Introducción

Un aspecto de reproducibilidad científica que es específico de las ciencias biológicas es cómo se obtiene, usa y reporta la información taxonómica. En la discusión tratamos los temas del uso de vouchers y la importancia de documentar adecuadamente la justificación de identificaciones taxonómicas. Aquí vamos a ver una herramienta para obtener y curar información taxonómica de manera reproducible.

Vamos a usar el paquete en R ‘taxize’. Por cierto, el artículo que describe el paquete fue publicado en la revista F1000Research, una de las revistas que vimos al comienzo del semestre es completamente abierta.

Chamberlian y Szöcs (2013).

¿Por qué ‘taxize’?

Existen bases de datos en línea de las que se puede obtener información taxonómica para diversos organismos biológicos. Pero existen ventajas de hacer estás búsquedas de manera programática:

  1. es más eficiente si tiene que buscar bastantes taxones
  2. la búsqueda se convierte en una parte reproducible del flujo de trabajo

La idea de taxize es hacer la extracción y uso de la iformación taxonómica fácil y reproducible.

Imagen: Rohan Chakravarty/CC BY-NC-ND 3.0.

¿Qué hace ‘taxize’?

‘taxize’ se conecta con varias bases de datos taxonómicas y más pueden ir siendo agregagas paulatinamente. Esta información se puede utilizar para llevar a cabo tareas comunes en el proceso de investigación. Por ejemplo:

Resuelve nombres taxonómicos

Si tenemos una lista de especímenes, posiblemente queremos saber si estamos usando nombres actualizados y si los nombres que tenemos están escritos correctamente. Podemos hacer esto usando la aplicación Global Names Resolver (GNR) de la Encyclopedia of Life, a través de taxize.

Como un ejemplo, veamos los datos de ocurrencia que bajé de GBIF. Bajé los registros de pajaritos del género Ramphocelus en Costa Rica, en la Colección Nacional de Zoología. Tal vez, estoy haciendo o planeo hacer un trabajo con estos espécimenes.

Los datos están aquí (https://doi.org/10.15468/dl.d8frtc)

y este es un ejemplo del pajarito:

Ramphocelus sanguinolentus, La Fortuna, Costa Rica 

# leamos los datos
dat <- read.csv(file = "./additional_files/0098054-200613084148143.csv",
    header = T, sep = "\t")

# cuales son las especies en CR?
Ram.names <- levels(dat$species)
Ram.names
## NULL

Veamos cuáles bases de datos puedo usar para buscar los nombres de mis especies

require(taxize)
require(kableExtra)
data.sources <- gnr_datasources()
data.sources[, c(1, 5, 8, 9)] %>%
    kbl() %>%
    kable_minimal()
created_at id refresh_period_days title
2012-07-06T11:36:36Z 1 14 Catalogue of Life Checklist
2012-07-06T11:38:14Z 2 14 Wikispecies
2012-02-09T10:31:13Z 3 14 Integrated Taxonomic Information SystemITIS
2012-02-09T10:47:55Z 4 14 National Center for Biotechnology Information
2012-02-09T11:16:43Z 5 14 Index Fungorum (Species Fungorum)
2012-02-09T11:28:38Z 6 14 GRIN Taxonomy for Plants
2012-02-09T11:32:18Z 7 14 Union 4
2012-02-09T12:08:54Z 8 14 The Interim Register of Marine and Nonmarine Genera
2012-02-09T12:40:45Z 9 14 World Register of Marine Species
2012-02-09T12:55:04Z 10 14 Freebase
2012-02-09T13:01:40Z 11 14 GBIF Backbone Taxonomy
2012-02-09T15:36:33Z 12 14 Encyclopedia of Life
2012-02-09T18:21:08Z 93 14 Passiflora vernacular names
2012-02-09T18:21:09Z 94 14 Inventory of Fish Species in the Wami River Basin
2012-02-09T18:21:09Z 95 14 Pheasant Diversity and Conservation in the Mt. Gaoligonshan Region
2012-02-09T18:21:09Z 96 14 Finding Species
2012-02-09T18:21:10Z 97 14 Birds of Lindi Forests Plantation
2012-02-09T18:21:11Z 98 14 Nemertea
2012-02-09T18:21:12Z 99 14 Kihansi Gorge Amphibian Species Checklist
2012-02-09T18:21:12Z 100 14 Mushroom Observer
2012-02-09T18:21:14Z 101 14 TaxonConcept
2012-02-09T18:21:15Z 102 14 Amphibia and Reptilia of Yunnan
2012-02-09T18:21:17Z 103 14 Common names of Chilean Plants
2012-07-06T11:49:07Z 104 14 Invasive Species of Belgium
2012-02-09T18:21:20Z 105 14 ZooKeys
2012-02-09T18:21:23Z 106 14 COA Wildlife Conservation List
2012-02-09T18:21:25Z 107 14 AskNature
2012-02-09T18:21:31Z 108 14 China: Yunnan, Southern Gaoligongshan, Rapid Biological Inventories Report No. 04
2012-02-09T18:21:34Z 109 14 Native Orchids from Gaoligongshan Mountains, China
2012-02-09T18:21:37Z 110 14 Illinois Wildflowers
2012-02-09T18:21:45Z 112 14 Coleorrhyncha Species File
2012-02-09T18:21:46Z 113 14 /home/dimus/files/dwca/zoological names.zip
2012-02-09T18:21:57Z 114 14 Peces de la zona hidrogeográfica de la Amazonia, Colombia (Spreadsheet)
2012-02-09T18:22:04Z 115 14 Eastern Mediterranean Syllidae
2012-02-09T18:22:06Z 116 14 Gaoligong Shan Medicinal Plants Checklist
2012-02-09T18:22:14Z 117 14 birds_of_tanzania
2012-02-09T18:22:23Z 118 14 AmphibiaWeb
2012-02-09T18:22:38Z 119 14 tanzania_plant_sepecimens
2012-02-09T18:22:45Z 120 14 Papahanaumokuakea Marine National Monument
2012-02-09T18:23:21Z 121 14 Taiwanese IUCN species list
2012-02-09T18:23:27Z 122 14 BioPedia
2012-02-09T18:24:06Z 123 14 AnAge
2012-02-09T18:24:25Z 124 14 Embioptera Species File
2012-02-09T18:24:28Z 125 14 Global Invasive Species Database
2012-02-09T18:24:38Z 126 14 Sendoya S., Fernández F. AAT de hormigas (Hymenoptera: Formicidae) del Neotrópico 1.0 2004 (Spreadsheet)
2012-02-09T18:25:00Z 127 14 Flora of Gaoligong Mountains
2012-02-09T18:25:16Z 128 14 ARKive
2012-02-09T18:25:27Z 129 14 True Fruit Flies (Diptera, Tephritidae) of the Afrotropical Region
2012-02-09T18:25:30Z 130 14 3i - Typhlocybinae Database
2012-02-09T18:26:09Z 131 14 CATE Sphingidae
2012-02-09T18:26:28Z 132 14 ZooBank
2012-02-09T18:26:44Z 133 14 Diatoms
2012-02-09T18:27:14Z 134 14 AntWeb
2012-02-09T18:27:40Z 135 14 Endemic species in Taiwan
2012-02-09T18:28:15Z 136 14 Dermaptera Species File
2012-02-09T18:28:21Z 137 14 Mantodea Species File
2012-02-09T18:28:29Z 138 14 Birds of the World: Recommended English Names
2012-02-09T18:29:01Z 139 14 New Zealand Animalia
2012-02-09T18:30:39Z 140 14 Blattodea Species File
2012-02-09T18:30:57Z 141 14 Plecoptera Species File
2012-02-09T18:31:58Z 143 14 Coreoidea Species File
2012-02-09T18:32:28Z 144 14 Freshwater Animal Diversity Assessment - Normalized export
2012-02-09T18:33:38Z 145 14 Catalogue of Vascular Plant Species of Central and Northeastern Brazil
2012-02-09T18:35:12Z 146 14 Wikipedia in EOL
2012-02-09T18:36:49Z 147 14 Database of Vascular Plants of Canada (VASCAN)
2012-02-09T18:38:13Z 148 14 Phasmida Species File
2012-02-09T18:38:29Z 149 14 OBIS
2012-02-09T18:40:09Z 150 14 USDA NRCS PLANTS Database
2012-02-09T18:42:04Z 151 14 Catalog of Fishes
2012-02-09T18:43:41Z 152 14 Aphid Species File
2012-02-09T18:44:03Z 153 14 The National Checklist of Taiwan
2012-02-09T18:46:06Z 154 14 Psocodea Species File
2012-02-09T18:46:24Z 155 14 FishBase
2012-02-09T18:48:19Z 156 14 3i - Typhlocybinae Database
2012-02-09T18:48:44Z 157 14 Belgian Species List
2012-02-09T18:51:49Z 158 14 EUNIS
2012-02-09T18:58:36Z 159 14 CU*STAR
2012-02-09T19:10:42Z 161 14 Orthoptera Species File
2012-02-09T19:11:37Z 162 14 Bishop Museum
2012-02-09T19:18:20Z 163 14 IUCN Red List of Threatened Species
2012-02-09T19:20:46Z 164 14 BioLib.cz
2012-02-09T19:43:03Z 165 14 Tropicos - Missouri Botanical Garden
2012-02-09T20:05:41Z 166 14 nlbif
2012-02-09T20:36:27Z 167 14 The International Plant Names Index
2012-05-07T13:45:07Z 168 14 Index to Organism Names
2012-05-07T13:50:15Z 169 14 uBio NameBank
2013-05-31T01:17:28Z 170 14 Arctos
2013-12-10T03:02:58Z 171 14 Checklist of Beetles (Coleoptera) of Canada and Alaska. Second Edition.
2014-12-08T11:17:24Z 172 14 The Paleobiology Database
2014-12-08T19:50:56Z 173 14 The Reptile Database
2014-12-09T21:27:18Z 174 14 The Mammal Species of The World
2014-12-11T00:19:59Z 175 14 BirdLife International
2015-03-03T13:48:51Z 176 14 Checklist da Flora de Portugal (Continental, Açores e Madeira)
2016-07-20T11:13:25Z 177 14 FishBase Cache
2016-10-18T20:00:31Z 178 14 Silva
2016-10-19T10:13:10Z 179 14 Open Tree of Life Reference Taxonomy
2016-10-30T00:46:40Z 180 14 iNaturalist
2016-11-03T16:09:05Z 181 14 The Interim Register of Marine and Nonmarine Genera
2017-03-22T15:26:50Z 182 14 Gymno
2020-05-25T02:43:22Z 183 14 Index Animalium
2020-05-25T10:32:16Z 184 14 ASM Mammal Diversity Database
2020-05-27T01:41:11Z 185 14 IOC World Bird List
2020-05-28T00:01:22Z 186 14 MCZbase
2020-05-28T16:50:17Z 187 14 The eBird/Clements Checklist of Birds of the World
2020-05-30T00:58:32Z 188 14 American Ornithological Society
2020-05-31T00:36:35Z 189 14 Howard and Moore Complete Checklist of the Birds of the World
2020-05-31T01:23:24Z 193 14 Myriatrix
2021-03-19T16:48:41Z 194 14 PLAZI treatments
2021-10-21T12:27:48Z 195 14 AlgaeBase
2021-12-28T13:04:34Z 196 14 World Flora Online consortium
2021-12-29T13:29:11Z 197 14 World Checklist of Vascular Plants
2021-12-30T14:32:51Z 198 14 The Leipzig Catalogue of Vascular Plants
2022-01-14T22:16:40Z 200 14 The Terrestrial Parasite Tracker
2022-02-14T15:59:43Z 201 14 ICTV Virus Taxonomy
2022-02-18T21:58:40Z 202 14 Discover Life Bee Species Guide

