived/TNRS_submit/tnrs.iter4.RData")Merge the resolved species lists into a Backbone
Read files
load("../_derived/TNRS_submit/tnrs.iter1.RData")
load("../_derived/TNRS_submit/tnrs.iter2.RData")
load("../_derived/TNRS_submit/tnrs.iter3.RData")
load("../_derived/TNRS_submit/tnrs.iter4.RData")
#Double check of wrong taxa from TNRS
finalcheck <- c("Salix repens subsp. repens var. repens","Hieracium lachenalii")
tpl.ncbi.certain <- tpl.ncbi.certain %>%
bind_rows(TPL(finalcheck))
Combine the certain data sets:
Backbone <- spec.list.TRY.sPlot %>%
as.tbl() %>%
rename(Name_sPlot_TRY=OriginalNames,
Name_string_corr1=Species) %>%
left_join(tnrs.submit.iter2 %>%
dplyr::select(-new) %>%
rename(Name_string_corr1=old, Name_string_corr2=Name_binomial),
by="Name_string_corr1") %>%
mutate(Name_submitted=ifelse(!is.na(Name_string_corr2), Name_string_corr2, Name_string_corr1)) %>%
dplyr::select(Name_sPlot_TRY, Name_string_corr1, Name_string_corr2, Source, Name_submitted) %>%
rename(sPlot_TRY=Source) %>%
left_join(tnrs.res.certain %>%
#filter(!is.na(Accepted_name)) %>%
bind_rows(tnrs.res.iter2.certain) %>%
bind_rows(tnrs.ncbi.certain) %>%
#reformat TPL output to tnrs output
bind_rows(tpl.ncbi.certain %>%
rename(Name_submitted=Taxon,
Name_matched_url=ID,
Taxonomic_status=Taxonomic.status,
Accepted_name_author=New.Authority,
Accepted_name_url=New.ID,
Accepted_name_family=Family,
Selected=Plant.Name.Index) %>%
mutate_at(.vars=vars(New.Hybrid.marker, New.Infraspecific.rank, New.Infraspecific),
.fun=~ifelse(is.na(.), "", .)) %>%
mutate(Accepted_name=paste(New.Genus, New.Hybrid.marker,
New.Species, New.Infraspecific.rank,
New.Infraspecific)) %>%
mutate(Accepted_name=gsub(pattern="\\s+", " ", Accepted_name)) %>%
mutate(Accepted_name_species=paste(New.Genus, New.Hybrid.marker, New.Species)) %>%
mutate(Accepted_name_species=gsub(pattern="\\s+", " ", Accepted_name_species)) %>%
mutate(Accepted_name_rank=ifelse(Higher.level==F, "species", NA)) %>%
mutate(Source=paste("tpl", TPL.version)) %>%
dplyr::select( (data.frame(colmatch=match(colnames(tnrs.ncbi),
names(.))) %>%
filter(!is.na(colmatch)))$colmatch)
) %>%
group_by(Name_submitted) %>% #Some double matches. Prioritize best taxonomic status
mutate(Taxonomic_status=factor(Taxonomic_status,
levels=c("Accepted","Synonym", "No opinion","Invalid",
"Illegitimate","Misapplied","Rejected name",
"Unresolved"))) %>%
arrange(Taxonomic_status) %>%
slice(1) %>%
#delete empty spaces at end of names
mutate(Accepted_name=gsub(pattern=" $", replacement="", x=Accepted_name)) %>%
mutate(Accepted_name_species=gsub(pattern=" $", replacement="", x=Accepted_name_species)),
by="Name_submitted")
#Double check
nrow(Backbone) == nrow(spec.list.TRY.sPlot)Tag unresolved names and create output columns
Add four additional columns.
If names were neither resolved at the accepted or synonym level, set Status_correct == "Other", and assign No suitable matches found. to the remaining species.
