How to determine extent of occurrence (EOO) and area of occupancy (AOO) for a species

Author details: Dr Jenna Wraith

Editor details: Dr Sebastian Lopez Marcano

Contact details: support@ecocommons.org.au

Copyright statement: This script is the product of the EcoCommons platform. Please refer to the EcoCommons website for more details: https://www.ecocommons.org.au/

Date: March 2025

Script and data info:

This notebook, developed by the EcoCommons team, showcases how to determine the extent of occurrence (EOO) and area of occupancy (AOO) for a species.

Introduction

A short explanation of the notebook

This notebook calculates two important ecological metrics, the Extent of Occurrence (EOO) and the Area of Occupancy (AOO), for the common wombat: Vombatus ursinus. These metrics are commonly used to assess species conservation status by providing information on the geographic range and area occupied by the species.

Extent of occurrence (EOO)

The EOO represents the geographic spread of a species. It’s defined as the area within the smallest convex polygon that encompasses the known occurrences of the species. Ecologically, EOO is a metric for assessing the geographic range of a species and is used in conservation to estimate risk. Species with a small EOO are potentially more likely to be susceptible to extinction because their populations are more likely to be impacted by habitat changes, climate fluctuations, and human activities across their limited range.

Area of Occupancy (AOO)

The AOO is a more fine-scale measurement of where a species is actually found within its range. This metric accounts for the specific locations where the species occupies habitat within the broader EOO and is calculated using a grid-based approach, typically with cells sized to 2x2 km as per IUCN standards. Lower AOO values imply that the species is confined to smaller, often isolated patches, which could increase its vulnerability.

Set-up: R Environment and Packages

For this notebook we are going to use ConR, which includes a EOO.computing and AOO.computing functions to calculate the EOO and AOO for a species. We will also be using the Atlas of Living Australia galah package to access species occurence records.

# Set the workspace to the current working directory
#setwd("") # Uncomment and replace the path below with your own working directory if needed:

# setting up a workspace
workspace <- getwd()  # Get the current working directory and store it in 'workspace'

# Increase the plot size by adjusting the options for plot dimensions in the notebook output
options(repr.plot.width = 16, repr.plot.height = 8)  # Sets width to 16 and height to 8 for larger plots

# Set CRAN mirror
options(repos = c(CRAN = "https://cran.rstudio.com/"))

# List of packages to check, install if needed, and load
packages <- c("galah", "lwgeom", "ggplot2" , "ggspatial", "sf", "devtools","dplyr")

# Function to display a cat message
cat_message <- function(pkg, message_type) {
  if (message_type == "installed") {
    cat(paste0(pkg, " has been installed successfully!\n"))
  } else if (message_type == "loading") {
    cat(paste0(pkg, " is already installed and has been loaded!\n"))
  }
}

# Install missing packages and load them
for (pkg in packages) {
  if (!requireNamespace(pkg, quietly = TRUE)) {
    install.packages(pkg)
    cat_message(pkg, "installed")
  } else {
    cat_message(pkg, "loading")
  }
  library(pkg, character.only = TRUE)
}

galah: version 2.1.1
ℹ Default node set to ALA (ala.org.au).
ℹ See all supported GBIF nodes with `show_all(atlases)`.
ℹ To change nodes, use e.g. `galah_config(atlas = "GBIF")`.

galah is already installed and has been loaded!

Attaching package: 'galah'

The following object is masked from 'package:stats':

    filter

lwgeom is already installed and has been loaded!

Linking to liblwgeom 3.0.0beta1 r16016, GEOS 3.13.0, PROJ 9.5.1

ggplot2 is already installed and has been loaded!
ggspatial is already installed and has been loaded!
sf is already installed and has been loaded!

Linking to GEOS 3.13.0, GDAL 3.8.5, PROJ 9.5.1; sf_use_s2() is TRUE

Attaching package: 'sf'

The following objects are masked from 'package:lwgeom':

    st_minimum_bounding_circle, st_perimeter

devtools is already installed and has been loaded!

Loading required package: usethis

dplyr is already installed and has been loaded!

Attaching package: 'dplyr'

The following object is masked from 'package:galah':

    desc

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

# Install ConR from GitHub if not already installed
if (!requireNamespace("ConR", quietly = TRUE)) {
  devtools::install_github("gdauby/ConR")
  cat_message("ConR", "installed")
} else {
  cat_message("ConR", "loading")
}

ConR is already installed and has been loaded!

# Load the ConR package
library(ConR)

Access the data

Species of interest: Vombatus ursinus

The common wombat (Vombatus ursinus) is native to southeastern Australia and Tasmania. It is currently listed as ‘Least Concern’ on the IUCN Red List. The occurrence data for Vombatus ursinus is sourced from the Atlas of Living Australia (ALA) via the Galah Package. This dataset provides the coordinates for the species, which are used to calculate the Extent of Occurrence (EOO) and Area of Occupancy (AOO) metrics. For this example we are filtering records to include those recorded since 2015 and are located in Victoria.

