Measuring Terrestrial Area of Habitat (AOH) and Its Utility for the IUCN Red List

Abstract

The International Union for Conservation of Nature (IUCN) Red List of Threatened Species includes assessment of extinction risk for 98 512 species, plus documentation of their range, habitat, elevation, and other factors. These range, habitat and elevation data can be matched with terrestrial land cover and elevation datasets to map the species' area of habitat (AOH; also known as extent of suitable habitat; ESH). This differs from the two spatial metrics used for assessing extinction risk in the IUCN Red List criteria: extent of occurrence (EOO) and area of occupancy (AOO). AOH can guide conservation, for example, through targeting areas for field surveys, assessing proportions of species' habitat within protected areas, and monitoring habitat loss and fragmentation. We recommend that IUCN Red List assessments document AOH wherever practical. Rigour and Dynamism in the IUCN Red ListThe IUCN Red List of Threatened Species [1] aspires to assess the extinction risk of the world's species, and to serve as a 'barometer of life' of the state of nature [2]. Of the approximately 2 million named species [3], the IUCN Red List has assessed 98 512 species (having increased from fewer than 20 000 in 2002 i ). The process of assessment classifies species in different categories of extinction risk. It does so through an open, rigorously defined process [4] that is objective and transparent. Petitioners can challenge decisions made by Red List Authorities. An important use of the IUCN Red List is the assessment of changes in species extinction risk to monitor changes in the status of individual species, classes and other groups of species, and species-level biodiversity overall [5,6]. These are essential, for example, in reporting on the Aichi Targets [7] and Sustainable Development Goals [8], as well as progress in conserving species [9].

A data-driven geospatial workflow to improve mapping species distributions and assessing extinction risk under the IUCN Red List

“…Using a dataset of 2,273 bird species in the Americas, 55% of all terrestrial birds found in the region, we have shown that our approach produced ranges that have reduced errors of omission, fewer errors of commission, and a higher accuracy when compared to expert-drawn alternatives. Thus, it also provides a more reliable, transparent, and consistent way to derive estimates of Area of Habitat (AOH), the newly recommended measure for the IUCN Red List (Brooks et al 2019).…”

“…Draw the Extent of Occurrence (EOO) around presence points. The EOO is the upper bound of the distribution of a species (Brooks et al 2019) and should be mapped as the Minimum Convex Polygon (MCP) due to its consistency and scale-free properties (Joppa et al 2016; IUCN Red List Technical Working Group 2019; IUCN Standards and Petitions Committee 2019).…”

“…Note that AOO is calculated by tallying the area of 2 × 2 km grid cells with documented presences (IUCN Standards and Petitions Committee 2019). Thus, it is a subset of Area of Habitat (AOH) (Brooks et al 2019). In addition to EOO or AOO thresholds, at least two out of three other conditions must be met if a species is to be assigned to threatened categories (IUCN 2012).…”

“…Recently, Brooks et al (2019) suggested that IUCN Red List assessments should measure Area of Habitat (AOH), also known as Extent of Suitable Habitat. AOH is relevant to guide conservation by quantifying habitat loss and fragmentation within a species range, and is already part of the criteria for identifying Key Biodiversity Areas (KBA) (IUCN 2016a; KBA Standards and Appeals Committee 2019).…”

Opportunities for prioritizing and expanding conservation enterprise in India using carnivores as flagships

“…Given the oft-overlooked caveats with global-scale assessments, such as data inaccuracy or inadequacy and broad generalization of inferences (Ocampo-Peñuela et al 2016; Norman & White 2019), our study demonstrates the utility of carefully designed large-scale investigations integrated with local knowledge and contexts. We present more nuanced distribution maps depicting species occurrences as probabilities within their Area of Habitat (Brooks et al 2019; Fig. S2), rather than Minimum Convex Polygons that over- or under-estimate species ranges (Ocampo-Peñuela et al 2016; Ramesh et al 2017).…”