Backbone <- Backbone %>%
mutate(Status_correct=fct_collapse(Taxonomic_status,
Other=c("No opinion","Invalid", "Unresolved",
"Illegitimate","Misapplied","Rejected name"))) %>%
mutate(Status_correct=fct_explicit_na(Status_correct, "No suitable matches found.")) %>%
#Create Name_correct field. Use Accepted names, if any. Otherwise matched names.
mutate(Name_correct=ifelse(!is.na(Accepted_name),
Accepted_name,
Name_matched)) %>%
mutate(Genus_correct=ifelse(!is.na(Name_correct) & (!Accepted_name_rank %in% c("family")),
word(Name_correct,1),
NA)) %>%
mutate(Rank_correct=ifelse(!is.na(Name_matched_rank),
as.character(Name_matched_rank),
"higher")) %>%
mutate(Rank_correct=factor(Rank_correct, levels=c("higher", "family", "genus", "species",
"subspecies", "variety", "infraspecies",
"race", "forma")))
summary(Backbone$Status_correct)
summary(Backbone$Rank_correct)There are r sum(is.na(Backbone$Name_correct)) species names for which we found no match in any of the taxonomic resources we used. Yet, for as many as r sum(Backbone$Rank_correct %in% c("higher", "family", "genus")) taxa, the matching did not properly resolve the species name, and we only found a match at genus or higher level.
Complete list of families
There are r sum(is.na(Backbone$Accepted_name_family)) records with missing family information. Create field Family_correct.
Backbone <- Backbone %>%
mutate(family.lev=str_extract(word(Name_correct,1), pattern='([^\\s]+aceae)')) %>%
mutate(Family_correct=ifelse(!is.na(Accepted_name_family),
Accepted_name_family,
family.lev)) %>%
dplyr::select(-family.lev)
# Remaining records with missing family info
sum((is.na(Backbone$Family_correct)))
Resolve genera with missing family info with TNRS
Genera_submit <- Backbone %>%
filter(is.na(Family_correct)) %>%
dplyr::select(Genus_correct) %>%
distinct()
write_csv(Genera_submit, "../_derived/TNRS_submit/Genera_submit.csv")Import results from TNRS. Best match only and simple download
import.profile <- cols(
Name_submitted = col_character(),
Name_matched = col_character(),
Author_matched = col_logical(),
Overall_score = col_double(),
Taxonomic_status = col_character(),
Accepted_name = col_character(),
Accepted_author = col_character(),
Accepted_family = col_character(),
Source = col_character(),
Warnings = col_character(),
Accepted_name_lsid = col_character()
)
tnrs.genera <- read_delim("../_derived/TNRS_submit/tnrs_genera.txt", delim="\t",
locale = locale(encoding = 'UTF-8'),quote="",col_type = import.profile)Attach resolved families to backbone
Backbone <- Backbone %>%
left_join(tnrs.genera %>%
dplyr::select(Name_submitted, Accepted_family) %>%
rename(Genus_correct=Name_submitted, Family_import=Accepted_family),
by="Genus_correct") %>%
mutate(Family_correct=ifelse(is.na(Family_correct),
Family_import,
Family_correct)) %>%
dplyr::select(-Family_import)
#Records with missing family info
sum(is.na(Backbone$Family_correct))
Complement with data from TRY 5.0
Data from try were received by Jens Kattge on Jan 21, 2020.