Revisemos si están escritos correctamente

name.res <- gnr_resolve(sci = Ram.names, data_source_ids = c(3:4))
name.res[, -1] %>%
    kbl() %>%
    kable_minimal()

¿Y si no lo estuvieran?

Ram.names2 <- Ram.names
Ram.names2[2] <- "Ramphocelus passerini"
name.res2 <- gnr_resolve(sci = Ram.names2, data_source_ids = c(3:4))
name.res2[, -1] %>%
    kbl() %>%
    kable_minimal()
submitted_name matched_name data_source_title score
Ramphocelus passerini Ramphocelus passerinii Bonaparte, 1831 Integrated Taxonomic Information SystemITIS 0.75
Ramphocelus passerini Ramphocelus passerinii National Center for Biotechnology Information 0.75

Identifica sinónimos

Busquemos si hay sinónimos para estas especies

synonyms(sci_id = Ram.names, db = "itis")

Para usar algunas bases de datos es necesario obtener un ‘API key’ Esto no se puede hacer automáticamente con ‘taxize’ pero se puede obtener instrucciones de cómo obtener y guardar el API key para usarlo desde R. Veamos un par de ejemplos:

use_tropicos()
use_iucn()
use_entrez()

# para más información
`?`(key_helpers())
`?`(`taxize-authentication`)

Ejercicio 1

  1. Instale el paquete ‘usethis’ con el commando install.packages("usethis")
  2. Obtenga el ‘API key’ para una base de datos de su interés
  3. Añada este ‘API key’ a su ambiente en R con el comando usethis::edit_r_environ()
  4. Reinicie R y verifique que tiene el ‘API key’ usando getkey()
  5. Si todo salió bien, ya lo vamos a usar.

 

Extrae clasificación taxonómica

Podemos obtener información sobre la clasificación taxonómica superior de nuestras especies. Si su clave es para ‘tropicos’ o ‘entrez’ puede usar las bases de datos respectivas (tropicos y ncbi). Por ejemplo:

Ram.class <- classification(Ram.names, db = "ncbi")
Ram.class[[1]] %>%
    kbl() %>%
    kable_minimal()

y si sólo queremos saber la familia…

Ram.fam <- tax_name(sci = Ram.names, get = "family", db = "ncbi")
Ram.fam %>%
    kbl() %>%
    kable_minimal()

Obtiene los nombres corriente abajo

Ta vez queremos saber cuáles o cuántos son los miembros de un cierto grupo taxonómico. Por ejemplo, ¿cuántas especies hay en el género Ramphocelus?

