Applied Marine Ecology |
African Wildlife Ecology and Conservation |
Ecosystem Dynamics |
Fire |
Grazing |
Invasive Species (Terrestrial) |
Predators |
Soils |
Climate Change |
Palaeoecology |
Remote Sensing |
Spatial Analyses and GIS |
Vegetation Survey and Mapping |
Adaptive Management |
Cultural Resources |
Habitat Restoration |
Information Management |
Policy |
Reintroductions |
Risk Assessment |
Threatened Species |
The International Union for the Conservation of Nature (IUCN) Red List of Ecosystems and Red List of Threatened Species are global standards for assessing risks of ecosystem collapse and species extinction. However, misconceptions of the Red List assessment process, along with its technically demanding nature, can result in the misapplication of their criteria, leading to inconsistent and potentially unreliable assessments. To address this problem, we developed redlistr, an R package aiding in the production of consistent species and ecosystem Red List assessments. Redlistr's features include methods to calculate (i) area from spatial data, (ii) range size metrics, (iii) rates of change of distributions or populations, and (iv) distribution or population at another time from these rates. A key feature of the package is the systematic approach used to eliminate geometric uncertainty when estimating Area of Occupancy. Here, we develop two case studies to demonstrate the functionalities of redlistr with typical workflows for both species and ecosystems. Redlistr was developed to be accessible to users with a broad range of experience in programming for spatial and temporal data analysis, and sufficiently flexible to allow users to parameterise functions and select equations to fit their purposes. The package specifically aims to assist researchers and conservation practitioners to conduct robust and transparent risk assessments of ecosystems and species under the IUCN Red List criteria but is also useful for other studies requiring analyses of range size, area change and calculations of rates of change.
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