# Species, Genus, Family from try
try.species <- read_csv(
"../_input/TRY5.0_v1.1/TRY_5_GapFilledData_2020/input_data/hierarchy.info.csv",
locale = locale(encoding = "latin1"))
Backbone <- Backbone %>%
left_join(try.species %>%
dplyr::select(Genus_correct=Genus, family=Family) %>%
distinct() %>%
filter(family != "") %>%
group_by(Genus_correct),
by="Genus_correct") %>%
mutate(Family_correct=coalesce(Family_correct, family)) %>%
dplyr::select(-family)
# Remaining records with missing family info
sum((is.na(Backbone$Family_correct)))
Complement with data from The Catalogue of Life
#Download data from Catalogue of Life - 2019
download.file("http://www.catalogueoflife.org/DCA_Export/zip/archive-kingdom-plantae-bl3.zip",
destfile="/data/sPlot/users/Francesco/Ancillary_Data/Catalogue_of_Life/CatLife2019.zip")
unzip("/data/sPlot/users/Francesco/Ancillary_Data/Catalogue_of_Life/CatLife2019.zip", files="taxa.txt", exdir = "/data/sPlot/users/Francesco/Ancillary_Data/Catalogue_of_Life/")
cat.life <- read_delim("/data/sPlot/users/Francesco/Ancillary_Data/Catalogue_of_Life/taxa.txt",
delim="\t",
col_types = cols(
.default = col_character(),
taxonID = col_double(),
datasetID = col_double(),
acceptedNameUsageID = col_double(),
parentNameUsageID = col_double(),
superfamily = col_logical(),
subgenus = col_logical(),
source = col_logical(),
namePublishedIn = col_logical(),
modified = col_character(),
taxonConceptID = col_double(),
isExtinct = col_logical()
)) %>%
#correct family names to match to the standards in TPL
mutate(family=ifelse(family=="Fabaceae", "Leguminosae", family)) %>%
mutate(family=ifelse(family=="Asteraceae", "Compositae", family))
Genera_missing <- Backbone %>%
filter(is.na(Family_correct) & !is.na(Genus_correct)) %>%
dplyr::select(Genus_correct) %>%
distinct()
Backbone <- Backbone %>%
left_join(cat.life %>%
dplyr::select(genus, family) %>%
distinct() %>%
filter(family != "") %>%
group_by(genus) %>% #There are two genera with multiple attribution to families
slice(1) %>%
filter(genus %in% Genera_missing$Genus_correct) %>%
rename(Genus_correct=genus),
by="Genus_correct") %>%
mutate(Family_correct=coalesce(Family_correct, family)) %>%
dplyr::select(-family)
#Records with missing family info
sum(is.na(Backbone$Family_correct))After matching the remaining genera with the Catalogue of life there are still r nrow(Backbone %>% filter(is.na(Family_correct))) records without Family affiliation, for a total of r nrow(Backbone %>% filter(is.na(Family_correct)) %>% dplyr::select(Genus_correct) %>% distinct()) genera.
Manually fix some known issues
Backbone <- Backbone %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Coptidium",
values="Ranunculaceae")) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Balanocarpus",
values="Dipterocarpaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Cardaminopsis",
values="Brassicaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Carpolepis",
values="Myrtaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Cathartolinum",
values="Linaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Didiscus",
values="Araliaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Grammadenia",
values="Primulaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Antholoma",
values="Elaeocarpaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Odontarrhena",
values="Brassicaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Trichinium",
values="Amaranthaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Limonium",
values="Plumbaginaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Eunanus",
values="Phrymaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Sunaptea",
values="Dipterocarpaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Aconogonon",
values="Polygonaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Cajophora",
values="Loasaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Calobota",
values="Leguminosae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Alsine",
values="Caryophyllaceae" )) %>%
mutate(Family_correct=replace(Family_correct,
list=Genus_correct=="Cyanococcus",
values="Ericaceae" )) %>%
mutate(Family_correct=ifelse(Family_correct %in% c("Papilionaceae", "Fabaceae"),
"Leguminosae", Family_correct)) %>%
mutate(Family_correct=ifelse(Family_correct=="Asteraceae", "Compositae", Family_correct)) %>%
mutate(Family_correct=ifelse(Family_correct=="Unknown", NA, Family_correct))
#Records with missing family info
sum(is.na(Backbone$Family_correct))Derive info from other species of the same Genera in the Backbone itself
Derive family info from each genus in the backbone, and use this info to complement records from the same genera, but with missing family info.