# separemos el genero
genus <- strsplit(Ram.names[1], " ")[[1]][1]

# obtengamos las especies
Ram.down <- downstream(sci_id = genus, downto = "species", db = "ncbi")
Ram.down[[1]] %>%
    kbl() %>%
    kable_minimal()

Obtiene información del estado de conservación

Si tienen el ‘API key’ para IUCN, pueden obtener información sobre el estado de consevación.

OJO: los autores de ‘taxize’ advierten usar con mucho cuidado ya que puede haber errores

Ram.sum <- iucn_summary(Ram.names)
iucn_status(Ram.sum)
get_iucn(Ram.names)

Ejemplos de aplicaciones en ciencia reproducible

Veamos algunos ejemplos de cómo usar herramientas como ’taxize’contribuyen a investigación más reproducible.

Listas de hospederos en miles de comunidades

Artículo de Gibb et al. 2020, Nature

De la sección de métodos:

“We compiled animal host–pathogen associations from several source databases, to provide as comprehensive a dataset as possible of zoonotic host species and their pathogens: the Enhanced Infectious Diseases (EID2) database; the Global Mammal Parasite Database v.2.0 (GMPD2) which collates records of parasites of cetartiodactyls, carnivores and primates; a reservoir hosts database; a mammal–virus associations database; and a rodent zoonotic reservoirs database augmented with pathogen data from the Global Infectious Disease and Epidemiology Network (GIDEON) (Supplementary Table 8). We harmonized species names across all databases, excluding instances in which either hosts or pathogens could not be classified to species level. To prevent erroneous matches due to misspelling or taxonomic revision, all host species synonyms were accessed from Catalogue Of Life using ‘taxize’ v.0.8.939. Combined, the dataset contained 20,382 associations between 3,883 animal host species and 5,694 pathogen species.”

Veamos el código del artículo y hagamos una pequeña modificación para aplicar la función a nuestros datos

# taxize/GBIFr
require(taxize)
require(rgbif)
library(plyr)

# function to find and resolve taxonomic synonyms based on
# Encyclopedia of Life
findSyns2 = function(x) {

    # get specific species name taxname = hosts_vec[x] un cambio
    # pequenio para usar la funcion con nuestros datos
    taxname = x
    # print progress
    print(paste("Processing:", taxname, sep = " "))

    # phyla
    phyla = c("Chordata", "Arthropoda", "Gastropoda", "Mollusca")

    # (1) resolve misspellings
    taxname_resolved = gnr_resolve(taxname, with_canonical_ranks = TRUE)$matched_name2[1]
    if (!is.null(taxname_resolved)) {
        if (length(strsplit(taxname_resolved, " ", fixed = TRUE)[[1]]) ==
            2) {
            taxa = taxname_resolved
        }
    }
    if (!is.null(taxname_resolved)) {
        if (length(strsplit(taxname_resolved, " ", fixed = TRUE)[[1]]) >
            2) {
            taxa = paste(strsplit(taxname_resolved, " ", fixed = TRUE)[[1]][1:2],
                collapse = " ")
        }
    }

    # if taxa == NA, return list with nothing defined
    if (is.na(taxa)) {
        if (class(syns)[1] == "simpleError") {
            return(data.frame(Original = taxname, Submitted = taxname_resolved,
                Accepted_name = NA, Selected_family = NA, Selected_order = NA,
                Selected_class = NA, Synonyms = NA))
        }
    }

    # (2) remove sub-species categorisations and set 'genus' and
    # 'species' variables
    genus = NULL
    if (length(strsplit(taxa, " ", fixed = TRUE)[[1]]) %in% c(2, 3)) {
        genus = strsplit(taxa, " ", fixed = TRUE)[[1]][1]
        species = strsplit(taxa, " ", fixed = TRUE)[[1]][2]
    }
    if (length(strsplit(taxa, "_", fixed = TRUE)[[1]]) %in% c(2, 3)) {
        genus = strsplit(taxa, "_", fixed = TRUE)[[1]][1]
        species = strsplit(taxa, "_", fixed = TRUE)[[1]][2]
    }
    if (length(strsplit(taxa, " ", fixed = TRUE)[[1]]) > 3 | length(strsplit(taxa,
        "_", fixed = TRUE)[[1]][1]) > 3) {
        return("name error")
    }
    if (is.null(genus)) {
        genus = taxa
        species = NA
    }

