Area of Habitat (AOH) maps aim to delineate the spatial distribution of
suitable habitat for a species (Brooks et al.
2019). They are used to
assess performance of protected area systems, measure impacts of threats
to biodiversity, and identify priorities for conservation actions (e.g.,
Rondinini et al.
2005; Tracewski et
al. 2016; Durán et al.
2021). These maps are
generally produced by obtaining geographic range data for a species, and
then removing areas that do not contain suitable habitat or occur
outside the known elevational limits for the species (Brooks et al.
2019). To help make these
maps accessible, the aoh R package provides routines for automatically
creating Area of Habitat data based on the International Union for
Conservation of Nature (IUCN) Red List of Threatened
Species. After manually downloading
species range data from the IUCN Red
List,
users can import them (using read_spp_range_data()), prepare them and
collate additional information for subsequent processing (using
create_spp_info_data()), and then create Area of Habitat data (using
create_spp_aoh_data()). Global elevation and habitat classification
data (Jung et al. 2020;
Lumbierres et al. 2021;
Robinson et al.
2014) are
automatically downloaded, and data on species’ habitat preferences and
elevational limits are obtained automatically using the IUCN Red List
API. Since accessing the IUCN Red List
requires a token, users may need to obtain a
token and update their R
configuration to recognize the token (see installation instructions
below for details).
The latest developmental version of the aoh R package can be installed using the following R code. Please note that it requires the sf, terra, and zen4R R packages, which may require additional software to be installed. If you encounter problems installing these dependencies, please consult their installation instructions.
if (!require(remotes)) install.packages("remotes")
remotes::install_github("prioritizr/aoh")The aoh R package can leverage the prepr R package to augment data cleaning procedures. Since the prepr R package is not available on the Comprehensive R Archive Network (CRAN), it is listed as an optional dependency. In some cases, the prepr R package is required to complete the data cleaning procedures (e.g., to fix especially extreme geometry issues) and the aoh R package will throw an error if the package is not available. To install the prepr R package, please use the following R code. Note that the prepr R package has system dependencies that need to be installed before the package itself can be installed (see below for platform-specific instructions).
if (!require(remotes)) install.packages("remotes")
remotes::install_github("dickoa/prepr")The package can also leverage the Geospatial Data Abstraction Library (GDAL) and Geographic Resources Analysis Support System (GRASS) to help reduce processing time. Although use of these software is optional, they can help improve computational performance when processing data across large spatial extents. Below we provide platform-specific instructions to install dependencies for the prepr R package, GDAL, and GRASS.
The Rtools software
needs to be installed to install the prepr R package package. This
software provides system requirements from
rwinlib. Additionally, the easiest way to
install GDAL and GRASS is through OSGeo4W.
Download the OSGeo4W installer,
select the “Express Install” option, and then follow the prompts to
complete the installation. After the installation process has finished,
please restart your computer. If the aoh::is_gdal_calc_available()
function cannot find GDAL, you may also need to update the PATH
environmental variable so that it contains the folder path for GDAL
(default folder path is C:\OSGeo4W\bin),
For recent versions of Ubuntu (18.04 and later), the libraries are available through official repositories. They can be installed using the following system commands.
apt-get -y update
apt-get install -y \
libgdal-dev libgeos-dev libproj-dev gdal-bin grass \
libgmp3-dev libmpfr-dev libudunits2-dev
For Unix-alikes, gdal (>= 3.0.2), gmp (>= 4.2.3), grass
(>= 7.8.7), and mpfr (>= 3.0.0) are required.
The easiest way to install the libraries is using HomeBrew. After installing HomeBrew, the libraries can be installed using the following system commands.
brew tap osgeo/osgeo4mac
brew install pkg-config
brew install gdal
brew install osgeo-grass
brew install gmp
brew install mpfr
After installing the aoh R package, you will need to obtain a token
for the IUCN Red List API (if you do
not have one already). To do so, please visit the IUCN API website
(https://apiv3.iucnredlist.org/), click the “Generate a token” link at
the top of the web page, and fill out the form to apply for a token. You
should then receive a token shortly after completing the form (but not
immediately). After receiving a token, please open the .Renviron file
on your computer (e.g., using usethis::edit_r_environ()). Next, please
add the following text to the file (replacing the string with the token)
and save the file, using your token in place of the string below.