genera_families <- Backbone %>%
filter(Taxonomic_status=="Accepted") %>%
dplyr::select(Genus_correct, Family_correct) %>%
rename(family=Family_correct) %>%
distinct() %>%
na.omit() %>%
#for some genera there are multiple families assigned
# (e.g. in case of unresolved species names )
# Extract the family names that occurs most often across each genus
group_by(Genus_correct, family) %>%
summarize(n=n()) %>%
arrange(desc(n)) %>%
slice(1) %>%
ungroup() %>%
dplyr::select(-n)
# Assign family derived from backbone to other records
Backbone <- Backbone %>%
left_join(genera_families, by="Genus_correct") %>%
mutate(Family_correct=ifelse( (is.na(Family_correct) & !is.na(family)),
family,
Family_correct)) %>%
dplyr::select(-family)
#Records with missing family info
sum(is.na(Backbone$Family_correct))
nrow(Backbone) == nrow(spec.list.TRY.sPlot)Delete records assigned to mushroom families, if any
mushroom.families <- c("Physalacriaceae", "Clavariaceae","Agaricaceae","Roccellaceae",
"Atheliaceae","Meruliaceae","Helotiaceae", "Dacrymycetaceae", "Boletaceae",
"Cortinariaceae", "Polyporaceae", "Pleosporaceae",
"Leotiaceae","Dermateaceae","Hymenochaetaceae","Stereaceae","Tremellaceae")
Backbone <- Backbone %>%
filter(!Genus_correct %in% mushroom) %>%
filter(!Family_correct %in% mushroom.families)
Create field Name_short
Shorten names that have more than two words and where the second word is a x. If there is no species name available, fill in with either genus or family info
Backbone <- Backbone %>%
mutate(Name_short=Name_correct) %>%
mutate(Name_short=gsub(pattern=" x ", replacement=" ", x=Name_short, fixed=T)) %>%
mutate(Name_short=word(Name_short, start=1L, end=2L)) %>%
mutate(Name_short=ifelse(!is.na(Name_short),
Name_short,
ifelse(!is.na(Genus_correct),
Genus_correct,
ifelse(!is.na(Family_correct),
Family_correct,
NA))))
sum(is.na(Backbone$Name_short))
sum(is.na(Backbone$Name_correct))
Create field is_vascular_plant and Taxon group
Attach phylum information from The Catalogue of Life.
Backbone <- Backbone %>%
left_join(cat.life %>%
dplyr::select(phylum, family) %>%
distinct() %>%
na.omit() %>%
rename(Family_correct=family),
by="Family_correct") Create fields is_vascular_species and Taxon group based on list of family manually classified, and on phyla from The Catalogue of Life.
Assign all families that belong to Tracheophyta to category is_vascular_species, based on
Backbone <- Backbone %>%
mutate(is_vascular_species=ifelse(phylum=="Tracheophyta", T, F)) %>%
mutate(is_vascular_species=replace(is_vascular_species,
list=Family_correct %in% vascular,
values=T)) %>%
mutate(`Taxon group`="Unknown") %>%
mutate(`Taxon group`=ifelse((!is.na(is_vascular_species) & is_vascular_species==T),
"Vascular plant", `Taxon group`)) %>%
mutate(`Taxon group`=replace(`Taxon group`,
list=Family_correct %in% lichens,
values="Lichen")) %>%
mutate(`Taxon group`=replace(`Taxon group`,
list=Genus_correct %in% lichen.genera,
values="Lichen")) %>%
mutate(`Taxon group`=replace(`Taxon group`,
list=Family_correct %in% algae_diatoms,
values="Alga")) %>%
mutate(`Taxon group`=replace(`Taxon group`,
list=phylum %in% c("Glaucophyta", "Rhodophyta", "Charophyta", "Chlorophyta"),
values="Alga")) %>%
mutate(`Taxon group`=replace(`Taxon group`,
list=Family_correct %in% mosses,
values="Moss")) %>%
mutate(`Taxon group`=replace(`Taxon group`,
list=phylum %in% c("Bryophyta", "Bryophyta", "Anthocerotophyta" ),
values="Moss")) %>%
mutate(is_vascular_species=ifelse(`Taxon group` %in% c("Moss", "Alga", "Lichen"),
F, is_vascular_species))
table(Backbone$`Taxon group`, exclude=NULL)
table(Backbone$is_vascular_species, exclude=NULL)Export Backbone
knitr::kable(Backbone %>%
sample_n(20),
caption="Example of Backbone (only 20 randomly selected taxa shown)") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")Description of fields in the Backbone
Name_sPlot_TRY - Name as retrieved in sPlot or TRY
Name_string_corr1 - Name after first round of string cleaning
Name_string_corr2 - Name after second round of string cleaning
sPlot_TRY - Origin or species name (S - sPlot, T - Try, A - Alpine dataset & combinations)
Name_submitted - Name as submitted to TNRS\TPL
Name_number - Number of species names when submitted to TNRS\TPL
Overall_score - Matching score from TNRS
Name_matched - Name matched in TNRS\TPL
Name_matched_rank - Taxonomic rank of name matched (e.g., species, family...)