    # (3) use genus to lookup family, order, class
    syns = tryCatch(name_lookup(genus)$data, error = function(e) e)
    if (class(syns)[1] == "simpleError") {
        return(data.frame(Original = taxname, Submitted = taxa, Accepted_name = NA,
            Selected_family = NA, Selected_order = NA, Selected_class = NA,
            Synonyms = NA))
    }

    # for cases where the lookup does not find a phylum within
    # the specified range
    if (all(!syns$phylum %in% phyla)) {
        fam1 = syns$family[!is.na(syns$family) & !is.na(syns$phylum)]
        order1 = syns$order[!is.na(syns$family) & !is.na(syns$phylum)]
        class1 = syns$class[!is.na(syns$family) & !is.na(syns$phylum)]
        datfam = data.frame(fam1 = fam1, order = 1:length(fam1), order1 = order1,
            class1 = class1)
        # select highest frequency fam/class/order combo
        fam2 = as.data.frame(table(datfam[, c(1, 3, 4)]))
        family2 = as.vector(fam2[fam2$Freq == max(fam2$Freq, na.rm = TRUE),
            "fam1"])
        order2 = as.vector(fam2[fam2$Freq == max(fam2$Freq, na.rm = TRUE),
            "order1"])
        class2 = as.vector(fam2[fam2$Freq == max(fam2$Freq, na.rm = TRUE),
            "class1"])
        if (length(fam2) > 1) {
            datfam2 = datfam[datfam$fam1 %in% family2, ]
            family2 = as.vector(datfam2[datfam2$order == min(datfam2$order,
                na.rm = TRUE), "fam1"])
            order2 = as.vector(datfam2[datfam2$order == min(datfam2$order,
                na.rm = TRUE), "order1"])
            class2 = as.vector(datfam2[datfam2$order == min(datfam2$order,
                na.rm = TRUE), "class1"])
        }
    } else {
        # for everything else
        fam1 = syns$family[!is.na(syns$family) & !is.na(syns$phylum) &
            (syns$phylum %in% phyla)]
        order1 = syns$order[!is.na(syns$family) & !is.na(syns$phylum) &
            (syns$phylum %in% phyla)]
        class1 = syns$class[!is.na(syns$family) & !is.na(syns$phylum) &
            (syns$phylum %in% phyla)]
        datfam = data.frame(fam1 = fam1, order = 1:length(fam1), order1 = order1,
            class1 = class1)
        # select highest frequency fam/class/order combo
        fam2 = as.data.frame(table(datfam[, c(1, 3, 4)]))
        family2 = as.vector(fam2[fam2$Freq == max(fam2$Freq, na.rm = TRUE),
            "fam1"])
        order2 = as.vector(fam2[fam2$Freq == max(fam2$Freq, na.rm = TRUE),
            "order1"])
        class2 = as.vector(fam2[fam2$Freq == max(fam2$Freq, na.rm = TRUE),
            "class1"])
        # select highest in list if more than one max
        if (length(family2) > 1) {
            datfam2 = datfam[datfam$fam1 %in% family2, ]
            family2 = as.vector(datfam2[datfam2$order == min(datfam2$order,
                na.rm = TRUE), "fam1"])
            order2 = as.vector(datfam2[datfam2$order == min(datfam2$order,
                na.rm = TRUE), "order1"])
            class2 = as.vector(datfam2[datfam2$order == min(datfam2$order,
                na.rm = TRUE), "class1"])
        }
    }

    # (4) search for species synonyms in ITIS
    syns = tryCatch(suppressMessages(synonyms(taxa, db = "itis")),
        error = function(e) e)
    if (class(syns)[1] == "simpleError") {
        return(data.frame(Original = taxname, Submitted = taxa, Accepted_name = "failed",
            Selected_family = family2, Selected_order = order2, Selected_class = class2,
            Synonyms = "failed"))
    }
    syns = as.data.frame(syns[[1]])

    # get info
    original = taxa
    accepted_name = taxa  # save accepted name as original searched name
    if ("acc_name" %in% names(syns))
        {
            accepted_name = syns$acc_name
        }  # unless search shows that this is not the accepted name
    if ("syn_name" %in% names(syns)) {
        synonyms = unique(syns$syn_name)
    } else {
        synonyms = NA
    }

    # combine into list and add synonyms
    result = data.frame(Original = taxname, Submitted = taxa, Accepted_name = accepted_name,
        Selected_family = family2, Selected_order = order2, Selected_class = class2)
    result = do.call("rbind", replicate(length(synonyms), result[1,
        ], simplify = FALSE))
    result$Synonyms = synonyms
    return(result)
}

# nest function within a tryCatch call in case of any errors
findSyns3 = function(x) {
    result = tryCatch(findSyns2(x), error = function(e) NULL)
    return(result)
}

Antes vimos que hay un nombre que es sinónimo de Ramphocelus sanguinolentus ¿qué haría la función de Gibb et al con ese?