IUCN_REDLIST_KEY="your_actual_token_not_this_string"
Please restart your R session. You should now be able to access the IUCN Red List API. To verify this, please try running the following R code and – assuming everything works correctly – you should the same output below.
# verify access to IUCN Red List API
is_iucn_rl_api_available()If these instructions did not work, please consult the documentation for the rredlist R package for further details.
Here we provide a short example for using the aoh R package. In this example, we will generate Area of Habitat data for the following Iberian species: Pyrenean brook salamander (Calotriton asper), Iberian frog (Rana iberica), western spadefoot toad (Pelobates cultripes), and golden striped salamnader (Chioglossa lusitanica). Please note that this example is an abridged version of the tutorial provided in the package vignette, so please consult the package vignette for a more detailed tutorial on using the package. To start off, we will load the package and several other packages to help with data processing and visualization.
# load packages
library(aoh)
library(terra)
library(rappdirs)
library(ggplot2)Now we will import range data for the species. Although users would typically obtain range data from the International Union for Conservation of Nature (IUCN) Red List of Threatened Species, here we will use built-in species range data that distributed with the package for convenience. Please note that these data were not obtained from the IUCN Red List, and were manually generated using occurrence records from the Global Biodiversity Information Facility.
# find file path for data
path <- system.file("extdata", "EXAMPLE_SPECIES.zip", package = "aoh")
# import data
spp_range_data <- read_spp_range_data(path)
# preview data
print(spp_range_data)## Simple feature collection with 4 features and 26 fields
## Geometry type: POLYGON
## Dimension: XY
## Bounding box: xmin: -9.479736 ymin: 36.59422 xmax: 3.302702 ymax: 43.76455
## Geodetic CRS: WGS 84
## # A tibble: 4 × 27
## id_no binomial prese…¹ origin seaso…² compi…³ yrcom…⁴ citat…⁵ subsp…⁶ subpop
## <dbl> <chr> <int> <int> <int> <chr> <dbl> <chr> <chr> <chr>
## 1 979 Alytes di… 1 1 1 Derive… NA <NA> <NA> <NA>
## 2 59448 Calotrito… 1 1 1 Derive… NA <NA> <NA> <NA>
## 3 4657 Chiogloss… 1 1 1 Derive… NA <NA> <NA> <NA>
## 4 58622 Rana iber… 1 1 1 Derive… NA <NA> <NA> <NA>
## # … with 17 more variables: source <chr>, island <chr>, tax_comm <chr>,
## # dist_comm <chr>, generalisd <int>, legend <chr>, kingdom <chr>,
## # phylum <chr>, class <chr>, order_ <chr>, family <chr>, genus <chr>,
## # category <chr>, marine <chr>, terrestial <chr>, freshwater <chr>,
## # geometry <POLYGON [°]>, and abbreviated variable names ¹presence,
## # ²seasonal, ³compiler, ⁴yrcompiled, ⁵citation, ⁶subspecies
Next, we will prepare all the range data for generating Area of Habitat data. This procedure – in addition to repairing any geometry issues in the spatial data – will obtain information on the species’ habitat preferences and elevational limits (via the IUCN Red List of Threatened Species). We also specify a folder to cache the downloaded data so that we won’t need to re-download it again during subsequent runs.
# specify cache directory
cache_dir <- user_data_dir("aoh")
# create cache_dir if needed
if (!file.exists(cache_dir)) {
dir.create(cache_dir, showWarnings = FALSE, recursive = TRUE)
}
# prepare information
spp_info_data <- create_spp_info_data(spp_range_data, cache_dir = cache_dir)We can now generate Area of Habitat data for the species. By default, these data will be generated using elevation data derived from Robinson et al. (2014) and habitat data derived from Lumbierres et al. (2021). Similar to before, we also specify a folder to cache the downloaded datasets so that we won’t need to re-downloaded again during subsequent runs.
# specify cache directory
cache_dir <- user_data_dir("aoh")
# specify folder to save Area of Habitat data
## although we use a temporary directory here to avoid polluting your
## with examples files, you would normally specify the folder
## on your computer where you want to save data
output_dir <- tempdir()
# generate Area of Habitat data
## note that this function might take a complete because it will need to
## download the global habitat and elevation data that first time you run it.
spp_aoh_data <- create_spp_aoh_data(
spp_info_data, output_dir = output_dir, cache_dir = cache_dir
)
# preview results
print(spp_aoh_data[, c("id_no", "seasonal", "path")])After generating the Area of Habitat data, we can import them.