Name_score - Matching score of name matched
Name_matched_author - Author names of matched names
Name_matched_url - Url from TNRS\TPL of matche name
Author_matched - Authors as matched from query (Empty since we only submitted species names)
Author_score - Score of author matching (Empty since we only submitted species names)
Family_matched - Family of the matched name
Family_score - Score of matched family name
Name_matched_accepted_family - Accepted family of the matched name (if available)
Genus_matched - Genus of the matched name
Genus_score - Score of matched genus name
Specific_epithet_matched - Specific epithet of the matched name
Specific_epithet_score - Score Specific epithet of the matched name
Infraspecific_rank - Rank of matched name, if below species
Infraspecific_epithet_matched - Infraspecific epithet of matched name
Infraspecific_epithet_score- Score of infraspecific epithet of matched name
Infraspecific_rank_2 - Rank of matched name (2nd level), if below species
Infraspecific_epithet_2_matched - Infraspecific epithet (2nd level) of matched name
Infraspecific_epithet_2_score - Score of infraspecific epithet (2nd level) of matched name
Annotations -
Unmatched_terms -
Taxonomic_status - Status of matched name (Accepted, Synonim, Unresolved...)
Accepted_name - Accepted name
Accepted_name_author - Author of accepted name
Accepted_name_rank - Rank of accepted name (family, genus, species, infraspecific...)
Accepted_name_url - url of accepted name
Accepted_name_species - Accepted species name (if Accepted_name_rank at species level of lower)
Accepted_name_family - Family of accepted name
Selected - ignore
Source - Database where the info comes from
Warnings -
Accepted_name_lsid -
Below columns added specifically for this backbone
Status_correct - Simplification of Taxonomic_status
Name_correct - If Accepted_name is non-null, otherwise returnes Name_matched. This field represent the union of accepted + matched name
Genus_correct - Genus derived from Name_correct, but only when Accepted_name_rank is lower than family
Rank_correct - Simplification of Accepted_name_rank
Family_correct - Family of Name_correct. Complements Accepted_name_family with multiple sources
Name_short - First two words of Name_correct
phylum - As derived from The Catalogue of Life
is_vascular_species - As derived based on selection of phylum from The Catalogue of Life
Taxon group - Taxon group, as in Turboveg. 'Vascular plant', 'Moss' (include liverworts), 'Lichen', 'Algae', 'Unknown
save(Backbone, mushroom, mushroom.families, lichen.genera,
file="../_output/Backbone3.0.RData")Export species list to request in TRY
ToSubmit1 <- Backbone %>%
filter(grepl(sPlot_TRY, pattern = "S")) %>%
filter(grepl(sPlot_TRY, pattern = "T")) %>%
dplyr::select(Name_sPlot_TRY) %>%
rename(Name_submit = Name_sPlot_TRY)
## add names from T, which match names from S only after standardization
ToSubmit2 <- Backbone %>%
filter(grepl(sPlot_TRY, pattern = "T")) %>%
filter(!grepl(sPlot_TRY, pattern = "S")) %>%
filter(Name_sPlot_TRY %in% ((Backbone %>%
filter(grepl(sPlot_TRY, pattern = "S")) %>%
filter(!grepl(sPlot_TRY, pattern = "T")) %>%
dplyr::select(Name_correct) %>%
distinct())$Name_correct)) %>%
dplyr::select(Name_sPlot_TRY) %>%
rename(Name_submit = Name_sPlot_TRY)
ToSubmit <- ToSubmit1 %>%
bind_rows(ToSubmit2)
write_csv(ToSubmit, "../_output/Submit_TRY.csv")Submitting r nrow(ToSubmit) species names to TRY.
