red-list-ecosystem/rlerestricted

Calculate spatial metrics for the IUCN Red List of Ecosystem, criterion B

RLErestricted

RLErestricted is an R package that contains a set of tools suitable for calculating the metrics required for making assessments of ecosystems against the IUCN Red List of Ecosystems categories and criteria.

Overview

The RLErestricted package was developed to assist users conduct assessments for the IUCN Red List of Ecosystems in R. Assessments of ecosystems under the IUCN Red List of Ecosystems criteria require calculation of standardised metrics that were developed to objectively assess risk to ecosystem (Keith et al. 2013).

This package was designed to assist in the calculation of two standard measures of the size of an ecosystems’ geographic distribution specified in the IUCN Red List of Ecosystems guidelines (Bland et al. 2017). These are the Extent of Occurrence (EOO) and Area of Occupancy (AOO).

In conducting an assessment with this package, we assume that you are familiar with IUCN red listing protocols. In particular, you should consult the IUCN guidelines and follow the recommended steps to ensure consistent application of IUCN criteria (Bland et al. 2017).

We also assume that you are reasonably familiar with the R programming language, and have some experience in conducting analyses of vector data within the R environment using the package sf (simple features).

This is a work in progress and we aim to continually add new functions to newer versions of package. Suggestions are welcomed, as are offers for collaborative development.

Installation

You can install the development version of RLErestricted from GitHub with:

# install.packages("devtools")
devtools::install_github("red-list-ecosystem/RLErestricted")

Example

The goal of RLErestricted is to assist users calculate one spatial metric (area of occupancy or AOO) to a group of polygons describing the distribution of an ecosystem. This information can be used to apply one of the criteria of the IUCN Red List of Ecosystems.

The first step is to create a AOO grid over the extent of the ecosystem:

library(RLErestricted)
library(sf)
#> Linking to GEOS 3.12.0, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUE
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
glaciers_on_volcanos <- tropical_glaciers |>
    dplyr::filter(ecosystem_name %in% "Volcanos de Peru y Chile") |>
    sf::st_transform(crs = 32719)

AOO_grid <- create_AOO_grid(glaciers_on_volcanos)
#> Warning: attribute variables are assumed to be spatially constant throughout
#> all geometries

We can see the results is a grid of cells with information about the area of the ecosystem:

AOO_grid
#> AOO grid for Volcanos de Peru y Chile with a total of 34 cells and total extent of:
#> 75.61276 [km^2]
#> There are 23 cells with small occurrences (<1 % of cell size)
#> There are 15 cells with marginal occurrences (<1 % of total extent)
#> Simple feature collection with 34 features and 5 fields
#> Geometry type: POLYGON
#> Dimension:     XY
#> Bounding box:  xmin: 69423.82 ymin: 7804685 xmax: 539423.8 ymax: 8384685
#> Projected CRS: WGS 84 / UTM zone 19S
#> # A tibble: 34 × 6
#>    layer ecosystem_name       area                     geoms prop_area cumm_area
#>  * <int> <chr>               [m^2]             <POLYGON [m]>       [%]       [%]
#>  1  2917 Volcanos de Peru … 3.73e0 ((69423.82 8284685, 7942…   3.73e-6   4.93e-6
#>  2   275 Volcanos de Peru … 4.51e1 ((529423.8 7804685, 5394…   4.51e-5   6.46e-5
#>  3  2998 Volcanos de Peru … 9.89e3 ((319423.8 8294685, 3294…   9.89e-3   1.31e-2
#>  4  3427 Volcanos de Peru … 1.08e4 ((129423.8 8374685, 1394…   1.08e-2   2.75e-2
#>  5  3053 Volcanos de Peru … 1.12e4 ((309423.8 8304685, 3194…   1.12e-2   4.23e-2
#>  6  2997 Volcanos de Peru … 1.35e4 ((309423.8 8294685, 3194…   1.35e-2   6.02e-2
#>  7  1110 Volcanos de Peru … 1.65e4 ((479423.8 7954685, 4894…   1.65e-2   8.20e-2
#>  8  2875 Volcanos de Peru … 2.52e4 ((209423.8 8274685, 2194…   2.52e-2   1.15e-1
#>  9  2925 Volcanos de Peru … 2.70e4 ((149423.8 8284685, 1594…   2.70e-2   1.51e-1
#> 10  3315 Volcanos de Peru … 2.75e4 ((129423.8 8354685, 1394…   2.75e-2   1.87e-1
#> # ℹ 24 more rows

And we can plot this grid:

plot(AOO_grid['area'])