IUCN distribution data

IUCN species distribution maps are widely used to conduct spatial analyses. However, these data are not always as accurate in some areas of the globe. Uruguay, in particular, has a severe lack of information on the distribution of its species. Luckily, last year the first comprehensive open-access biodiversity database in the country was made available by Biodiversidata. We are going to use this data to check how well represented are these species in the IUCN Red List database.

Let’s see !

The vertebrates species list includes 664 tetrapod species.

## # A tibble: 4 x 2
## # Groups:   class [4]
##   class        n
##   <chr>    <int>
## 1 Amphibia    50
## 2 Aves       430
## 3 Mammalia   116
## 4 Reptilia    68

To analyse the data availabe in IUCN for Uruguay we are going to use the function rl_occ_country from the package rredlist. This function enables us to get country occurrence by species name.



Function

Let’s create a function (getIUCNSpeciesInUy) to find if the species recorded in Uruguay are documented as extant in the country according to IUCN.

library(rredlist)
library(taxize)
library(tidyverse)

getIUCNSpeciesInUy <- function(data){
  speciesIsinUy <- data.frame(species= character(),
                              presence=character(),
                              establishmentMeans=character(),
                              numCountries= numeric(),
                              stringsAsFactors = FALSE)
  for (species in data$species){
    isInUy <- rl_occ_country(species)
    if (isInUy$count == 0) {
      speciesIsinUy_species <- data.frame(species= species,
                                          presence= 'NOT FOUND',
                                          establishmentMeans= NA,
                                          numCountries=NA,
                                          stringsAsFactors = F)
      speciesIsinUy <- rbind(speciesIsinUy, speciesIsinUy_species)
    }
    else if ('UY' %in% isInUy$result$code) {
      speciesIsinUy_species <- data.frame(species= species,
                                          presence= isInUy$result$presence[isInUy$result$code=='UY'],
                                          establishmentMeans=isInUy$result$origin[isInUy$result$code=='UY'],
                                          numCountries=isInUy$count,
                                          stringsAsFactors = F)
      speciesIsinUy <- rbind(speciesIsinUy, speciesIsinUy_species)
    }
    else {
      speciesIsinUy_species <- data.frame(species= species,
                                          presence= 'Not recorded in UY',
                                          establishmentMeans= 'Not recorded in UY',
                                          numCountries=isInUy$count,
                                          stringsAsFactors = F)
      speciesIsinUy <- rbind(speciesIsinUy, speciesIsinUy_species)
    }
  }
  return(speciesIsinUy)
}

The function needs an object (dataframe) with a column named species, which will be used for the search.

  • If the species is not found within the IUCN database, a ‘NOT FOUND’ message will be retrieved, and we will later need to check if we can find a synonym species in IUCN.
  • Else, if the species name is found in IUCN, the function will search if it is recorded in Uruguay (code = ‘UY’).
  • Then, if the species is found in Uruguay the function will retrieve the presence status (presence), establishment means (establishmentMeans) and just as a curiosity, the number of countries the species is recorded in (numCountries).
  • Else, if the species is not found in Uruguay, it will retrieve ‘Not recorded in UY’.



Run

Lets run the function with our data:

IUCNSpeciesInUy <- getIUCNSpeciesInUy(speciesList_Tetrapods)

This function might take a while to run, depending on the size of your species list 🍵 🍪



Explore

So, let’s see how it went.

IUCNSpeciesInUy %>%  
  select(-numCountries) %>% 
  head(n=20)

##                           species           presence establishmentMeans
## 1          Dendropsophus sanborni             Extant             Native
## 2          Elachistocleis bicolor             Extant             Native
## 3                 Boana pulchella             Extant             Native
## 4          Leptodactylus gracilis             Extant             Native
## 5         Leptodactylus latinasus             Extant             Native
## 6        Leptodactylus mystacinus             Extant             Native
## 7           Leptodactylus latrans             Extant             Native
## 8  Melanophryniscus montevidensis             Extant             Native
## 9        Odontophrynus americanus             Extant             Native
## 10          Odontophrynus maisuma          NOT FOUND               <NA>
## 11           Physalaemus gracilis             Extant             Native
## 12             Pleurodema bibroni             Extant             Native
## 13                 Pseudis minuta             Extant             Native
## 14      Pseudopaludicola falcipes             Extant             Native
## 15              Rhinella arenarum             Extant             Native
## 16             Rhinella dorbignyi             Extant             Native
## 17              Scinax granulatus             Extant             Native
## 18           Scinax squalirostris             Extant             Native
## 19          Argenteohyla siemersi             Extant             Native
## 20              Rhinella diptycha Not recorded in UY Not recorded in UY

According to IUCN, 76 species in our databse are not considered extant in the country. There’s a species considered extinct (Blastocerus dichotomus) and one as possibly extinct (Chrysocyon brachyurus). But, what about those NOT FOUND? We need to check on their synonym species.

To find synonym species, we can use the function synonyms from the package taxize. First, filter the species not found for IUCN, then create a new list with this species and run the function getSynonym. Finally, run the function to check for these species again.

getSynonym <- function(species){
  species_synonym <- synonyms(species, db='itis', rows=1)
  if(is.na(species_synonym) || is_empty(species_synonym[[1]])){
    species_synonym$synonym <- 'NOT FOUND'
  }
  else {
    species_synonym$synonym <- species_synonym[[1]]$syn_name[1]
  }
  return(species_synonym$synonym)
}

synonym_IUCNSpeciesInUy <- IUCNSpeciesInUy %>% 
  filter(presence=='NOT FOUND') %>% 
  mutate(speciesSynonym=map_chr(species, getSynonym)) 

Once we have checked if the synonyms are still not found in the IUCN database - therefore not assessed - we have all the information we need.



So, let’s explore the final results:

Data on IUCN Number of Species %
Extant 559 84.2
Extinct Post-1500 1 0.2
Not assessed 22 3.3
Not recorded in UY 81 12.2
Possibly Extinct 1 0.2

Around 12% of the species occurring in Uruguay are not recorded as present in the country according to the IUCN distribution maps. Also, these species seem to be more range restricted, as they are recorded in less countries (mean=18.1, sd=25), while the extant species are present in more countries (mean=24.2, sd=42.5).



And, that’s all !

Take this gaps in mind next time you want to map species using IUCN species range maps.

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Florencia Grattarola
PhD Student in Life Sciences

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