Check how many species from sPlot where submitted to TRY5.0
Matched_names <- Backbone %>%
filter(grepl(sPlot_TRY, pattern = "S")) %>%
filter(Name_sPlot_TRY %in% ToSubmit$Name_submit) %>%
bind_rows(Backbone %>%
filter(grepl(sPlot_TRY, pattern = "S")) %>%
filter(Name_correct %in% ToSubmit$Name_submit)) %>%
distinct() Of the species names submitted to TRY there are r nrow(Matched_names) species names that match sPlot's (+ Alpine dataset) species names, before or after taxonomic resolution. These correspond to r Matched_names %>% distinct(Name_correct) %>% nrow() species names, AFTER taxonomic resolution.
Statistics
Statistics for backbone combining names in sPlot3.0 and TRY5.0
All taxon name entries
load("../_output/Backbone3.0.RData")How many new entries are in the backbone 3.0 compared to the backbone 2.1? How many entries are in common?
load("/data/sPlot/releases/sPlot2.1/backbone.splot2.1.try3.is.vascular.Rdata")
incommon <- nrow(Backbone %>%
dplyr::select(Name_sPlot_TRY) %>%
inner_join(backbone.splot2.1.try3 %>%
dplyr::select(names.sPlot.TRY) %>%
rename(Name_sPlot_TRY=names.sPlot.TRY),
by="Name_sPlot_TRY"))The new backbone contains r nrow(Backbone). The backbone 2.1 contained r nrow(backbone.splot2.1.try3). The two backbones have r incommon records in common.
Database affiliations (sPlot 3.1, TRY 3.0, and Alpine).
kable((table(Backbone$sPlot_TRY)), caption = "Number of (standardized) name entries
unique to, or shared between sPlot (S), TRY (T) and Alpine (A).") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")r nrow(Backbone %>% filter(sPlot_TRY %in% c("S", "ST", "SA", "STA"))) of the total number of entries belong to sPlot. r nrow(Backbone %>% filter(sPlot_TRY %in% c("T", "ST", "TA", "STA"))) name entries belong to TRY.
Taxonomic ranks:
kable((table(Backbone$Rank_correct, exclude=NULL)), caption = "Number of (standardized) name entries per taxonomic rank.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")Taxonomic status:
kable((table(Backbone$Taxonomic_status, exclude=NULL)), caption = "Number of (standardized) name entries for taxonomic status")%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")Total number of unique standardized taxon names and families:
length(unique(Backbone$Name_short))-1 # minus 1 for NA
length(unique(Backbone$Family_correct))-1 # minus 1 for NANumber of entries corresponding to vascular plant species:
table(Backbone$is_vascular_species, exclude=NULL)Number of duplicated entries after taxonomic standardization: Frequency of original (non-standardized) species names per resolved (non-standardized) name (excluding non-vascular and non-matched species).
df.count <- Backbone %>%
dplyr::filter(is_vascular_species == TRUE & !is.na(Name_correct)) %>%
dplyr::group_by(Name_correct) %>%
dplyr::summarise(n = n()) %>%
dplyr::arrange(desc(n))
kable(df.count[c(1:20), ], , caption = "Number of unresolved, original name
entries per resolved name. (Only first 20 shown") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")
Based on unique standardized names
Generate version of the backbone that only includes the unique resolved names in Name.short, and for the non-unique names, the first rows of duplicated name:
Backbone.uni <- Backbone %>%
distinct(Name_short, .keep_all = T) %>%
filter(!is.na(Name_short))There are r nrow(Backbone.uni) unique taxon names the in the backbone.
