Assessing habitat specialization using IUCN data

Since quite some time ecological models have tried to incorporate both continuous and discrete characteristics of species into their models. Newbold et al. (2013) demonstrated that functional traits affect the response of tropical bird species towards land-use intensity. Tropical forest specialist birds seem to decrease globally in probability of presence and abundance in more intensively used forests. This patterns extends to many taxonomic groups and the worldwide decline of “specialist species” has been noted before by Clavel et al. (2011).

From Newbold et al. 2013

(a) Probabilities of presence of tropical bird species in in different disturbed forests and (b) ratios of abundance in light and intensive disturbed forests relative to undisturbed forests. Forest specialists are disproportionally affected in intensively used forests. Figure from Newbold et al. 2013 doi: http://dx.doi.org/10.1098/rspb.2012.2131

But how to acquire such data on habitat specialization? Ether you assemble your own exhaustive trait database or you query information from some of the openly available data sources. One could for instance be the IUCN redlist, which not only has expert-validated data on a species current threat status, but also on population size and also on a species habitat preference. Here IUCN follows its own habitat classification scheme ( http://www.iucnredlist.org/technical-documents/classification-schemes/habitats-classification-scheme-ver3 ). The curious ecologist and conservationist should keep in mind however, that not all species are currently assessed by IUCN.

There are already a lot of scripts available on the net from which you can get inspiration on how to query the IUCN redlist (Kay Cichini from the biobucket explored this already in 2012 ). Even better: Someone actually compiled a whole r-package called letsR full of web-scraping functions to access the IUCN redlist. Here is some example code for Perrin’s Bushshrike, a tropical bird quite common in central Africa

# Install package
install.packages(letsR)
library(letsR)

# Perrin's or Four-colored Bushshrike latin name
name <- 'Telophorus viridis'

# Query IUCN status
lets.iucn(name)

#>Species        Family Status Criteria Population Description_Year
#>Telophorus viridis MALACONOTIDAE LC Stable 1817
#>Country
#>Angola, Congo, The Democratic Republic of the Congo, Gabon, Zambia

# Or you can query habitat information
lets.iucn.ha(name)

#>Species Forest Savanna Shrubland Grassland Wetlands Rocky areas Caves and Subterranean Habitats
#>Telophorus viridis      1       1         1         0        0           0                               0
#> Desert Marine Neritic Marine Oceanic Marine Deep Ocean Floor Marine Intertidal Marine Coastal/Supratidal
#>      0              0              0                       0                 0                         0
#>  Artificial/Terrestrial Artificial/Aquatic Introduced Vegetation Other Unknown
#>                      1                  0                     0     0       0

letsR also has other methods to work with the spatial data that IUCN provides ( http://www.iucnredlist.org/technical-documents/spatial-data ), so definitely take a look. It works by querying the IUCN redlist api for the species id (http://api.iucnredlist.org/go/Telophorus-viridis). Sadly the habitat function does only return the information if a species is known to occur in a given habitat, but not if it is of major importance for a particular species (so if for instance a Species is known to be a “forest-specialist” ). Telophorus viridis for instance also occurs in savannah and occasionally artificial habitats like gardens ( http://www.iucnredlist.org/details/classify/22707695/0 ).

So I just programmed my own function to assess if forest habitat is of major importance to a given species. It takes a IUCN species id as input and returns ether “Forest-specialist”, if forest habitat is of major importance to a species, “Forest-associated” if a species is just known to occur in forest or “Other Habitats” if a species does not occur in forests at all. The function works be cleverly querying the IUCN redlist and breaking up the HTML structure at given intervals that indicate a new habitat type.

Find the function on gist.github (Strangely WordPress doesn’t include them as they promised)

How does it work? You first enter the species IUCN redlist id. It is in the url after you have queried a given species name. Alternatively you could also download the whole IUCN classification table and match your species name against it 😉 Find it here. Then simply execute the function with the code.

name = 'Telophorus viridis'
data <- read.csv('all.csv')
# This returns the species id
data$Red.List.Species.ID[which(data$Scientific.Name==name)]
#> 22707695

# Then simply run my function
isForestSpecialist(22707695)
#> 'Forest-specialist'

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About Martin Jung

PhD researcher at the University of Sussex. Interested in nature conservation, ecology and biodiversity as well as statistics, GIS and 'big data'

3 responses to “Assessing habitat specialization using IUCN data”

  1. pvanb says :

    Nice work. And as an observation (although you are of course free to take this as a feature request 😉 ), it should be easy to extent/adapt the script if one is interested in other habitat specialists.

    • Curlew says :

      Sure is. In the gist just replace the “forest” with “savanna” or any other IUCN habitat class. I was only interested in forest specialists, but other kinds of habitat specialists could be queried as well.

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