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Although species ranges may be obtained using expert maps (e.g., IUCN and EUFORGEN) or modeling methods, expert data remains limited in the number of available species while applying models usually need more technical expertise, as well as many species observations.
When unavailable, such information may be extracted from the Global Biodiversity Information facility (GBIF), the largest public data repository inventorying georeferenced species observations worldwide (https://www.gbif.org/). However, retrieving GBIF records at large scale in R may be tedious, if users are unaware of the limitations of the rgbif library.
Here we present gbif.range, a R library that contains automated methods to generate species range maps from scratch using in-house ecoregions shapefiles and an easy-to-use GBIF download wrapper. Finally, this library also offers a set of additional very useful tools for large GBIF datasets (generate doi, extract GBIF taxonomy, records filtering...).
(source: globe image from the Noun Project adapted by LenaCassie-Studio)
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get_gbif(): improves the accessibility of the rgbif R package (CRAN) in retrieving GBIF observations of a given species (accepted and synonym names). It uses a dynamic moving windows if the given geographic extent contains > 100,000 observations and implements 13 post-processing options to flag and clean erroneous records based on custom functions and the CoordinateCleaner R package (CRAN).
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get_range(): estimates species ranges based on occurrence data (a get_gbif output or a set of coordinates) and ecoregion polygons.
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read_bioreg(): download and read available ecoregion files from different available URL sources. See also associated calls bioreg_list, get_bioreg() and check_and_get_bioreg().
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get_status(): Generates, based on a given species name, its IUCN red list status and a list of all scientific names (accepted, synonyms) found in the GBIF backbone taxonomy to download the data. Children and related doubtful names not used to download the data may also be extracted.
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obs_filter(): obs_filter() accepts as input a get_gbif() output (one or several species) and filter the observations according to a specific given grid resolution (one observation per pixel grid kept). This function allows the user to refine the density of GBIF observations according to a defined analysis/study's resolution.
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make_tiles(): May be used to generate a set of SpatialExtent and geometry arguments POLYGON() based on a given geographic extent. This function is meant to help users who want to use the rgbif R package and its parameter geometry that uses a POLYGON() argument.
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get_doi(): A small wrapper of derived_dataset() in rgbif that simplifies the obtention of a general DOI for a set of several gbif species datasets.
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make_ecoregion(): A function to create custom ecoregions based on environmental layers.
You can install the development version from GitHub with:
remotes::install_github("8Ginette8/gbif.range")
library(gbif.range)
library(terra)
library(rnaturalearth)Let's download worldwide the records of Panthera tigris only based on true observations:
# Download
obs_pt = get_gbif(sp_name="Panthera tigris",
basis=c("OBSERVATION","HUMAN_OBSERVATION","MACHINE_OBSERVATION"))
# Plot species records
countries = vect(ne_countries(type = "countries",returnclass = "sf"))
plot(countries,col="#bcbddc")
points(obs_pt[,c("decimalLongitude","decimalLatitude")],pch=20,col="#99340470",cex=1.5)
Note that an additional filtering needs here to be done as one observation is found in the 🔷US🔷. A lot of tigers are being captive in this country hence the recorded observation. Therefore using additional functions from CoordinateCleaner might solve this issue. We can also retrieve the tiger IUCN red list status, and its scientific names (accepted and synonyms) that were used in the download with the GBIF backbone taxonomy. If all = TRUE, additonal children and related doubtful names may also be extracted (not used in get_gbif()):
get_status("Panthera tigris",all=FALSE)Let's now extract the terrestrial ecoregions of the world (Nature Conservancy) and generate the distributional range map of Panthera tigris :
# Download ecoregion and read
eco_terra = read_bioreg(bioreg_name = "eco_terra", save_dir = NULL)
# Range
range_tiger = get_range(occ_coord=obs_pt,
bioreg=eco_terra,
bioreg_name="ECO_NAME")Let's plot the result now:
plot(countries,col="#bcbddc")
plot(range_tiger,col="#238b45",add=TRUE,axes=FALSE,legend=FALSE)
Interestingly no tiger range was found in the US. Our get_range default parameters allowed the one US record of Panthera tigris to be flagged and considered as an outlier. Note that five parameters need to be set in get_range and those should be carefully explored before any definite map process.
Although whatever shapefile may be set in get_range() as input, note that ecoregion shapefiles may be dowload using the package: eco.earh (for terrestrial species; The Nature conservancy 2009 adapted from Olson & al. 2001), eco.marine (for marine species, two versions; The Nature Conservancy 2012 adapted from Spalding & al. 2007, 2012) and eco.fresh (for freshwater species; Abell & al. 2008). Each are available under different precision levels:
- eco_terra has three different levels: 'ECO_NAME', 'WWF_MHTNAM' and 'WWF_REALM2'.