# import the Area of Habitat data
spp_aoh_rasters <- lapply(spp_aoh_data$path, rast)
# preview raster data
print(spp_aoh_rasters)## [[1]]
## class : SpatRaster
## dimensions : 2593, 3701, 1 (nrow, ncol, nlyr)
## resolution : 100, 100 (x, y)
## extent : -467931, -97831, 4364377, 4623677 (xmin, xmax, ymin, ymax)
## coord. ref. : World_Behrmann
## source : 979_1.tif
## name : lyr1
## min value : 0
## max value : 1
##
## [[2]]
## class : SpatRaster
## dimensions : 2266, 5670, 1 (nrow, ncol, nlyr)
## resolution : 100, 100 (x, y)
## extent : -248331, 318669, 4838277, 5064877 (xmin, xmax, ymin, ymax)
## coord. ref. : World_Behrmann
## source : 59448_1.tif
## name : lyr1
## min value : 0
## max value : 1
##
## [[3]]
## class : SpatRaster
## dimensions : 5149, 5361, 1 (nrow, ncol, nlyr)
## resolution : 100, 100 (x, y)
## extent : -914731, -378631, 4551877, 5066777 (xmin, xmax, ymin, ymax)
## coord. ref. : World_Behrmann
## source : 4657_1.tif
## name : lyr1
## min value : 0
## max value : 1
##
## [[4]]
## class : SpatRaster
## dimensions : 4978, 7512, 1 (nrow, ncol, nlyr)
## resolution : 100, 100 (x, y)
## extent : -904331, -153131, 4568977, 5066777 (xmin, xmax, ymin, ymax)
## coord. ref. : World_Behrmann
## source : 58622_1.tif
## name : lyr1
## min value : 0
## max value : 1
Finally, let’s create some maps to compare the range data with the Area of habitat data.
# create maps
## N.B. you might need to install the ggmap package
map <-
plot_spp_aoh_data(spp_aoh_data, zoom = 6, maptype = "toner-background") +
scale_fill_viridis_d() +
scale_color_manual(values = c("range" = "red")) +
scale_size_manual(values = c("range" = 0.5)) +
theme(
axis.title = element_blank(),
axis.text = element_text(size = 6),
strip.text = element_text(color = "white"),
strip.background = element_rect(fill = "black", color = "black")
)## |---------|---------|---------|---------|=========================================
# display maps
print(map)
Please cite the aoh R package and the underlying datasets used to produce Area of Habitat data.
Area of Habitat data are created using a range of different datasets.
Depending on which datasets are used, please cite the aoh package and
relevant data using:
Hanson JO (2022) aoh: Create Area of Habitat Data. R package version
0.0.2.2. Available at https://github.com/prioritizr/aoh.
IUCN [insert year] IUCN Red List of Threatened Species. Version
[insert version]. Available at www.iucnredlist.org.
BirdLife International and Handbook of the Birds of the World
([insert year]) Bird species distribution maps of the world. Version
[insert version]. Available at
http://datazone.birdlife.org/species/requestdis.
Jung M, Dahal PR, Butchart SHM, Donald PF, De Lamo X, Lesiv M, Kapos
V, Rondinini C, and Visconti P (2020a) A global map of terrestrial
habitat types. Scientific Data, 7, 1--8.
Jung M, Dahal PR, Butchart SHM, Donald PF, De Lamo X, Lesiv M, Kapos
V, Rondinini C, and Visconti P (2020b) A global map of terrestrial
habitat types (insert version) [Data set]. Zenodo. Available at
https://doi.org/10.5281/zenodo.4058819.
Lumbierres M, Dahal PR, Di Marco M, Butchart SHM, Donald PF, and
Rondinini C (2021) Translating habitat class to land cover to map
area of habitat of terrestrial vertebrates. Conservation Biology, 36,
e13851. Available at <https://doi.org/10.1111/cobi.13851>.
Robinson N, Regetz J, and Guralnick RP (2014) EarthEnv-DEM90: A
nearly-global, void-free, multi-scale smoothed, 90m digital elevation
model from fused ASTER and SRTM data. ISPRS Journal of Photogrammetry
and Remote Sensing, 87, 57--67.
To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.
Please refer to the package website for more information. If you have any questions about using the package or suggestions for improving it, please file an issue at the package’s online code repository.