Ram.syn1 <- findSyns3("Phlogothraupis sanguinolenta")
## [1] "Processing: Phlogothraupis sanguinolenta"
## ══  1 queries  ═══════════════
## ✔  Found:  Phlogothraupis sanguinolenta
## ══  Results  ═════════════════
## 
## • Total: 1 
## • Found: 1 
## • Not Found: 0
Ram.syn1 %>%
    kbl() %>%
    kable_minimal()
Original Submitted Accepted_name Selected_family Selected_order Selected_class Synonyms
Phlogothraupis sanguinolenta Phlogothraupis sanguinolenta Ramphocelus sanguinolentus Thraupidae Passeriformes Aves Phlogothraupis sanguinolenta

¿Y con uno que está mal escrito?

Ram.syn2 <- findSyns3(Ram.names2[2])
## [1] "Processing: Ramphocelus passerini"
## ══  1 queries  ═══════════════
## ✔  Found:  Ramphocelus passerinii
## ══  Results  ═════════════════
## 
## • Total: 1 
## • Found: 1 
## • Not Found: 0
Ram.syn2 %>%
    kbl() %>%
    kable_minimal()
Original Submitted Accepted_name Selected_family Selected_order Selected_class Synonyms
Ramphocelus passerini Ramphocelus passerinii Ramphocelus passerinii Thraupidae Passeriformes Aves NA

Manejo de datos de cámaras trampa

Artículo de Niedballa et al. 2016,Methods Ecol. Evol.

De la sección de métodos:

“Users are free to use any species names (or abbreviations or codes) they wish. If scientific or common species names are used, the function checkSpeciesNames can check them against the ITIS taxonomic database (www.itis.gov) and returns their matching counterparts (utilizing the R package taxize (Chamberlain & Szöcs 2013) internally), making sure species names and spelling are standardized and taxonomically sound, and thus making it easier to combine data sets from different studies.”

Veamos algunos ejemplos del las viñetas de camtrapR. No vamos a bajar el paquete, porque la versión de CRAN de ‘camtrapR’ no es compatible con la versión de CRAN de ‘taxize’

Vamos a reescribir la función actualizando los argumentos de ‘taxize’

checkSpeciesNames <- function(speciesNames, searchtype, accepted = TRUE,
    ask = TRUE) {
    if (!requireNamespace("taxize", quietly = TRUE)) {
        stop("Please install the package taxize to run this function")
    }
    if (!requireNamespace("ritis", quietly = TRUE)) {
        stop("Please install the package ritis to run this function")
    }
    searchtype <- match.arg(searchtype, choices = c("scientific",
        "common"))
    stopifnot(is.logical(accepted))
    stopifnot(is.character(speciesNames) | is.factor(speciesNames))
    speciesNames <- unique(as.character(speciesNames))
    file.sep <- .Platform$file.sep
    tsns <- try(taxize::get_tsn(sci_com = speciesNames, searchtype = searchtype,
        accepted = accepted, ask = ask, messages = FALSE))
    if (inherits(tsns, "try-error")) {
        message(paste("error in get_tsn. Exiting without results:\n",
            tsns, sep = ""))
        return(invisible(NULL))
    }
    tsns <- taxize::as.tsn(unique(tsns), check = FALSE)
    if (any(is.na(tsns))) {
        not.matched <- which(is.na(tsns))
        warning(paste("found no matches for", length(not.matched),
            "name(s):\n", paste(speciesNames[not.matched], collapse = ", ")),
            immediate. = TRUE, call. = FALSE)
        tsns_worked <- taxize::as.tsn(tsns[-not.matched], check = FALSE)
    } else {
        tsns_worked <- tsns
    }
    if (length(tsns_worked) >= 1) {
        scientific <- common <- author <- rankname <- taxon_status <- data.frame(matrix(NA,
            nrow = length(tsns_worked), ncol = 2), stringsAsFactors = FALSE)
        colnames(scientific) <- c("tsn", "combinedname")
        colnames(common) <- c("tsn", "commonName")
        colnames(author) <- c("tsn", "authorship")
        colnames(rankname) <- c("tsn", "rankname")
        colnames(taxon_status) <- c("tsn", "taxonUsageRating")
        for (i in 1:length(tsns_worked)) {
            scientific_tmp <- ritis::scientific_name(tsns_worked[i])
            common_tmp <- ritis::common_names(tsns_worked[i])
            author_tmp <- ritis::taxon_authorship(tsns_worked[i])
            rankname_tmp <- ritis::rank_name(tsns_worked[i])
            if ("tsn" %in% colnames(scientific_tmp)) {
                scientific[i, ] <- scientific_tmp[c("tsn", "combinedname")]
            }
            if ("tsn" %in% colnames(common_tmp)) {
                if (table(common_tmp$tsn) > 1) {
                  common2 <- tapply(common_tmp$commonName, INDEX = common_tmp$tsn,
                    FUN = paste, collapse = file.sep)
                  common_tmp <- data.frame(commonName = common2, tsn = rownames(common2),
                    stringsAsFactors = FALSE)
                }
                common[i, ] <- common_tmp[, c("tsn", "commonName")]
            }
            if ("tsn" %in% colnames(author_tmp)) {
                author[i, ] <- author_tmp[c("tsn", "authorship")]
            }
            if ("tsn" %in% colnames(rankname_tmp)) {
                rankname[i, ] <- rankname_tmp[c("tsn", "rankname")]
            }
            if (accepted == FALSE) {
                taxon_status_tmp <- ritis::core_metadata(tsns_worked[i])
                if ("tsn" %in% colnames(taxon_status_tmp)) {
                  taxon_status[i, ] <- taxon_status_tmp[c("tsn", "taxonUsageRating")]
                }
            }
        }
        dat.out <- data.frame(user_name = speciesNames, tsn = as.numeric(tsns))
        dat.out <- merge(x = dat.out, y = scientific, by = "tsn",
            all.x = TRUE, sort = FALSE)
        dat.out <- merge(x = dat.out, y = common, by = "tsn", all.x = TRUE,
            sort = FALSE)
        dat.out <- merge(x = dat.out, y = author, by = "tsn", all.x = TRUE,
            sort = FALSE)
        dat.out <- merge(x = dat.out, y = rankname, by = "tsn", all.x = TRUE,
            sort = FALSE)
        dat.out$itis_url <- NA
        dat.out$itis_url[match(tsns_worked, dat.out$tsn)] <- attributes(tsns_worked)$uri
        colnames(dat.out)[colnames(dat.out) == "combinedname"] <- "scientificName"
        if (accepted == FALSE) {
            dat.out <- merge(x = dat.out, y = taxon_status, by = "tsn",
                all.x = TRUE, sort = FALSE)
        } else {
            dat.out$taxon_status[!is.na(dat.out$tsn)] <- "valid"
        }
        return(dat.out)
    } else {
        stop("found no TSNs for speciesNames", call. = FALSE)
    }
}