# Configure the Galah settings
galah_config(atlas = "ALA",
             username = "ecocommons",
             email = "ecocommonsaustralia@gmail.com",
             password = Sys.getenv("GALAH_PASSWORD"))

# Choose the occurence records and apply any filters. 

Occurrences<- galah_call() |>
  identify("Vombatus ursinus") |> 
  filter(year >= 2015, cl22 == "Victoria") |> 
  select(basisOfRecord, group = "basic") |>
  collect()

Request for 10315 occurrences placed in queue
Current queue length: 1

--

Downloading

 Occurrences |> head() # View the top rows of data

# A tibble: 6 × 9
  recordID        scientificName taxonConceptID decimalLatitude decimalLongitude
  <chr>           <chr>          <chr>                    <dbl>            <dbl>
1 00094a3b-9a6c-… Vombatus ursi… https://biodi…           -37.7             145.
2 001466ef-8f9a-… Vombatus ursi… https://biodi…           -37.5             144.
3 0016ead2-dabb-… Vombatus ursi… https://biodi…           -37.4             144.
4 00184c9a-6769-… Vombatus ursi… https://biodi…           -38.1             145.
5 001ce4dc-9244-… Vombatus ursi… https://biodi…           -38.6             146.
6 001f41e1-8448-… Vombatus ursi… https://biodi…           -37.3             148.
# ℹ 4 more variables: eventDate <dttm>, occurrenceStatus <chr>,
#   dataResourceName <chr>, basisOfRecord <chr>

# To cite the galah package use:
citation(package = "galah")

To cite galah in publications use:

  Westgate M, Kellie D, Stevenson M, Newman P (2025). _galah:
  Biodiversity Data from the GBIF Node Network_. R package version
  2.1.1, <https://CRAN.R-project.org/package=galah>.

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {galah: Biodiversity Data from the GBIF Node Network},
    author = {Martin Westgate and Dax Kellie and Matilda Stevenson and Peggy Newman},
    year = {2025},
    note = {R package version 2.1.1},
    url = {https://CRAN.R-project.org/package=galah},
  }

Data Preparation

Load the data and convert it to an sf object

The occurrence data for Vombatus ursinus is loaded from Galah package and converted to an sf object using the st_as_sf function from the sf package. The latitude and longitude columns are specified as the coordinates for the sf object. The coordinate reference system (CRS) is set to EPSG:4326 (WGS 84) to ensure the correct spatial representation of the data.

Here we also process the data by removing records with missing coordinates, modify the data to keep only the necessary columns (latitude, longitude and species) and ensure all the species names match.

# Process the data to keep only necessary columns

MyData <- Occurrences %>%
  filter(!is.na(decimalLongitude) & !is.na(decimalLatitude)) %>%  # Remove missing coordinates
  transmute(  
    Latitude = decimalLatitude,  
    Longitude = decimalLongitude,  
    Species = first(scientificName)  # Unify species name
  )

# Convert to sf object
species_points <- st_as_sf(MyData, coords = c("Longitude", "Latitude"), crs = 4326)

# Check the result
print(species_points)

Simple feature collection with 10315 features and 1 field
Geometry type: POINT
Dimension:     XY
Bounding box:  xmin: 140.9672 ymin: -39.12958 xmax: 149.9317 ymax: -35.96943
Geodetic CRS:  WGS 84
# A tibble: 10,315 × 2
   Species                      geometry
 * <chr>                     <POINT [°]>
 1 Vombatus ursinus (145.4908 -37.67752)
 2 Vombatus ursinus  (144.2599 -37.5146)
 3 Vombatus ursinus  (144.2405 -37.3892)
 4 Vombatus ursinus  (145.2701 -38.1358)
 5 Vombatus ursinus   (145.57 -38.62757)
 6 Vombatus ursinus (148.3121 -37.33037)
 7 Vombatus ursinus (148.6383 -36.99493)
 8 Vombatus ursinus (147.0542 -37.46073)
 9 Vombatus ursinus   (146.165 -36.8437)
10 Vombatus ursinus (147.7134 -37.51464)
# ℹ 10,305 more rows

Calculate Extent of Occurrence (EOO)

Calculate the EOO using the EOO.computing function

The EOO for Vombatus ursinus is calculated using the EOO.computing function from the ConR package. The function takes the species occurrence points as input and returns the EOO polygon and EOO value. If the EOO polygon is NULL, a convex hull is generated from the species points as an alternative representation of the EOO.

# Calculate EOO (Extent of Occurrence)
EOO_polygon <- EOO.computing(MyData, export_shp = FALSE)

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# Check if the spatial polygon is NULL
if (is.null(EOO_polygon$spatial.polygon_1)) {
  # Create the convex hull from the species points
  eoo_hull <- st_convex_hull(st_union(st_geometry(species_points)))
  # Save the convex hull as an sf object
  EOO_sf <- st_sf(geometry = eoo_hull)
} else {
  # Convert the existing polygon to an sf object if available
  EOO_sf <- st_as_sf(EOO_polygon$spatial.polygon_1)
}

Calculate Area of Occupancy (AOO)

Calculate the AOO using the AOO.computing function

The AOO for Vombatus ursinus is calculated using the AOO.computing function from the ConR package. The function takes the species occurrence points as input and returns the AOO value based on a 2x2 km grid. The AOO value represents the area actually occupied by the species within the grid cells.