Exclude the non-vascular plant and non-matching taxon names:
Backbone.uni.vasc <- Backbone.uni %>%
dplyr::filter(is_vascular_species == TRUE)Now, run the stats for unique resolved names (excluding non-vascular and non-matching taxa):
length(Backbone.uni.vasc$Name_short)There are r length(Backbone.uni.vasc$Name.short) unique (vascular plant) taxon names:
kable((table(Backbone.uni.vasc$sPlot_TRY)), caption = "Number of (standardized) vascular plant taxon names per unique to, and shared between TRY (S), sPlot (T) and the Alpine (A) dataset.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")Taxonomic ranks:
kable((table(Backbone.uni.vasc$Rank_correct, exclude=NULL)), caption = "Number of (standardized) name entries per taxonomic rank.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")
Taxonomic status:
kable((table(Backbone.uni.vasc$Status_correct, exclude=NULL)), caption = "Number of (standardized) name entries per taxonomic status")%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")Total number of unique standardized taxon names and families:
length(unique(Backbone.uni.vasc$Name_short))-1 # minus 1 for NA
length(unique(Backbone.uni.vasc$Family_correct))-1
Stats for the corrected names in sPlot only:
Backbone.uni.sPlot <- Backbone.uni.vasc %>%
filter(sPlot_TRY %in% c("S", "ST", "SA", "STA"))There are r nrow(Backbone.uni.sPlot %>% distinct(Name_correct)) unique, corrected names of vascular plants for sPlot species
Database affiliations
kable((table(Backbone.uni.sPlot$sPlot_TRY)), caption = "Number of (standardized) vascular
plant taxon names per unique to sPlot (S), and shared with TRY (ST), the Alpine dataset (SA) or both (STA).")%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")Taxonomic ranks:
kable((table(Backbone.uni.sPlot$Rank_correct, exclude=NULL)), caption = "Number of (standardized) vascular plant taxon names per taxonomic rank.")%>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")
Taxonomic status:
kable((table(Backbone.uni.sPlot$Status_correct, exclude=NULL)), caption = "Number of (standardized) vascular plant taxon names that correspond to `Accepted`, `Synonyms` or Unresolved species, respecively.") %>%
kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"),
latex_options = "basic",
full_width = F, position = "center")
Number of families in sPlot:
length(unique(Backbone$Family_correct))Done!
Appendix
Create lists of genera manually classified into taxonomic groups
## fungi genera #NOT COMPLETE LIST
mushroom <- c("Mycena", "Boletus", "Russula","Calocybe","Collybia","Amanita","Amanitopsis","Coprinus",
"Galerina","Geoglossum","Hebeloma","Hydnum","Lactarius","Leucocarpia","Naucoria","Otidea","Polyporus",
"Sarcodom","Sarcoscyphus","Scleroderma","Stropharia","Tylopilus","Typhula", "Calyptella", "Chrysopsora", "Lacrymaria", "Dermoloma",
"Agaricus","Alnicola", "Amanitina", "Bovista", "Cheilymenia","Clavulinopsis", "Clitocybe", "Entoloma", "Geaster", "Inocybe", "Paxillus",
"Laccaria", "Laetiporus", "Lepista", "Macrolepiota", "Macrolepis", "Marasmius", "Panaeolus", "Psathyrella", "Psilocybe",
"Rickenella", "Sarcoscypha", "Vascellum", "Ramaria", "Agrocybe",
"Flammulaster", "Phaeocollybia", "Cortinarius", "Lepiota", "Cystoderma",
"Armillaria", "Athelia", "Ceraceomyces", "Chlorociboria", "Clavariaceae",
"Cystoderma", "Dacrymyces","Dendrographa","Dirina", "Flammulaster","Fomes","Gyrophora",