- eco_fresh has only one: 'ECOREGION'.
- eco_marine and eco_hd_marine (very coastal-precise version) contains three distinct levels: 'ECOREGION', 'PROVINCE' and 'REALM'.
Available ecoregion files that can be downloaded with the package:
# List
bioreg_listAdditonally, if the in-house ecoregions are too coarse for a given geographic region (e.g., for local studies) or an ecoshapefile of finer environmental details is needed, make_ecoregion() can be used based on spatially-informed environment (e.g. climate) of desired resolution and extent defining the study area; example:
# Let's download the observations of Arctostaphylos alpinus in the European Alps:
shp_lonlat = vect(paste0(system.file(package = "gbif.range"),"/extdata/shp_lonlat.shp"))
obs_arcto = get_gbif("Arctostaphylos alpinus",geo=shp_lonlat)
# Create an ecoregion layer of 200 classes, based on 12 environmental spatial layers:
rst = rast(paste0(system.file(package = "gbif.range"),"/extdata/rst.tif"))
my_eco = make_ecoregion(rst,200)
# Create the range map based on our custom ecoregion
# (always set 'EcoRegion' as a name when using a make_ecoregion() output):
range_arcto = get_range(occ_coord=obs_arcto,
bioreg=my_eco,
bioreg_name="EcoRegion",
res=20,
degrees_outlier = 5,
clustered_points_outlier = 2,
buffer_width_point = 4,
buffer_increment_point_line = 0.5,
buffer_width_polygon = 0.1)Here we adapted the extra-parameters to the extent of the study area, e.g., (i) consider points as outliers (a maximum group of two points) if this bunch is away > 555km (1° ~ 111km) from the other cluster points and (ii) apply a buffer of ~10km around the drawn polygons.
# Plot
plot(rst[[1]])
plot(shp_lonlat,add=TRUE)
plot(range_arcto,add=TRUE,col="darkgreen",axes=FALSE,legend=FALSE)
points(obs_arcto[,c("decimalLongitude","decimalLatitude")],pch=20,col="#99340470",cex=1)
Let's reapply the same process as for Panthera tigris, but with the marine species Delphinus delphis (> 100'000 observations).
obs_dd = get_gbif("Delphinus delphis",occ_samp=1000) # Here the example is a sample of 1000 observations per geographic tile
get_status("Delphinus delphis",all=TRUE) # Here the list is longer because 'all=TRUE' includes every names (even doubtful)Let's now generate three range maps of Delphinus delphis using the eco.marine as ecoregion shapefile:
# Download ecoregion and read
eco_marine = read_bioreg(bioreg_name = "eco_marine", save_dir = NULL)
# Range from different levels
range_dd1 = get_range(obs_dd,eco_marine,"ECOREGION")
range_dd2 = get_range(obs_dd,eco_marine,"PROVINCE")
range_dd3 = get_range(obs_dd,eco_marine,"REALM")The three results are pretty similar because most of the observations are near the coast. But let's plot the third result:
plot(countries,col="#bcbddc")
plot(range_dd3,col="#238b45",add=TRUE,axes=FALSE,legend=FALSE)
points(obs_dd[,c("decimalLongitude","decimalLatitude")],pch=20,col="#99340470",cex=1)
Althought our result map follows the sampling pattern found in GBIF, the dolphin range map might have been improved if more GBIF observations woud have been extracted. Therefore, occ_samp must be in this case increased or removed.
Yohann Chauvier; Oskar Hagen; Camille Albouy; Patrice Descombes; Fabian Fopp; Michael P. Nobis; Philipp Brun; Lisha Lyu; Loïc Pellissier; Katalin Csilléry (2022). gbif.range - An R package to generate species range maps based on ecoregions and a user-friendly GBIF wrapper. EnviDat. doi: 10.16904/envidat.352
Oskar Hagen, Lisa Vaterlaus, Camille Albouy, Andrew Brown, Flurin Leugger, Renske E. Onstein, Charles Novaes de Santana, Christopher R. Scotese, Loïc Pellissier. (2019) Mountain building, climate cooling and the richness of cold-adapted plants in the Northern Hemisphere. Journal of Biogeography. doi: 10.1111/jbi.13653
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The Nature Conservancy (2012). Marine Ecoregions and Pelagic Provinces of the World. GIS layers developed by The Nature Conservancy with multiple partners, combined from Spalding et al. (2007) and Spalding et al. (2012). Cambridge (UK): The Nature Conservancy. Data URL: http://data.unep-wcmc.org/datasets/38
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