¡Ahora sí! ¿Qué podemos hacer?

Buscar información con nombres comunes

checkNames1 <- checkSpeciesNames(speciesNames = c("Bearded Pig", "Malayan Civet"),
    searchtype = "common")
checkNames1 %>%
    kbl() %>%
    kable_minimal()
tsn user_name scientificName commonName authorship rankname itis_url taxon_status
625012 Bearded Pig Sus barbatus bearded pig/Bearded Pig Müller, 1838 Species https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=625012 valid
622004 Malayan Civet Viverra tangalunga Malayan Civet Gray, 1832 Species https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=622004 valid

Buscar información con nombres científicos (incluyendo subespecie)

checkNames2 <- checkSpeciesNames(speciesNames = "Viverra tangalunga tangalunga",
    searchtype = "scientific")
checkNames2 %>%
    kbl() %>%
    kable_minimal()
tsn user_name scientificName commonName authorship rankname itis_url taxon_status
726578 Viverra tangalunga tangalunga Viverra tangalunga tangalunga NA Gray, 1832 Subspecies https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=726578 valid

Buscar información con un nombre incorrecto

checkNames3 <- checkSpeciesNames(speciesNames = "Felis bengalensis",
    searchtype = "scientific", accepted = FALSE)
checkNames3 %>%
    kbl() %>%
    kable_minimal()
tsn user_name scientificName commonName authorship rankname itis_url taxonUsageRating
183793 Felis bengalensis Felis bengalensis leopard cat Kerr, 1792 Species https://www.itis.gov/servlet/SingleRpt/SingleRpt?search_topic=TSN&search_value=183793 invalid

Buscar información con un nombre ambiguo

checkNames4 <- checkSpeciesNames(speciesNames = "Chevrotain", searchtype = "common")
# escoger del menu
1
checkNames4 %>%
    kbl() %>%
    kable_minimal()

