redlistr Introductory Workshop

This repository contains materials for a basic introductory workshop to the redlistr package version 2.0

A beginner workshop for using the {redlistr} package in R to calculate the key spatial metrics used in the IUCN Red List of Ecosystems and IUCN Red List of Threatened Species workflows — from ecosystem distribution rasters and other spatial data formats.

Workshop dataset:

Mangrove distributions from Western Port Bay (French Island), Victoria, Australia — two time points: 2000 and 2017.

The occurrence records for alpine plant species (primarily Richea continentis) from south-eastern Australia (NSW/VIC ranges). Columns: species, lon, lat, uncertainty (metres), date, year, month. Records span 1910–2006 from herbarium and survey sources.

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Prerequisites

Install the following software before the workshop. All are free.

Software	Purpose	Download
R (≥ 4.2)	The analysis language	https://cran.r-project.org
RStudio	IDE (code editor)	https://posit.co/download/rstudio-desktop
Quarto	Workshop document format (optional)	https://quarto.org/docs/get-started

No prior R experience is assumed. If you are new to R, work through R for Beginners chapters 1–3 before the workshop.

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Installation {#installation}

Step 1 — Install R packages

Run this once on your computer before the workshop. You do not need to repeat this every session.

# Spatial and visualisation packages (from CRAN)
install.packages(c(
  "sf",       # vector spatial data (points, lines, polygons)
  "terra",    # raster spatial data
  "leaflet"   # interactive web maps
))
 
# redlistr — install the development version from GitHub
# (CRAN release v1.0.4 has older function signatures — use GitHub for this workshop)
install.packages("remotes")
remotes::install_github("red-list-ecosystem/redlistr")

If prompted to update packages, enter 1 to update ALL and click Yes in the popup window.

Step 2 — Download the workshop repository

1. Go to the workshop GitHub page
2. Click the green Code button → Download ZIP
3. Unzip and save the folder somewhere convenient (e.g., Desktop/redlistr_workshop/)
4. Open RStudio → File → Open Project → select redlistr_intro_workshop.Rproj Opening the .Rproj file automatically sets your working directory to the workshop folder. All file paths in this workshop assume that.

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Repository Structure {#repository-structure}

redlistr_intro_workshop/
├── Data/
│   ├── WhiteAshForest/         ← shapefile dataset for exercises
│   │   └── (spatial files)
│   └── Plant_species_points.csv  ← species point occurrences
├── Exercises/
│   ├── Exercises.qmd     ← Lessons 1–4 exercises
│   └── Exercises_ANSWERS.qmd
├── redlistr_intro_workshop.Rproj  ← open this in RStudio
└── README.md

Theory

What is Criterion A?
Criterion A assesses decline in ecosystem extent over a 50-year window (past, present, or future). Two decline rate methods are used:

• ARD (Absolute Rate of Decline): constant km²/yr loss
• PRD (Proportional Rate of Decline): constant % per year loss, like compound interest in reverse
• ARC (Annual Rate of Change): log-linear rate (Puyravaud 2003) IUCN thresholds for Criterion A:

Category	Decline over 50 yrs
CR	≥ 80%
EN	≥ 50%
VU	≥ 30%

What is EOO?
EOO is the area of the minimum convex polygon (smallest polygon with no inward angles) drawn around all known occurrences of an ecosystem or species. It is not the same as the actual occupied area — it captures the spatial breadth of the distribution and vulnerability to broad-scale threats (e.g., climate shifts, regional land clearing).

IUCN thresholds for Criterion B1:

Category	EOO
Critically Endangered (CR)	< 2,000 km²
Endangered (EN)	< 20,000 km²
Vulnerable (VU)	< 50,000 km²

What is AOO?
AOO is the number of 2 km × 2 km grid cells occupied by the ecosystem or species. The IUCN mandates the 2 km grid for standardisation. Because AOO depends on grid placement, {redlistr} uses grid-shifting to find the minimum AOO (most conservative estimate).

IUCN thresholds for Criterion B2:

Category	AOO (km²)
CR	< 2 km²
EN	< 20 km²
VU	< 50 km²

Data Formats Supported

{redlistr} accepts three spatial formats. Use whichever matches your data source.

Format	Class	Typical Source	Read with
Raster (GeoTIFF, etc.)	SpatRaster (terra)	Remote sensing, ALA gridded layers	terra::rast()
Polygon (Shapefile, GeoPackage)	sf	TERN, state agency shapefiles	sf::st_read()
Points (CSV with lat/lon)	sf (after conversion)	ALA species records, GBIF	read.csv() + st_as_sf()

Tips

• All formats must be in a projected CRS (metres) before being passed to any {redlistr} function. Rule of thumb: For small study areas (< 500 km across), almost any projected CRS will give consistent results. For national-scale analyses, test whether different CRS choices affect your outputs.

• {redlistr} requires coordinates in metres (projected CRS), not degrees (geographic CRS). The package will throw an error if you pass in longitude/latitude data.

• What is |>? The pipe operator passes the result of one step directly into the next function, without saving intermediate objects. It makes code easier to read top-to-bottom.

• Note on function naming: The dev version uses getEOO() (not makeEOO()). The returned object has slots accessible with @ (e.g., EOO@EOO for the area, EOO@spatial for the polygon geometry). Toggle layers on/off in the map to compare the two time points visually.

• For small study areas (< 500 km across), almost any projected CRS will give consistent results. For national scale analyses, test whether different CRS choices affect your outputs.

Common Errors & Fixes

Error message	Cause	Fix
Input raster has a longitude/latitude CRS	Data is in degrees (EPSG:4326)	project(r, "EPSG:32755") for rasters; st_transform(v, 32755) for vectors
could not find function "makeEOO"	Using old function name	Use getEOO() in the dev version
crs(r1) == crs(r2) returns FALSE	Two rasters in different CRS	Reproject one to match: project(r2, crs(r1))

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References

IUCN (2024). Guidelines for the application of IUCN Red List of Ecosystems Categories and Criteria, Version 2.0. Keith, D.A., Ferrer-Paris, J.R., Ghoraba, S.M.M., Henriksen, S., Monyeki, M., Murray, N.J., Nicholson, E., Rowland, J., Skowno, A., Slingsby, J.A., Storeng, A.B., Valderrábano, M. & Zager, I. (Eds.). IUCN, Gland, Switzerland. https://doi.org/10.2305/CJDF9122

Keith, D.A., Rodríguez, J.P., Rodríguez-Clark, K.M., et al. (2013). Scientific foundations for an IUCN Red List of Ecosystems. PLOS ONE, 8(5), e62111. https://doi.org/10.1371/journal.pone.0062111

Puyravaud, J.-P. (2003). Standardizing the calculation of the annual rate of deforestation. Forest Ecology and Management, 177(1–3), 593–596. https://www.sciencedirect.com/science/article/pii/S0378112702003353