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Description

Tools for the IUCN Red List of Ecosystems and Species.

A toolbox created by members of the International Union for Conservation of Nature (IUCN) Red List of Ecosystems Committee for Scientific Standards. Primarily, it is a set of tools suitable for calculating the metrics required for making assessments of species and ecosystems against the IUCN Red List of Threatened Species and the IUCN Red List of Ecosystems categories and criteria. See the IUCN website for detailed guidelines, the criteria, publications and other information.

redlistr

redlistr is an R package that contains a set of tools suitable for calculating the metrics required for making assessments of species and ecosystems against the IUCN Red List of Threatened Species and the IUCN Red List of Ecosystems categories and criteria.The paper describing redlistr has been published on Ecography and is available here.

important note: rredlist is a different package that works with the IUCN Red List of Threatened Species’ API.

Overview

The redlistr package was developed to assist users conduct assessments for the IUCN Red List of Ecosystems in R. It is also useful for users interested in conducting assessments for the Red List of Threatened Species. 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 these metrics, including two methods of calculating the rate of distirbutional decline: Absolute Rate of Decline (ARD) and Proportional Rate of Decline (PRC). An additional metric: the Annual Rate of Change (ARC), which uses a compound interest law to determine the instantaenous rate of change (Puyravaud 2003) is also included.

Also included are the two standard measures of the size of an ecosystems’ geographic distribution specified in the red list of ecosystems guidelines (Bland et al. 2016). These are the Extent of Occurrence (EOO) and Area of Occupancy (AOO). As many of these measures are also useful for assessing species under the IUCN Red List of Threatened Species criteria, we expect this package will also be useful for assessors conducting species assessments.

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 for both of the red lists, which are the definitive sources of all information required to ensure consistent application of IUCN criteria (Bland et al. 2016). In addition, the papers by Keith et al. (2013) and Rodriguez et al. (2015) are particularly useful for navigating the IUCN Red List of Ecosystems criteria. A range of important resources, including software tools and guiding material is available on the IUCN Red List of Ecosystems website. There is also plenty of handy material for assessing species available on the IUCN Red List of Threatened Species website.

We also assume that you are reasonably familiar with the R programming language, and have some experience in conducting analyses of vector and raster data within the R environment. Of particular use will be familiarity with the raster, sp and rgeos packages. This is certainly not a prerequisite, but this package is built upon many of the functions available in these three packages.

For a more detailed tutorial explaining how to use this package, please refer to the ‘Calculating spatial metrics for IUCN red list assessments’ vignette available with the package.

Lastly, 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

redlistr is now on CRAN! You can also install the development version from GitHub.

# Easiest way to get redlistr:
install.packages("redlistr")

# For the development version from GitHub:
# install.packages("devtools")
devtools::install_github("red-list-ecosystem/redlistr")
Metadata

Version

1.0.4

License

Unknown

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