# Calculate AOO (Area of Occupancy)
AOO_result <- AOO.computing(
  MyData,
  cell_size_AOO = 2,
  nbe.rep.rast.AOO = 0,
  parallel = FALSE,
  NbeCores = 2,
  show_progress = TRUE,
  export_shp = FALSE,
  proj_type = "cea"
)

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  |======================================================================| 100%

Visualise and analyse the results

Visualise the EOO, AOO, and species occurrence points

Now, to conclude this short notebook, we will visualise the calculated EOO, AOO, and the species occurrence points on a map using ggplot2. The map will display the land area, species points, EOO polygon (or convex hull), and include annotations for the EOO and AOO values. The map will provide a visual representation of the species distribution and the extent of its occurrence.

# Extract the EOO and AOO values
print(paste("The EOO value is:", EOO_polygon$eoo, "km²"))

[1] "The EOO value is: 179846 km²"

print(paste("The AOO value is:", AOO_result$aoo, "km²"))

[1] "The AOO value is: 11144 km²"

# Get bounding box for the species points
bbox <- st_bbox(species_points)

# Create the ggplot with the template colours
ggplot() +
  # Plot the land layer
  geom_sf(data = land, fill = "#D9E3F0", color = "#4A90E2") +
  
  # Plot the EOO convex hull
  geom_sf(data = EOO_sf, fill = NA, color = "#E74C3C", linetype = "dashed", size = 3) +
  
  # Plot the species points
  geom_sf(data = species_points, color = "#11AA96", size = 2, shape = 16) +
  
  # Set coordinate limits based on the bounding box
  coord_sf(xlim = c(bbox["xmin"] - 0.5, bbox["xmax"] + 0.5), 
           ylim = c(bbox["ymin"] - 0.5, bbox["ymax"] + 0.5), expand = FALSE) +
  
  # Add EOO and AOO values as an annotation box
  annotate("label", 
           x = bbox["xmin"] + 0.4, 
           y = bbox["ymax"] - 0.1, 
           label = paste("EOO:", round(EOO_polygon$eoo, 2), "km²\nAOO:", round(AOO_result$aoo, 2), "km²"), 
           size = 3, 
           fill = "white", 
           color = "black", 
           fontface = "bold", 
           label.size = 0) +  # Removes the border around the label
  
  # Set gridlines at 2 km intervals
  scale_x_continuous(breaks = seq(floor(bbox["xmin"]), ceiling(bbox["xmax"]), by = 1)) +
  scale_y_continuous(breaks = seq(floor(bbox["ymin"]), ceiling(bbox["ymax"]), by = 1)) +
  
  # Customize the theme
  theme_minimal() +
  labs(title = "EOO and AOO for Vombatus ursinus") +
  theme(legend.position = "none")

Conclusion

In this notebook, we have demonstrated how to calculate the Extent of Occurrence (EOO) and Area of Occupancy (AOO) for the common wombat (Vombatus ursinus). This serves as a starting point for understanding a species’ distribution and provides a first glance at its potential extinction risk. While EOO and AOO are valuable, they are just the beginning of a more comprehensive assessment of a species’ conservation status. These metrics can guide further research and inform more detailed analyses, ultimately contributing to better-informed conservation strategies.

How to Cite EcoCommons

If you use EcoCommons in your research, please cite the platform as follows:

EcoCommons Australia 2024. EcoCommons Australia – a collaborative commons for ecological and environmental modelling, Queensland Cyber Infrastructure Foundation, Brisbane, Queensland. Available at: https://data–explorer.app.ecocommons.org.au/ (Accessed: MM DD, YYYY). https://doi.org/10.3565/chbq-mr75

You can download the citation file for EcoCommons Australia here: Download the BibTeX file

References

Dauby, G., Stévart, T., Droissart, V., Cosiaux, A., Deblauwe, V., Simo‐Droissart, M., Sosef, M.S., Lowry, P.P., Schatz, G.E., Gereau, R.E. and Couvreur, T.L., 2017. ConR: An R package to assist large‐scale multispecies preliminary conservation assessments using distribution data. Ecology and evolution, 7(24), pp.11292-11303.

Gaston, K.J. and Fuller, R.A., 2009. The sizes of species’ geographic ranges. Journal of applied ecology, 46(1), pp.1-9.

Marsh, C.J., Syfert, M.M., Aletrari, E., Gavish, Y., Kunin, W.E. and Brummitt, N., 2023. The effect of sampling effort and methodology on range size estimates of poorly-recorded species for IUCN Red List assessments. Biodiversity and Conservation, 32(3), pp.1105-1123.

Rivers, M.C., Taylor, L., Brummitt, N.A., Meagher, T.R., Roberts, D.L. and Lughadha, E.N., 2011. How many herbarium specimens are needed to detect threatened species?. Biological conservation, 144(10), pp.2541-2547.

Westgate, M., Kellie, D., Stevenson, M., Newman, P., 2025. galah: Biodiversity Data from the GBIF Node Network. R package version 2.1.1, https://CRAN.R-project.org/package=galah.