Información de la sesión

sessionInfo()
## R version 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0
## 
## locale:
##  [1] LC_CTYPE=es_ES.UTF-8      
##  [2] LC_NUMERIC=C              
##  [3] LC_TIME=es_CR.UTF-8       
##  [4] LC_COLLATE=es_ES.UTF-8    
##  [5] LC_MONETARY=es_CR.UTF-8   
##  [6] LC_MESSAGES=es_ES.UTF-8   
##  [7] LC_PAPER=es_CR.UTF-8      
##  [8] LC_NAME=C                 
##  [9] LC_ADDRESS=C              
## [10] LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=es_CR.UTF-8
## [12] LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets 
## [6] methods   base     
## 
## other attached packages:
##  [1] plyr_1.8.7          rgbif_3.7.3        
##  [3] taxize_0.9.100      tufte_0.12         
##  [5] rticles_0.24        revealjs_0.9       
##  [7] rmdformats_1.0.4    kableExtra_1.3.4   
##  [9] rmarkdown_2.14      sketchy_1.0.2      
## [11] remotes_2.4.2       leaflet_2.1.1      
## [13] knitr_1.39          xaringanExtra_0.7.0
## [15] emo_0.0.0.9000      cowsay_0.8.0       
## 
## loaded via a namespace (and not attached):
##   [1] uuid_1.1-0          workflowr_1.7.0    
##   [3] systemfonts_1.0.4   lazyeval_0.2.2     
##   [5] crosstalk_1.2.0     usethis_2.1.6      
##   [7] ggplot2_3.3.6       urltools_1.7.3     
##   [9] digest_0.6.29       foreach_1.5.2      
##  [11] htmltools_0.5.3     rsconnect_0.8.26   
##  [13] fansi_1.0.3         magrittr_2.0.3     
##  [15] memoise_2.0.1       vertical_0.1.0.0000
##  [17] svglite_2.1.0       prettyunits_1.1.1  
##  [19] colorspace_2.0-3    rvest_1.0.2        
##  [21] xfun_0.31           dplyr_1.0.9        
##  [23] callr_3.7.0         crayon_1.5.1       
##  [25] jsonlite_1.8.0      zoo_1.8-10         
##  [27] iterators_1.0.14    ape_5.6-2          
##  [29] glue_1.6.2          gtable_0.3.0       
##  [31] emmeans_1.7.4-1     webshot_0.5.3      
##  [33] pkgbuild_1.3.1      scales_1.2.0       
##  [35] oai_0.3.2           mvtnorm_1.1-3      
##  [37] solrium_1.2.0       DBI_1.1.3          
##  [39] Rcpp_1.0.9          viridisLite_0.4.0  
##  [41] xtable_1.8-4        bold_1.2.0         
##  [43] clisymbols_1.2.0    datawizard_0.4.1   
##  [45] htmlwidgets_1.5.4   httr_1.4.3         
##  [47] papaja_0.1.1        RColorBrewer_1.1-3 
##  [49] ellipsis_0.3.2      pkgconfig_2.0.3    
##  [51] reshape_0.8.9       sass_0.4.1         
##  [53] utf8_1.2.2          here_1.0.1         
##  [55] conditionz_0.1.0    crul_1.2.0         
##  [57] tidyselect_1.1.2    rlang_1.0.4        
##  [59] later_1.3.0         effectsize_0.7.0   
##  [61] munsell_0.5.0       tools_4.1.1        
##  [63] cachem_1.0.6        fortunes_1.5-4     
##  [65] cli_3.3.0           generics_0.1.2     
##  [67] tinylabels_0.2.3    devtools_2.4.3     
##  [69] evaluate_0.15       stringr_1.4.0      
##  [71] fastmap_1.1.0       yaml_2.3.5         
##  [73] processx_3.6.1      fs_1.5.2           
##  [75] purrr_0.3.4         ritis_1.0.0        
##  [77] packrat_0.8.0       rrtools_0.1.5      
##  [79] nlme_3.1-152        whisker_0.4        
##  [81] formatR_1.12        xml2_1.3.3         
##  [83] brio_1.1.3          compiler_4.1.1     
##  [85] rstudioapi_0.13     curl_4.3.2         
##  [87] testthat_3.1.4      tibble_3.1.8       
##  [89] bslib_0.3.1         stringi_1.7.8      
##  [91] highr_0.9           ps_1.7.1           
##  [93] parameters_0.18.1   desc_1.4.1         
##  [95] lattice_0.20-44     vctrs_0.4.1        
##  [97] pillar_1.8.0        lifecycle_1.0.1    
##  [99] rmsfact_0.0.3       triebeard_0.3.0    
## [101] jquerylib_0.1.4     estimability_1.3   
## [103] data.table_1.14.2   insight_0.17.1     
## [105] httpuv_1.6.5        R6_2.5.1           
## [107] bookdown_0.27       promises_1.2.0.1   
## [109] sessioninfo_1.2.2   codetools_0.2-18   
## [111] assertthat_0.2.1    pkgload_1.2.4      
## [113] rprojroot_2.0.3     withr_2.5.0        
## [115] httpcode_0.3.0      bayestestR_0.12.1  
## [117] parallel_4.1.1      grid_4.1.1         
## [119] coda_0.19-4         git2r_0.30.1       
## [121] getPass_0.2-2       lubridate_1.8.0