"Kirschsteiniothelia", "Lepiota","Llimoniella","Mazosia","Mollisia",
"Multiclavula","Phaeocollybia","Phellinus","Plectocarpon","Pleospora","Ramariopsis","Reinkella",
"Roccella","Roccellina","Sigridea","Stereum","Tremella","Tulostoma","Unguiculariopsis" ,
"Xanthoconium")
vascular <- c("Leguminosae" , "Alliaceae", "Bombacaceae" ,"Taxodiaceae",
"Aceraceae", "Centrolepidaceae","Callitrichaceae" ,"Flacourtiaceae",
"Compositae", "Asclepiadaceae", "Papilionaceae","Tiliaceae",
"Mimosaceae" , "Xanthorrhoeaceae","Arthropteridaceae",
"Valerianaceae", "Grammitidaceae" ,"Anarthriaceae", "Caesalpiniaceae",
"Chenopodiaceae", "Corylaceae", "Diervillaceae", "Dipsacaceae","Guttiferae",
"Haptanthaceae", "Hymenophyllopsidaceae", "Isoëtaceae","Labiatae",
"Lactoridaceae","Lemnaceae","Selaginaceae","Sterculiaceae","Myoporaceae",
"Myrsinaceae" ,"Pyrolaceae", "Rhoipteleaceae" ,"Xanthoceraceae")
lichens <- c("Acarosporaceae" , "Parmeliaceae", "Physciaceae", "Lichinaceae",
"Caliciaceae", "Lecanoraceae", "Venturiaceae" ,"Sphaerophoraceae" ,
"Verrucariaceae", "Tricholomataceae","Baeomycetaceae",
"Catillariaceae" ,"Megasporaceae","Ramalinaceae","Pilocarpaceae" ,
"Teloschistaceae","Candelariaceae","Rhizocarpaceae","Lecideaceae",
"Icmadophilaceae","Cladoniaceae","Collemataceae","Pannariaceae" ,
"Lobariaceae", "Ophioparmaceae" ,"Psoraceae","Stereocaulaceae",
"Massalongiaceae","Peltigeraceae","Nephromataceae", "Umbilicariaceae" )
lichen.genera <- c("Amygdalaria", "Anamylospora", "Arthonia", "Pertusaria", "Pyrenula","Opegrapha",
"Ochrolechia", "Graphis", "Micarea", "Porpidia", "Arthopyrenia", "Graphina", "Anisomeridium",
"Mycobilimbia","Peltula", "Thelotrema", "Arthothelium", "Diploschistes", "Strigula",
"Trichothelium", "Melaspilea", "Phaeographis", "Thelenella", "Chaenothecopsis","Fuscidea",
"Dactylospora", "Gyalecta", "Myriotrema", "Placynthium", "Umbilicaria",
"Lasallia", "Microthelia", "Lichenochora", "Roselliniopsis", "Homostegia",
"Verrucaria", "Leptorhaphis")
mosses <- c("Pilotrichaceae", "Chonecoleaceae", "Hypopterygiaceae", "Scorpidiaceae",
"Balantiopsaceae", "Mesoptychiaceae","Octoblepharaceae" ,"Takakiaceae")
algae_diatoms <- c("Sargassaceae", "Chordaceae", "Cocconeidaceae", "Desmarestiaceae",
"Chordariaceae", "Dinobryaceae", "Diploneidaceae", "Ectocarpaceae",
"Fragilariaceae","Sphacelariaceae","Vaucheriaceae" ,
"Amphipleuraceae", "Fucaceae", "Gomphonemataceae", "Melosiraceae",
"Laminariaceae","Acinetosporaceae" ,"Botryochloridaceae",
#diatoms below
"Thalassiosiraceae", "Cymbellaceae", "Naviculaceae","Bacillariaceae")
save(mushroom, vascular, lichens, lichen.genera, mosses, algae_diatoms, file="../_derived/taxa_manual.RData")
R-settings
sessionInfo()toCheck_manual <- read_csv("../_derived/TPL/tpl.ncbi.uncertain.csv")
load("../_output/Backbone3.0.RData")
toCheck_manual <- toCheck_manual %>%
rename(Name_string_corr2=Taxon) %>%
left_join(Backbone %>%
dplyr::select(Name_sPlot_TRY:sPlot_TRY),
by="Name_string_corr2") %>%
left_join(DT0 %>%
rename(Name_sPlot_TRY=`Matched concept`) %>%
dplyr::select(Name_sPlot_TRY) %>%
group_by(Name_sPlot_TRY) %>%
summarize(count=n()),
by="Name_sPlot_TRY") %>%
left_join(DT0 %>%
rename(Name_sPlot_TRY=`Matched concept`) %>%
dplyr::select(Name_sPlot_TRY, Taxonomy) %>%
distinct(Name_sPlot_TRY, .keep_all=T),
by="Name_sPlot_TRY"
)
write_csv(toCheck_manual, path="../_derived/TPL/toCheck_Manual.csv")